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Table of Contents

• Where AI Fits

• The Data Problem Underneath

• Key Terms

• Sources

Most of the cost in post-trade operations is not in straight-through processing. It is in the exceptions. Up to 10% of operating costs in asset servicing come from errors and breaks (ISSA/ValueExchange, 2024). STP rates for mandatory and income events sit at about 71% (ISSA, 2024). Two thirds of the errors trace to bad data, not bad process (ValueExchange/ISSA, 2024).

Those inefficiencies carry a heavier penalty today. Volumes rose 25% in 2025, automation levels fell at more than 60% of brokers over the same period (Broadridge, 2025), and T+1 compressed the US exception window from overnight to two to five hours (Tokenovate, 2024). The workload got bigger and the time got shorter.

Where AI Fits

Exception management follows patterns. The details vary but the shape of the work repeats: something is wrong in the data, someone works out what, someone fixes it.

AI can speed up the early parts of that. Catching data gaps before end-of-day reconciliation rather than after. Sorting breaks by type and severity in seconds rather than minutes. Some systems now propose fixes based on past resolutions. Early data points to 45% lower handling time and 35% lower costs where AI is applied (Tokenovate, 2024), though those numbers come from limited samples.

Where it gets stuck is the last step. Agentic AI, systems that can reason through a problem and act on it, would make the biggest difference here. But automated resolution requires firms to let the model act, and most will not, because the governance is not there. Who owns the model? Who is accountable for a wrong resolution? What gets escalated and what does not? Without answers to those questions, the model triages but a person still fixes.

The Data Problem Underneath

Vendors rarely talk about the next part. Two thirds of exceptions come from bad data. The AI is trained on that same data. If the training data is full of the errors the model is supposed to catch, the model learns the errors as normal. It does not flag them. It treats them as baseline.

That makes the data quality problem the actual AI problem. Firms that want AI to work in exception management need to fix the data first, or at least fix it at the same time. That means reference data, static data, market data feeds, and the way corporate actions are coded across systems. It is slow work and it does not make for good conference slides, but without it the model has nothing clean to learn from.

The governance problem sits on top of the data problem. Even if the model is accurate, nobody will let it act without clear accountability. And even if the accountability is clear, nobody will trust the outputs if the data underneath is unreliable. One does not get solved without the other.

That connection between data quality and governance is what determines whether AI in exception management actually ships or stays in pilot. The technology itself is the least interesting part.

I write about AI adoption for operations leaders at Getting AI To Work.

Key Terms

Exception management: Identifying, investigating, and resolving breaks in post-trade processing.

STP (straight-through processing): When a trade or event processes without anyone needing to touch it.

T+1 settlement: Trades settle one business day after execution. Adopted in the US and Canada in May 2024.

Reconciliation break: A mismatch when comparing records between two parties or systems.

Agentic AI: AI that can reason through a problem, use tools, and act with limited human oversight.

Sources

• ISSA/ValueExchange, Asset Servicing Study, 2024

• Broadridge, Operations Survey, 2025

• Tokenovate, Post-Trade AI Analysis, 2024

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Table of Contents

• The Challenge

• How AI Addresses This

  • The Technical Approach

  • What is Retrieval-Augmented Generation?

• Real-World Applications

• The Agentic Evolution

• Implementation Considerations

• Measuring Success

• Key Terms

• Sources

Based on available disclosures, every major US custodian has deployed AI internally to help staff handle client queries faster or do other simple tasks under strict governance and controls. Almost none have let the AI respond to clients directly. The adoption barrier is not the technology. It is whether your firm trusts AI to communicate on its behalf. For broader context on AI across custody functions, see The Complete Guide to AI in Global Custody.

The Challenge

The majority of inbound client queries are factual lookups: position balances, settlement status, fee calculations, corporate action deadlines. Each requires an analyst to search across custody, accounting, corporate actions, and settlement systems, compile the answer, and respond. The work is high-volume, manual, and largely identical from one query to the next.

The problem is getting worse. Assets under custody are growing faster than firms can add headcount. BNY manages $59.3 trillion in assets under custody and administration, up 14% year-on-year (Q4 2025 earnings). Citi's securities services assets grew 24% to $31 trillion (Q4 2025 earnings). Fee compression limits the business case for proportional staffing increases. Post-T+1, settlement-related queries are now time-critical in ways they were not under T+2.

The client-side conversation has barely started. A Coalition Greenwich survey of 121 institutional investors found that two-thirds of UK institutions have not discussed AI with their asset managers or service providers. The gap between what is technically possible and what clients are prepared to accept remains wide. Regulators are now requiring the very transparency that research shows erodes client trust, a bind explored further below.

How AI Addresses This

The Technical Approach

AI client service combines several capabilities. Natural language processing classifies incoming queries by intent and extracts key entities (account numbers, security identifiers, date ranges). According to John Snow Labs benchmarks, NLP achieves 92% accuracy in extracting key information from financial documents, compared with 74% for conventional rule-based methods. Retrieval systems then pull the relevant data from structured sources: positions, transactions, corporate actions, settlement instructions. A generation layer drafts the response.

Citi's approach illustrates the pattern. Its NLP models are trained on SWIFT MT599 free-format messages to classify client inquiries and generate draft responses for agent review (Citi press release, 2025). The sequence is: classify the query, retrieve the data, draft the response.

What is Retrieval-Augmented Generation?

Retrieval-augmented generation (RAG) is an architecture that makes this feasible for client communications. Rather than generating answers from training data (which risks fabrication), RAG retrieves factual data from authoritative systems before generating a response. Every answer is grounded in the system of record.

This matters because unconstrained large language models are not accurate enough for consequential monetary-impact type client communications without extensive testing and guardrails. Academic benchmarks show LLMs struggle beyond 50% accuracy on complex real-world financial tasks (Finance Agent Benchmark, arXiv). RAG does not eliminate errors, but it constrains the AI to verified data rather than statistical prediction. Broadridge's OpsGPT uses RAG for settlement fails research, trained on data from systems handling $10 trillion in daily trades (Broadridge press release, 2025). The same architectural pattern applies to client service.

Real-World Applications

Custodian disclosures reveal a consistent pattern: AI is deployed to help staff, not to replace them in client interactions. The data retrieval patterns mirror those used in AI Reconciliation in Custody Operations, but applied to client queries rather than break resolution.

Citi operates the most visible example. CitiService Agent Assist provides real-time guidance to customer service agents during client interactions across 47 countries. It surfaces procedural information, generates call transcripts, and creates after-call summaries, reducing average handle time and improving first-contact resolution (Citi press release, 2025). The client speaks to a human. The AI supports the human behind the scenes. This is Level 2 on the autonomy spectrum.

BNY Mellon has deployed 125+ AI use cases and over 130 "Digital Employees" through its Eliza 2.0 platform (Q4 2025 earnings). None are client-facing. The firm has explored "Client Co-pilots" but has not deployed them, with industry commentary citing data privacy and multi-tenant security concerns as blockers. 125 use cases, and none face the client directly.

JPMorgan is the closest to a client-facing deployment. Its Payments Virtual Assistant, launched in November 2025, supports natural language queries for Commerce Center reporting (J.P. Morgan Developer Portal). It is the only confirmed live client-facing AI tool among the major custody banks, and it is in payments reporting, not securities services.

Based on available disclosures, no major custodian has deployed AI that communicates directly with securities services clients at scale.

The Agentic Evolution

The autonomy spectrum helps explain where client service AI stands and where the resistance lies. For the broader shift from automation to autonomy across post-trade, see Agentic AI in Post-Trade: From Automation to Autonomy.

Based on available disclosures, most custodians operate at Level 1-2 for client service. Citi's Agent Assist is Level 2: the AI drafts, the human reviews and responds. The critical shift to Level 3 is where AI handles routine queries directly, with humans monitoring outcomes and managing escalations. An agent at this level might autonomously confirm a settled cash balance but instantly route any query containing "dispute" or "missing" to a human supervisor.

The jump from Level 2 to Level 3 is where the trust barrier bites hardest. Internal AI tools carry low reputational risk. An analyst who receives a bad AI suggestion simply ignores it. Client-facing AI carries the risk of embarrassing the firm in front of its most important stakeholders. The technology for AI client service exists today. Whether your firm will trust it to speak to clients on its behalf is a different problem entirely.

Implementation Considerations

The technology described above exists. So why is most client service still handled manually?

The 5C Adoption Friction Model identifies where resistance emerges. For client service, the dominant friction point is Credibility, not Capability.

Credibility (trust in AI outputs) is the primary barrier. Client service is the firm's public face. A wrong answer to a client is not just an operational error; it is a relationship risk. Experimental research demonstrates the problem: in an experimental setting with general participants, when subjects learned that AI was involved in generating financial content, trust scores dropped from 5.08 to 4.18 on a seven-point scale (Schilke and Reimann, Organizational Behavior and Human Decision Processes, 2025). A separate Brunswick Group survey of 100 institutional US equity investors found that only four in ten trust AI summaries as much as analyst-written content (Brunswick Group 2026 Investor Survey). Teams will manually revalidate every AI-drafted response until they trust it, which defeats the efficiency gain.

Clarity (understanding what the AI actually does): "The AI looked it up" is not sufficient for institutional clients managing billions in assets. Control (who is accountable when it goes wrong): if the AI gives a client incorrect position data, accountability frameworks are largely undefined. Capability (skills to work alongside AI): teams need to interpret confidence scores and manage hybrid workflows of AI-handled routine queries alongside human-handled complex ones.

Consequences (acceptable risk exposure): ESMA guidance (May 2024) expects firms to disclose AI use in client interactions. FINRA Rule 2210 applies the same communications standards to AI-generated content as human-generated. Disclosure itself reduces trust, creating a compliance-credibility bind.

Measuring Success

Client service AI should be measured on outcomes, not deployment.

Query resolution time is the headline metric: reducing routine queries from hours to minutes. First-contact resolution rate measures whether queries are resolved without escalation. Client satisfaction scores (NPS or equivalent) capture whether faster responses actually improve the relationship. Cost per query should track headcount reallocation, not headcount reduction; the goal is freeing experienced staff for complex work and time-critical T+1 exceptions. Error rate comparison between AI-assisted and manual processes is the credibility metric: until AI-assisted error rates are demonstrably lower, teams will not trust the system.

The firms that solve the trust problem first will have a structural advantage in client retention after assets under custody at the two largest firms grew 14-24% year-on-year in 2025. Client service headcount cannot keep pace.

The disclosure paradox makes this harder than it sounds. Regulators are increasingly expecting firms to disclose when clients interact with AI. But research shows that disclosure itself reduces trust. Firms must build AI that is trustworthy enough to survive the transparency it is legally required to provide.

Is your firm at Level 1, where AI helps analysts research answers? Or is it ready for Level 3, where agents handle routine queries directly and humans focus on the work that actually requires judgment?

I write about AI adoption for operations leaders at Get AI To Work. If you have read this far, the technology is not your problem. Subscribe for the part that is.

Key Terms

Agentic AI: AI systems that can reason, plan, use tools, and take actions with minimal human oversight. Unlike traditional AI that makes predictions or generative AI that creates content, agentic AI can complete multi-step tasks autonomously within defined guardrails.

AI Agent: A software system that uses AI to perceive its environment, make decisions, and take actions to achieve goals. In client service, an agent might receive a query, investigate across multiple systems, draft a response, and escalate only genuinely complex issues without human intervention.

Human-in-the-Loop: An AI design pattern where humans review and approve AI decisions before execution. Common in current client service AI, where AI drafts responses but humans review and send them. The dominant pattern at Level 1-2.

Guardrails: Constraints and boundaries that limit what an AI agent can do. In client service, guardrails might include approved response templates, mandatory escalation triggers for certain query types, or value thresholds requiring human review.

Tool Use: The ability of an AI agent to interact with external systems, APIs, and data sources. Enables client service agents to query custody platforms, access position data, retrieve corporate actions information, or check settlement status as part of answering client queries.

Autonomy Level: The degree of independence an AI system has to make decisions and take actions. Ranges from fully human-controlled (Level 0) to fully autonomous (Level 4). Most custody client service AI currently operates at Level 1-2.

Natural Language Processing (NLP): AI techniques that enable computers to interpret and extract meaning from human language. In client service, NLP classifies query intent, extracts key entities (account numbers, securities, dates), and enables systems to understand unstructured client requests.

Retrieval-Augmented Generation (RAG): An AI architecture where the system retrieves factual data from authoritative sources before generating a response. Reduces hallucination risk by grounding answers in verified data. Critical for client communications where every answer must be factually correct.

Straight-Through Processing (STP): Automated processing of transactions or queries from start to finish without manual intervention. In client service, STP would mean a query received, processed, and answered without human involvement.

Assets Under Custody/Administration (AuC/A): The total market value of financial assets held or administered by a custodian on behalf of clients. A key scale metric; as AuC/A grows, client service query volumes grow proportionally.

Sources

Custodian Disclosures

1. BNY Mellon Q4 2025 Earnings (January 2026): Eliza 2.0 platform, 125+ AI use cases, 130+ digital employees, $59.3T AuC/A.

2. Citi Q4 2025 Earnings (January 2026): Securities services record revenue, 70%+ AI adoption, $31T AuC/AUA.

3. Citi press release (2025): CitiService Agent Assist deployment in 47 countries, SWIFT MT599 NLP, Pega Innovation Award.

4. JPMorgan Q4 2025 Earnings (January 2026): 1,000+ AI use cases targeted, LLM Suite for 50,000 employees.

5. J.P. Morgan Developer Portal (November 2025): Payments Virtual Assistant launch.

Vendor Disclosures 6. Broadridge press releases (January 2024, May 2025): OpsGPT capabilities, RAG architecture, $10T daily trade data.

Industry and Academic Research 7. Coalition Greenwich: Institutional Investing in the AI Era (121 institutional investors surveyed across Asia/Europe). 8. Brunswick Group: 2026 Investor Survey (100 institutional US active equity investors, November-December 2025). 9. Schilke, O. and Reimann, M.: "The Transparency Dilemma: How AI Disclosure Erodes Trust", Organizational Behavior and Human Decision Processes, Vol. 188, May 2025. 10. John Snow Labs benchmarks: NLP accuracy for financial document extraction (vendor research). 11. Finance Agent Benchmark (arXiv): LLM accuracy on real-world financial tasks.

Regulatory 12. ESMA: Public Statement on AI and Investment Services (May 2024). 13. FINRA: Regulatory Notice 24-09 (July 2024).

Article prepared February 2026. Company-reported figures have not been independently verified.

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Table of Contents

• AI in Corporate Actions Processing

• The Challenge

• How AI Addresses This

• What is Natural Language Processing?

• The Implementation Gap

• The Agentic Evolution

• Why Adoption Stalls

• The 5C Adoption Friction Framework

• Measuring Success

• Key Terms

• Sources

AI in Corporate Actions Processing

Corporate actions cost the industry $58 billion annually. That figure, from industry analysis presented at SIBOS 2025, increases by roughly 10% each year. Yet automation rates remain below 40%, and industry surveys suggest 75% of market participants still manually revalidate data before processing. The technology to extract and classify announcements exists. It has existed for years. The revalidation persists anyway.

This article looks at why corporate actions remains one of the most resistant areas to AI adoption in custody operations, and what it would take to change that. For broader context on AI across custody functions, see The Complete Guide to AI in Global Custody.

The Challenge

Corporate actions processing involves receiving announcements from issuers, interpreting their terms, calculating entitlements, collecting client elections for voluntary events, and ensuring securities and cash move correctly. The complexity begins at the source.

According to SIFMA, there is no US regulatory mandate for standardised corporate action announcements. Issuers, exchanges, and regulators each use different formats. Most announcements arrive as unstructured PDFs or HTML pages. A 2021 SIX survey found that 46% of global corporate actions data is still published and received manually.

The problem compounds through the intermediary chain. Each custodian and sub-custodian sets earlier internal deadlines to allow processing time. Cross-border holdings multiply the layers. By the time an announcement reaches the end investor, the original deadline may have compressed from weeks to days.

Voluntary corporate actions create additional friction. Elections require client instruction, often across multiple accounts and jurisdictions. Tight deadlines leave little margin for error. A 2015 SWIFT survey found that handling voluntary corporate actions received automation scores of only 1.83 out of 4, the lowest of any custody function measured. More recent industry data on this specific metric is sparse.

The consequences of failure are severe. Oxera research documented that processing failures during the France Telecom rights issue in 2003 could have resulted in losses in the tens of millions of euros for a single custodian. These are not hypothetical risks. They are historical near-misses that shaped the defensive processing culture that persists today.

How AI Addresses This

AI applications in corporate actions focus on three areas: announcement extraction, classification, and exception prediction.

Announcement extraction uses natural language processing (NLP) and optical character recognition (OCR) to turn unstructured PDF and HTML announcements into structured data. Machine learning models identify entity names, event types, key dates, and terms. According to company disclosures, BNP Paribas Securities Services processes approximately 30,000 messages monthly through its ML translation tool, handling announcements in dozens of languages.

Classification uses supervised learning to sort events by type (dividend, merger, rights issue, stock split) and flag anomalies. These models train on historical data to spot patterns and identify announcements that differ from expected structures.

Exception prediction uses pattern recognition to identify announcements likely to cause processing issues, letting teams focus review effort on high-risk events rather than processing everything in order.

What is Natural Language Processing?

NLP refers to AI techniques that let computers interpret and extract meaning from human language. In corporate actions, NLP parses announcement text to identify relevant terms (record date, election deadline, exchange ratio) even when formatting varies across issuers and jurisdictions.

The Implementation Gap

The technology described above exists. So why is it not working at scale?

The gap between available technology and operational deployment is wider in corporate actions than in other custody functions. What follows is not a list of vendor failures, but a symptom of deeper structural problems.

Vendor solutions exist but outcomes are unverified. SmartStream offers AI-powered lifecycle automation. IHS Markit provides an AI chatbot for data queries. SIX operates a bot for corporate actions data access. None of these vendors have published independently verified metrics on accuracy, STP rates, or cost reduction in production settings.

Custodian disclosures are thin. No major custodian has publicly disclosed specific AI outcomes for corporate actions processing. BNY's Eliza platform covers corporate actions in general terms but without operational metrics. State Street's Alpha platform references corporate actions in training data descriptions but has not disclosed deployment outcomes. Custodians have been more forthcoming about AI in reconciliation and client service; few have matched that transparency in corporate actions.

The Chainlink initiative has potential but is not yet in production. In September 2025, Chainlink announced a consortium with Swift, Euroclear, and 24 financial institutions to address corporate actions data problems using blockchain-based attestation. Phase 2 pilot results claimed high accuracy for attestor-validated records. As of February 2026, no verified production deployment has been announced. The initiative addresses data integrity after capture, creating what some call a "golden record" of validated corporate actions data. But even a golden record requires internal systems ready to consume it without manual intervention. The bottleneck shifts from data quality to workflow readiness.

ISO 20022 will not solve this alone. Adoption of ISO 20022 for corporate actions messaging has been slow, with DTCC indicating it will not mandate format changes. Even with full adoption, ISO 20022 standardises message format, not data quality at source. Announcements will still arrive as unstructured PDFs requiring AI interpretation.

The Agentic Evolution

The autonomy spectrum helps explain where corporate actions AI stands and where it might go.

Based on available disclosures, most corporate actions deployments appear to operate at Level 1. Industry commentary suggests some firms are piloting Level 2 for dividend processing. No major custodian has publicly disclosed a Level 3 deployment for corporate actions. Level 4 is not a realistic near-term target given the liability implications of missed elections and the complexity of multi-jurisdictional tax treatment.

The challenge is that Level 3 requires more than better technology. It requires solving the trust deficit. When the majority of firms manually revalidate data they receive, the problem is not extraction accuracy. The problem is that nobody trusts the data enough to act on it automatically.

Why Adoption Stalls

The barriers to AI adoption in corporate actions are primarily organisational, not technical.

Data quality at source remains broken. AI can extract data from PDFs, but it cannot fix errors in the original announcement. Until issuers adopt standardised formats (which no regulator mandates), extraction will always involve some guesswork.

Integration complexity is high. Corporate actions processing touches position systems, client instruction platforms, tax engines, and settlement infrastructure. AI that extracts data but cannot connect to downstream systems creates a new manual step rather than removing one.

Defensive processing culture is rational. Teams that have experienced or heard about multi-million dollar losses from missed elections are not being irrational when they insist on manual validation. The consequence asymmetry (small time savings vs catastrophic loss) makes caution the individually rational choice even when it is collectively inefficient.

Expertise hoarding is real, and it is often a form of misguided protection. Research indicates that approximately one-third of employees stockpile expertise when they perceive AI as a threat to their role. But in corporate actions, this behaviour often stems from the same instinct that drives defensive processing: specialists believe their vigilance protects the firm from catastrophic loss.

They are not being selfish. They are acting as the firm's informal risk-management layer because they believe the formal systems are insufficient. They are holding onto the controls because they do not trust anyone else to catch what they would catch.

Knowledge management research suggests 90% of organisational knowledge is unwritten and hard to capture in systems. Specialists who have spent decades learning market-specific rules and edge cases have reason to resist transferring that knowledge to systems that might replace them.

The 5C Adoption Friction Framework

Adoption friction typically falls into five categories. Applying them to corporate actions:

• Clarity (do teams understand what AI will do?): Teams may expect AI to handle everything, then lose trust when it escalates complex events. Defining what "automation" means for voluntary actions requiring client instruction matters.

• Control (do they feel agency over the change?): Specialists who built expertise over decades resist handing decision-making to systems they did not build and do not fully understand.

• Capability (do they have the skills to use it?): Cross-training is difficult when knowledge is unwritten and market-specific. New staff cannot easily validate AI outputs.

• Credibility (do they trust it?): One missed election destroys trust for years. AI systems have no margin for error in building credibility.

• Consequences (do they see personal benefit?): If AI handles routine announcements, what remains for the specialists? The answer ("exception handling and client advisory") may not feel like enough.

For a deeper treatment of this framework, see Agentic AI in Post-Trade.

Measuring Success

Metrics should reflect both efficiency and risk:

• Extraction accuracy: Percentage of fields correctly captured without manual correction

• STP rate by event type: Distinguish mandatory from voluntary; dividends from mergers

• Time to client notification: For voluntary events, elapsed time from announcement receipt to client communication

• Election capture rate: Percentage of elections received before deadline, by channel

• Exception rate: Percentage of events requiring manual intervention, with root cause analysis

• Error rate and severity: Incidents per 1,000 events, weighted by financial impact

The industry consensus, reflected in A-Team Insight research, is that complete end-to-end automation is not realistic. The practical target is 80:20: 80% of events processed with supervised automation, 20% handled manually for complexity, novelty, or risk.

Corporate actions may be the last custody function to achieve meaningful AI autonomy. The technology exists. The data remains broken at source. The intermediary chain amplifies friction. And the people who process these events have spent decades developing expertise that AI threatens to commoditise.

Solving this requires more than better extraction models. It requires addressing why teams still do not trust the data enough to stop checking it manually. That trust problem has a structure. The path from Level 1 to Level 3 is shorter than most custody leaders expect, once you identify which friction point is actually in the way.

I write about AI transformation and the playbooks to overcome AI adoption friction at brennanmcdonald.com. Subscribe to my newsletter for weekly insights. Subscribe here

Key Terms

Agentic AI: AI systems that can reason, plan, use tools, and take actions with minimal human oversight. Unlike traditional AI that makes predictions or generative AI that creates content, agentic AI can complete multi-step tasks on its own within defined guardrails.

AI Agent: A software system that uses AI to perceive its environment, make decisions, and take actions to achieve goals. In corporate actions, an agent might interpret announcements, identify affected positions, and process elections without human intervention for routine cases.

Human-in-the-Loop: An AI design pattern where humans review and approve AI decisions before execution. Common in current corporate actions AI, where extraction is automated but validation remains manual.

Guardrails: Constraints and boundaries that limit what an AI agent can do. In corporate actions, guardrails might include value thresholds for automatic processing, approved event types, or mandatory escalation triggers for voluntary actions.

Tool Use: The ability of an AI agent to interact with external systems, APIs, and data sources. Lets agents query position systems, access announcement feeds, send client notifications, or update entitlement records as part of completing tasks.

Autonomy Level: The degree of independence an AI system has to make decisions and take actions. Ranges from fully human-controlled (Level 0) to fully autonomous (Level 4). Most corporate actions AI currently operates at Level 1, with some firms piloting Level 2 for routine mandatory events.

Corporate Action: Any event initiated by a public company that affects its securities. Includes dividends, stock splits, mergers, rights issues, and reorganisations. Requires processing by custodians to ensure shareholders receive correct entitlements.

Voluntary Action: A corporate action requiring shareholder election, such as a tender offer or rights issue. Contrasts with mandatory actions (dividends, stock splits) that apply automatically. Voluntary actions involve tighter deadlines and client instruction collection.

Entitlement: The benefit (cash, shares, or rights) a shareholder receives from a corporate action. Calculating entitlements correctly requires accurate position data, event terms, and applicable tax treatment.

Straight-Through Processing (STP): Automated processing of transactions from start to finish without manual intervention. STP rates for corporate actions remain below 40% industry-wide despite decades of automation investment.

Sources

Industry Research

• Chainlink/industry consortium corporate actions cost analysis, SIBOS 2025

• SIFMA, "Standardize Corporate Action Announcements" (2024)

• SIX, Corporate Actions Data Survey (2021)

• SWIFT fund manager automation survey (2015)

• Oxera, "Corporate Action Processing: What Are the Risks?" (2018)

• A-Team Insight, "Automation Adds Efficiency to Corporate Actions Processing, But is Not a Sole Solution" (2024)

• ValueExchange, "Corporate Actions: An Investor's Perspective" (2024)

Standards and Implementation

• FinOps, "ISO 20022 for Corporate Actions: A for Effort, C for Completion in US" (2025)

• Clearstream Market Standards for Corporate Actions Data

Custodian and Vendor Disclosures

• BNP Paribas Securities Services ML translation tool (company-disclosed, not independently verified)

• Chainlink Phase 2 pilot results (company-disclosed, not independently verified)

Change Management Research

• McKinsey State of AI 2025

• Prosci change management failure rates

• Canadian HR Reporter, knowledge hoarding research

Article prepared February 2026. Company-reported figures have not been independently verified.

Share this email with a colleague now so they’re on the same page. We only grow through word of mouth and your referrals - Brennan

Table of Contents

• AI in Securities Settlement Prediction

  • The Challenge

  • How AI Addresses This

  • Real-World Applications

  • The Agentic Evolution

  • Implementation Considerations

  • Measuring Success

  • The Leadership Gap

  • Key Terms

  • Sources

AI in Securities Settlement Prediction

In the T+2 era, a settlement exception was a morning annoyance. In the T+1 era, it is a midnight crisis. The compression of the US exception handling window from 29.5 hours to just 6 hours represents an 80% reduction in time available to identify, investigate, and resolve potential settlement fails. Nearly two years after the May 2024 transition, fail rates have remained stable at 2-3%. But stable fail rates with dramatically less time to act means prediction is no longer optional. The firms that can identify likely fails before market close have 6 hours to fix them. The firms that cannot are managing exceptions reactively in a window that no longer tolerates reactive approaches.

This article examines how AI-powered settlement prediction works, where it is being deployed, and why most firms are not yet using it. For broader context on AI across custody functions, see The Complete Guide to AI in Global Custody. For the broader shift from automation to autonomy, see Agentic AI in Post-Trade: From Automation to Autonomy.

The Challenge

Settlement fails cost money. CSDR penalty data from ESMA shows European markets have reduced fail rates by 42-47% since the settlement discipline regime took effect in February 2022. The mechanism is simple: liquid shares incur 1 basis point per day in penalties, other assets 0.5 basis points. A $10 million failed trade in liquid shares costs $1,000 per day in penalties alone, before accounting for funding costs, operational overhead, and counterparty relationship damage.

The T+1 transition amplified the operational pressure. Under T+2, exception teams had from 4:30 PM on trade date through 10:00 AM the following morning to resolve breaks. Under T+1, that window closes at 10:00 PM on trade date itself. Cross-border trades face even tighter constraints. When New York closes at 4:00 PM Eastern, it is midnight in London. European operations teams wake up to problems they have no time to fix.

The SIFMA/ICI/DTCC After Action Report noted that same-day affirmation rates jumped from 69% in December 2023 to approximately 95% post-transition. The industry achieved this through automation, not prediction. Straight-through processing got trades affirmed faster. But affirmation is not the same as settlement. Trades can be affirmed and still fail due to inventory shortfalls, funding gaps, or counterparty issues that emerge after affirmation.

How AI Addresses This

Settlement prediction uses machine learning to identify trades likely to fail before they reach settlement date. The models analyse historical patterns across multiple dimensions: counterparty behaviour, trade characteristics, market conditions, and inventory positions.

Feature engineering matters more than algorithm selection. The most predictive features typically include counterparty historical fail rates, trade size relative to normal volume, time of day the trade was executed, asset liquidity characteristics, and whether the counterparty has outstanding fails in the same security. Models trained on these features can flag high-risk trades hours before the exception window opens.

The output is not a binary pass/fail prediction but a probability score. Models train on years of trade and fail data to identify patterns that precede settlement failures, learning which combinations of trade characteristics, counterparty behaviour, and market conditions correlate with fails. Operations teams use these scores to prioritise which trades to investigate first. In a 6-hour window, investigating the 50 highest-risk trades is feasible. Investigating 500 is not.

Real-World Applications

Custodian disclosures on settlement prediction remain thin, but some data points exist.

BNY Mellon disclosed in 2023 a predictive model for US Treasury settlement in partnership with Google Cloud. The system uses 15 months of historical Fed settlement data to train the model. Based on that disclosure, the model identifies 40% of potential fails with 90% accuracy. The 40% figure is notable: it means the model catches fewer than half of eventual fails, but the ones it catches are highly likely to actually fail. This precision-over-recall trade-off makes sense operationally. False positives waste investigation time; false negatives are failures you did not predict but would have investigated anyway through normal exception processes.

State Street disclosed in 2024 that its Alpha platform uses neural network models to reduce false positive exception alerts. The comparison cited was 4,000 genuine exceptions versus 31,000 false positives under the previous rules-based system. This is exception detection rather than prediction, but it addresses the same operational problem: helping teams focus attention on trades that actually need intervention.

JPMorgan, Northern Trust, and Citi have not published specific settlement prediction metrics. Their AI disclosures focus on reconciliation, client service, and fraud detection rather than settlement forecasting.

DTCC's Exception Manager calculates predicted CSDR penalties and prioritises exceptions by penalty size, but it does not predict settlement probability. The platform automates penalty calculation and counterparty outreach, not fail prediction. DTCC's October 2025 "Path Forward" initiative signals continued investment in post-T+1 infrastructure, though predictive AI remains a gap.

Broadridge announced at SIBOS 2025 that its OpsGPT platform now includes fail prediction using historical patterns to assess failure probability and prioritise accordingly. The platform also deploys "asset alignment AI" to ensure securities are in the correct settlement location before trades are booked, addressing a major cause of fails. Broadridge claims resolution cycles have been cut from days to hours.

BNY Mellon's Q4 2025 earnings disclosed a 75% quarter-over-quarter increase in AI solutions in production, though specific settlement prediction metrics were not broken out. State Street indicated AI-related savings would accelerate in H2 2026 and 2027. For how similar patterns play out in other custody functions, see AI Exception Management in Post-Trade.

The Agentic Evolution

The autonomy spectrum helps explain where settlement prediction AI stands and where it might go.

Based on available disclosures, most firms operate at Level 1. The BNY Treasury model appears to be Level 1-2: it scores trades and prioritises the queue, but humans investigate and resolve. The critical shift happens at Level 3, where the human moves from "in-the-loop" (approving each action) to "on-the-loop" (monitoring outcomes).

The jump to Level 3 requires more than better models. It requires systems integration that lets the agent act on predictions. An agent that predicts a securities shortfall needs access to inventory systems to source stock. An agent that predicts a funding gap needs access to liquidity systems to arrange coverage. Most custody technology stacks were not built for this kind of cross-system agency.

The technology described above exists. So why is it not working at scale?

Implementation Considerations

Adoption stalls for reasons that have little to do with model accuracy.

Data fragmentation provides the technical excuse. Settlement prediction requires joining data across trade capture, position keeping, counterparty master, and market data systems. Many firms lack a unified data layer. Building one is a multi-year programme that competes for resources with regulatory mandates and client-facing initiatives. But data integration is a solvable engineering problem. The deeper blockers are human.

The 5C Adoption Friction Model identifies where resistance emerges:

• Clarity (understanding what the model predicts): Teams may not understand probability scores or why certain trades are flagged. A 73% fail probability means what, exactly? Without clear mental models, teams default to investigating everything or ignoring the scores entirely.

• Control (accountability for model-driven decisions): If the model says a trade is low-risk and it fails, who is accountable? Settlement teams are reluctant to cede judgment to systems they did not build and cannot fully explain.

• Capability (skills to work with predictions): Using prediction models effectively requires understanding precision/recall trade-offs, score thresholds, and model limitations. Most settlement teams were not hired for these skills.

• Credibility (trust in model outputs): Early false positives or missed fails erode trust quickly. Teams revert to manual processes when models disappoint, even if the models outperform human judgment on average.

• Consequences (acceptable failure modes): Settlement fails have regulatory, financial, and reputational consequences. The asymmetry between upside (time saved) and downside (fail penalty plus relationship damage) makes teams risk-averse about trusting predictions.

Measuring Success

Settlement prediction ROI is measurable but rarely published.

Fail rate reduction is the headline metric. If prediction enables earlier intervention, fail rates should decline. The challenge is attribution: did fails drop because of prediction, or because of broader STP improvements?

Exception volume per analyst measures productivity. If prediction correctly prioritises the queue, analysts should resolve more exceptions per hour because they are working on genuine problems rather than false positives.

Funding cost reduction captures the liquidity benefit. Predicting fails earlier allows treasury to arrange funding or securities borrowing at better rates than last-minute scrambles.

Time to resolution measures operational efficiency. With a 6-hour window, resolving exceptions in 2 hours versus 5 hours creates material buffer for escalations.

If the technology is ready and the metrics are clear, the remaining bottleneck is cultural.

The Leadership Gap

Your AI initiative isn't stuck because of technology

It's stuck because of the humans around it. The AI Change Leadership Intensive uses the 5C Adoption Friction Model to identify which friction point is actually in the way, and the specific moves that would shift your first resistant group to active adoption.

Learn more

The compressed T+1 window has made settlement prediction operationally valuable in ways it was not under T+2. With the EU targeting T+1 by October 2027, European firms face the same transition US markets completed in 2024. The question is no longer whether prediction models work. The question is whether firms can integrate them into workflows fast enough to capture the benefit. Is your team still at Level 1, reviewing every alert manually? What would it take to reach Level 3, where agents investigate and resolve predicted fails within defined thresholds?

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Key Terms

Agentic AI: AI systems that can reason, plan, use tools, and take actions with minimal human oversight. Unlike traditional AI that makes predictions or GenAI that generates content, agentic AI can complete multi-step tasks autonomously within defined guardrails.

AI Agent: A software system that uses AI to perceive its environment, make decisions, and take actions to achieve goals. In settlement, agents might predict fails, source inventory, contact counterparties, and resolve exceptions without human intervention for routine cases.

Human-in-the-Loop: An AI design pattern where humans review and approve AI decisions before execution. Common in early settlement prediction implementations where analysts review model flags before taking action.

Guardrails: Constraints and boundaries that limit what an AI agent can do. In settlement, guardrails might include transaction value thresholds for auto-resolution, approved counterparty lists, or mandatory escalation triggers for cross-border trades.

Tool Use: The ability of an AI agent to interact with external systems, APIs, and data sources. Enables settlement agents to query inventory systems, initiate securities lending, contact counterparties, or update settlement instructions as part of resolving predicted fails.

Autonomy Level: The degree of independence an AI system has to make decisions and take actions. Ranges from fully human-controlled (Level 0) to fully autonomous (Level 4). Most settlement prediction is currently Level 1-2.

Settlement Fail: A trade that does not settle on the intended settlement date due to securities shortfall, funding gap, instruction mismatch, or counterparty issue. Fails incur penalties under CSDR in Europe and funding charges in the US.

T+1 Settlement: The requirement for trades to settle one business day after trade date, effective May 28, 2024 in the US. Compressed the exception handling window from approximately 29.5 hours to 6 hours.

CSDR: Central Securities Depositories Regulation, the European framework that introduced mandatory cash penalties for settlement fails in February 2022. Penalty rates range from 0.5 to 1.0 basis points per day depending on asset type.

Same-Day Affirmation (SDA): The process of confirming trade details between counterparties on trade date rather than waiting until settlement date. Critical for T+1 readiness; industry rates improved from 69% to 95% during the T+1 transition.

Sources

1. ESMA Final Report on CSDR RTS Settlement Discipline (October 2025) - EU T+1 target date of October 2027, updated settlement discipline rules, fail rate improvements since 2022.

2. DTCC "The Path Forward" Initiative (October 2025) - Post-T+1 clearing and settlement transformation roadmap.

3. Broadridge SIBOS 2025 Announcement (October 2025) - OpsGPT fail prediction capabilities, asset alignment AI, autonomous agent deployment.

4. BNY Mellon Q4 2025 Earnings (January 2026) - 75% QoQ increase in AI solutions in production, Eliza platform expansion.

5. State Street Q4 2025 Earnings (January 2026) - AI savings acceleration timeline (H2 2026-2027), record revenue of $14B.

6. AFME High-Level Roadmap to T+1 (June 2025) - EU industry committee established, technical and operational change requirements.

7. SIFMA/ICI/DTCC T+1 After Action Report (September 2024) - Post-T+1 fail rates (2-3% stable), affirmation improvements (69% to 95%), clearing fund reduction ($3B).

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Table of Contents

• What 97% Actually Means

• Where the 3% Lives

• The Real Bottleneck

• What Good Actually Looks Like

• Questions for Your Operations Team

• Key Terms

• Sources

When a vendor reports a 97% auto-match rate, the natural assumption is that the problem is 97% solved. It is not. That figure measures the easy part: deterministic matching on standard fields where records align cleanly. What remains is the 3% where complexity lives, where T+1 cut-offs get missed, where counterparty disputes escalate, and where operations teams work late.

Custody operations are defined by the exceptions, not the volume that flows through smoothly. The firms that confuse high auto-match rates with transformation are the ones whose reconciliation teams remain the bottleneck. For context on how reconciliation fits within the broader AI transformation of custody, see The Complete Guide to AI in Global Custody.

What 97% Actually Means

Most vendor metrics measure what happens when records are clean, identifiers are standard, and data arrives on time. An exact match on trade identifier, instrument code, quantity, price, and settlement date is not an AI achievement. It is table stakes.

The critical questions are rarely answered in marketing materials. What is the denominator: all records processed, or only those that passed initial validation? Which asset classes are included: equities with standard identifiers, or OTC derivatives where matching is genuinely difficult? What counts as a match: exact, fuzzy within tolerance thresholds, or AI-suggested requiring human confirmation?

One major custodian disclosed that its rules-based exception processing flagged over 31,000 exceptions over six months, of which only 250 were true positives. The AI-enabled replacement identified approximately 4,000 exceptions while catching 100% of genuine issues. That disclosure is notable because it reports both sides: false positive reduction and true positive capture. Most vendor claims report only the first.

Where the 3% Lives

OTC derivatives, loans, and commodities lack standard security identifiers. When there is no ISIN or CUSIP, the system must interpret counterparty-specific references that vary across jurisdictions and trading relationships. Match rates for these asset classes drop steeply from the headline figures vendors report for equities and government bonds.

Counterparty statements arrive in formats that resist automation. PDF nostro statements, faxed confirmations, emails with embedded tables require extraction and interpretation before matching can begin. The investment leading vendors are making in unstructured document handling is telling: this is where the operational burden sits.

Cross-currency transactions create breaks even when the underlying trade is correct. Roughly one quarter of firms cite cross-currency matching as their greatest reconciliation challenge. Exchange rate differences, timing variances, and inconsistent rate sources generate exceptions requiring judgment, not computation.

Late-arriving data creates noise in the exception queue. When the counterparty file does not arrive until after the cut-off, the break is real but not meaningful. Operations teams spend time re-checking once data arrives rather than investigating genuine discrepancies.

This is where the 15-year veteran of your reconciliation team earns their salary. They know which counterparties consistently send late files. They recognise patterns in how specific custodians format their statements. They understand which breaks resolve themselves and which require escalation. That institutional knowledge does not transfer easily to an AI system.

The Real Bottleneck

Recent research found that most firms struggle to capture value from AI "not because the technology fails, but because their people, processes, and politics do." Fear of replacement, rigid workflows, and entrenched power structures derail initiatives even in companies with advanced tools.

The reconciliation team that has been doing this work for 15 years did not sign up to become "exception managers" or "digital system trainers." When technology change is positioned as threat rather than augmentation, adoption stalls regardless of system performance. For a deeper examination of why AI projects fail despite working technology, see Agentic AI in Post-Trade: Why Most Projects Fail and What to Do About It.

What Good Actually Looks Like

At Sibos 2025, one vendor disclosed that their AI reconciliation product reduced manual effort from 40-45 hours to approximately three hours for a specific deployment. That is a tenfold productivity gain on the work that actually matters: the exceptions, not the volume.

The measure of success is not the auto-match rate. It is what happens to the 3%.

An AI-prioritised exception queue presents the highest-impact breaks first. When a break affects settlement timing, client reporting, or regulatory compliance, it surfaces immediately. Contextual enrichment pulls relevant data before the analyst sees the break: historical patterns with this counterparty, similar breaks and how they were resolved, related transactions that might explain the discrepancy. Investigation starts with context, not a blank screen.

Resolution pattern learning allows the system to incorporate analyst decisions into future matching. When a senior analyst resolves a break in a particular way, that pattern becomes available to the model. The goal is not replacing the 15-year veteran. It is giving them 90% of their time back for the cases that genuinely need their expertise.

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Questions for Your Operations Team

Does your team share a common understanding of what you are actually building? When leadership expects autonomous agents but operations expects assisted tools, the disconnect registers as failure regardless of technical performance.

Do you control this workflow, or is it outsourced? You cannot effectively transform operations you do not directly manage.

Has your team been trained to supervise AI, not just use it? The role shift from "first line of defence" to "trainer of digital systems" requires skills most operations professionals have not developed.

Do your analysts trust the AI's exception prioritisation? Trust cannot be mandated. It must be earned.

Can your team see personal benefit in the transformation? If becoming an "exception manager" feels like demotion rather than elevation, adoption will stall.

Reconciliation proves the technology works. AI can match records, reduce false positives, and prioritise exceptions. The firms that treat high auto-match rates as mission accomplished are the ones whose teams remain the bottleneck on T+1 cut-off nights. The measure of success is not the 97%. It is what happens in the 3%.

If your reconciliation transformation is stalled

If you want to explore what is actually in the way, you can book a conversation here.

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Key Terms

Reconciliation: Comparing internal records against external sources to identify discrepancies requiring investigation.

Break: A discrepancy identified during reconciliation. May represent a genuine error or a timing difference.

Exception: A record flagged as requiring human review. Not all exceptions represent genuine breaks.

Auto-Match Rate: Percentage of records matched without human intervention. Varies significantly by asset class and methodology.

False Positive: An exception that, upon investigation, does not represent a genuine break.

Observational Learning: Machine learning approach where the system learns by watching how analysts resolve exceptions.

Sources

State Street Q3 2025 10-Q Filing (17 October 2025)

Sibos 2025 Conference Coverage (September-October 2025)

Opimas, "Reconciliation for Securities: Vendor Solutions and the Impact of AI" (2025)

Harvard Business Review, "Overcoming the Organizational Barriers to AI Adoption" (November 2025)

Kani Payments, "When Reconciliation Breaks" (August 2025)

Article prepared February 2026. Company-reported figures have not been independently verified.

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Table of Contents

• The Definitional Problem

  • A Proposed Autonomy Framework (L1-L4)

• Where Post-Trade Actually Stands

  • What "Production" Means

  • BNY: Disclosed Supervised Autonomy

  • State Street: The Case for Operational Control

  • Disclosure Gaps

• Why AI Projects Fail

• Governance Considerations

• Limits of the Current Evidence

• The Path Forward

• Sources

• Glossary

You know the pattern. The technology works. The pilots succeed. Then something happens between proof-of-concept and production, and the initiative stalls.

Budgets get reallocated. Sponsors move on. The AI team is quietly absorbed into other functions. According to the MIT NANDA Initiative, 95% of generative AI pilots fail to achieve revenue acceleration. Gartner predicts more than 40% of agentic AI projects will be cancelled by 2027. Deloitte reports that while 38% of organisations are piloting agentic solutions, only 11% have systems in production. Definitions of "pilot," "agentic," and "production" vary across these studies, which limits direct comparison. But the signal is consistent: there is a wide gap between pilot success and sustained production value.

If this sounds familiar, the question is why. The conventional explanation blames technology: models hallucinate, data quality is poor, legacy systems resist integration. These challenges are real. But they do not explain why most projects stall.

The technology is not the problem.

This article examines an alternative hypothesis: that the real blocker is often organisational, not technical. Diagnosing which friction point is in the way may matter more than selecting the right vendor or model.

Post-trade reflects these patterns. Global custodians are expanding AI teams, budgets, and platform capabilities. BNY reports 117 AI solutions in production as at Q3 2025. State Street has operationalised its Alpha AI Data Quality platform. Northern Trust, according to a December 2025 press release, has reduced custody tax operations from eight hours to thirty minutes using AI analytics. Yet the gap between pilot and production remains wide.

Research points to two friction points that merit early attention: clarity about the autonomy target, and control over the workflows being transformed. The MIT NANDA Initiative found that line manager empowerment tracks with success. State Street's insourcing decision before AI deployment reinforces control as foundational. Deloitte's finding that 42% of organisations lack a defined agentic strategy points to clarity. The remaining three friction points (capability, credibility, and consequences) may become binding only after these first two are addressed.

The Definitional Problem

There is no consensus definition of what agentic AI actually means.

Gartner describes it as artificial intelligence systems that take action-based roles, operating independently or in collaboration with humans. IDC Research Director Heather Hershey sets a higher bar: autonomous systems that can independently plan, reason, make decisions, and execute multi-step tasks with minimal human intervention.

That gap affects what buyers expect. Many products currently marketed as "agentic" are closer to traditional automation wrapped in a conversational interface. Industry observers have a term for this: agent-washing. The confusion is reflected in recent commentary:

"I've seen many 'agentic AI' products in the past twelve months that, upon further investigation, were AI co-pilots or LLM wrappers on conventional machine learning. There was no 'agent' in the 'agentic AI'. Buyers will need to arm themselves with the knowledge of what agentic AI is and what it is not in order to avoid overpaying for features that cannot perform as promised." — Heather Hershey, Research Director, IDC (2025)

"Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied." — Anushree Verma, Analyst, Gartner (June 2025)

A Proposed Autonomy Framework (L1-L4)

One way to cut through this confusion is to classify AI systems by their level of autonomy. This framework is a practical alignment tool, not an industry standard. Its value is in forcing explicit conversation about autonomy targets.

Level 1 (Assisted): AI generates recommendations only. No system writes. Humans review outputs and decide whether to act.

Tests: (1) Does the AI write to any system of record? (2) Can the AI execute without human approval? If both answers are no, it is L1.

Level 2 (Supervised Autonomy): AI executes system writes (tickets, messages, repairs, data updates) within enforced guardrails. Mandatory human review thresholds and audit trails.

Tests: (1) Does the AI write to systems of record? (2) Are there defined guardrails with human review triggers? If both answers are yes, it is L2.

Level 3 (Conditional Autonomy): AI proposes and executes novel multi-step plans across tools. Human intervention by exception only.

Tests: (1) Can the AI chain multiple actions without per-action approval? (2) Does it handle novel scenarios not pre-defined in rules? If both answers are yes, it is L3.

Level 4 (Full Autonomy): AI operates without human oversight across all scenarios. Not a current goal for custody operations.

Why does this matter? Because vendor claims often conflate Level 1 and Level 2 capabilities with Level 3 and Level 4 aspirations. When buyers expect autonomous agents but receive assisted tools, the initiative gets classified as failure even if the technology performed exactly as designed.

For a detailed examination of how AI is currently deployed across custody operations, see The Complete Guide to AI in Global Custody, the first article in this series.

Where Post-Trade Actually Stands

There is a gap between agentic AI rhetoric and post-trade reality. Understanding what firms have publicly disclosed, versus what vendors claim, provides context.

What "Production" Means

In custody and post-trade, "production" typically means: (1) integrated into core workflow, not a side tool; (2) clear ownership across operations and technology; (3) defined escalation, audit, and rollback procedures; (4) monitored error rates with incident playbooks; (5) measurable improvement sustained over multiple months. Claims of production deployment should be tested against these criteria.

BNY: Disclosed Supervised Autonomy

BNY has been among the most transparent disclosers of AI deployment among major custodians.

Disclosed: BNY operates 117 AI solutions in production as at Q3 2025 (earnings release). Over 100 digital employees work on payment validations and code repairs (CEO statement). 98% of employees have been trained on generative AI; 20,000 are actively building agents (company disclosures). The NAV calculation agent achieved a 40% reduction in false positives; legal contract review time decreased by 25% (company-reported, not independently verified).

Inference: Based on the "trainer and nurturer" framing and defined escalation protocols, these deployments operate at Level 2 (Supervised Autonomy). Chief Data and AI Officer Sarthak Pattanaik described the shift: "Now, instead of handling certain tasks in the first instance, the role of the human operator is to be the trainer or the nurturer of the digital employee."

BNY's approach addresses multiple friction points: the 98% training rate targets Capability; the "trainer and nurturer" framing targets Consequences; the defined escalation protocols address Credibility by building trust in the system's guardrails.

Not publicly disclosed: Human review rates, rollback procedures, error budgets, audit scope, and incident frequency. Most custodians do not disclose operational AI metrics at this level of granularity.

State Street: The Case for Operational Control

State Street's approach offers a different lesson about prerequisites for AI deployment.

Disclosed: The firm insourced operations previously outsourced to vendors in India (Tahiri statement, June 2025). The Alpha AI Data Quality system reduced false positive exceptions from over 31,000 to approximately 4,000 over six months while maintaining detection of genuine issues (company-reported).

Executive Vice President and Chief Operating Officer Mostapha Tahiri explained the rationale at the Fortune COO Summit in June 2025: "Why would you insource more people in an era of AI?" His answer: operational control is a prerequisite for change. Third-party arrangements create friction due to compliance complexity, cultural misalignment, and coordination costs. When you outsource an operation, you also outsource your ability to transform it.

Inference: The insourcing decision suggests operational control was viewed as prerequisite for AI deployment. The principle (you cannot transform operations you do not control) holds regardless of specific numbers. This move directly addresses Control, establishing operational ownership as prerequisite for transformation.

Not publicly disclosed: Cost of insourcing, timeline, scope of operations affected, and current AI deployment status in insourced functions. This is standard practice for strategic operational decisions.

For detailed analysis of AI in settlement and clearing operations, see AI in Clearing and Settlement, the third article in this series.

Disclosure Gaps

Despite extensive industry coverage, no major custodian has publicly disclosed a fully autonomous agentic system (Level 3 or above) in production custody operations. The most advanced deployments disclosed operate with human supervision.

"It depends on what you say an agent is, what you think an agent is going to accomplish and what kind of value you think it will bring. It's quite a statement to make when we haven't even yet figured out ROI on LLM technology more generally." — Marina Danilevsky, Senior Research Scientist, IBM (November 2025)

Other major custodians, including JPMorgan, Citi, and Northern Trust, have disclosed less about their AI initiatives. Direct comparison is difficult, and BNY's transparency may not be representative of industry practice.

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Why AI Projects Fail

The failure statistics are sobering. But understanding why projects fail is harder than counting them. Research points to several categories of adoption friction that may distinguish successful implementations from abandoned initiatives.

1. Clarity

Deloitte reports that 42% of organisations are still developing their agentic strategy roadmap, with 35% having no formal strategy at all. When leadership expects autonomous agents but operations teams expect assisted tools, the disconnect registers as project failure regardless of technical performance.

Without shared clarity, every stakeholder evaluates the initiative against different expectations. The autonomy framework above exists precisely to force this conversation: are we building Level 1 or Level 2? What would Level 3 require? Agreement on the target is prerequisite for meaningful progress assessment.

2. Control

The MIT NANDA Initiative found that empowering line managers rather than just central AI labs to drive adoption tracks with success. According to the study, vendor purchases succeed approximately 67% of the time, while internal builds succeed only about 22%.

When AI initiatives are driven top-down without business unit sponsorship, adoption stalls. State Street's insourcing decision exemplifies this at the operational level: the firm sought direct control over processes before attempting to transform them.

3. Capability

According to BCG's 2025 survey, only one-third of financial services employees have received adequate training in generative AI. The AICPA and CIMA's December 2025 survey found that 88% of respondents believe AI will be the most transformative technology trend over the next 12 to 24 months, but only 8% feel their organisation is "very well prepared."

BNY's response has been heavy investment in capability. According to company disclosures, 98% of employees have received generative AI training and approximately 20,000 employees are actively building AI agents through the Eliza platform.

4. Credibility

A December 2025 YouGov survey found that only 19% of Americans trust AI in financial services, while 48% say they do not. According to the Edelman Trust Barometer, among those who already distrust AI, 67% feel it is being "forced" upon them.

Trust cannot be mandated. When executives push AI adoption faster than employees find comfortable (and the IBM CEO Study found that 60% of banking and financial markets CEOs acknowledge doing exactly this), the result is compliance rather than genuine adoption.

5. Consequences

The Edelman Trust Barometer Flash Poll from November 2025 contains a finding worth attention. The survey covered respondents across five countries (Brazil, China, Germany, UK, US); results may vary by geography and organisation type.

When organisations frame AI adoption around job security (telling employees their jobs are safe and AI will not replace them), only 26% embrace the technology. When organisations reframe around job transformation (how AI helps employees do their current jobs better), the embrace rate rises to 43%.

Framing may matter as much as substance. Teams that can see personal benefit from becoming agent supervisors adopt more readily. Teams that perceive threat, even implicit threat, resist.

But framing works only when it matches practice. Training, role definitions, and career paths must align with the story being told. Messaging without structural change reads as manipulation.

BNY's approach reflects this. Chief Information Officer Leigh-Ann Russell stated that "while productivity is a metric, 'joy' and employee satisfaction are equally important. AI removes the drudgery from work." The firm positions human operators as "trainers and nurturers" of digital employees rather than workers being displaced.

AI will affect employment. Morgan Stanley analysis projects that European banks will cut approximately 200,000 jobs over the next five years, with back-office, middle-office, and operational roles most exposed (reported in Financial Times, "European banks set to cut 200,000 jobs as AI takes hold," December 2025). Successful framing requires acknowledging change while emphasising transformation rather than displacement.

For analysis of how AI is transforming digital asset custody specifically, see AI in Digital Asset Custody, the second article in this series.

Governance Considerations

Regulatory frameworks for agentic AI are emerging but not yet fully defined.

The European Union AI Act achieves full enforcement for high-risk AI systems on 2 August 2026. Credit scoring, which touches custody operations through collateral valuation and counterparty assessment, is classified as high-risk. Non-compliance carries penalties of up to 7% of global annual turnover.

In the United States, Federal Reserve SR Letter 11-7 establishes model risk management requirements. While specific guidance on agentic AI has not been issued, these requirements would reasonably apply to AI systems.

Research from the Federal Reserve Bank of Richmond, published in October 2025, found that banks with higher AI intensity incur greater operational losses than their less AI-intensive counterparts, driven by external fraud, customer problems, and system failures. But the researchers noted: "AI's negative effect is especially pronounced for banks that lack strong risk management, suggesting that robust internal control is an important prerequisite."

Governance may enable adoption rather than constrain it. Teams that trust the controls around AI systems adopt more readily than teams that perceive ungoverned risk.

This discussion is not legal advice. Firms should confirm regulatory obligations with counsel and relevant supervisory authorities.

Limits of the Current Evidence

Intellectual honesty requires acknowledging the limits of current evidence.

No verified Level 3+ deployments exist in production post-trade operations. The most advanced publicly disclosed systems operate with human supervision. Claims of fully autonomous custody agents remain aspirational or vendor-driven based on available disclosures.

Quantified ROI from custodian agentic AI has not been independently verified. BNY reports efficiency metrics, but these are company-disclosed figures. Whether improvements derive from agentic autonomy or Level 1-2 assistance is often unclear from public disclosures.

Regulatory enforcement under the EU AI Act is untested. The Act does not achieve full enforcement until August 2026.

Which friction point matters most remains an open question. The categories of friction likely interact in ways that vary by organisation. Diagnosing the binding constraint requires assessment, not assumption.

The evidence base for change management approaches is still developing. The studies cited in this article represent early findings that may or may not generalise.

The Path Forward

The realistic near-term model for agentic AI in post-trade is supervised autonomy: agents that handle routine tasks independently while escalating complexity to humans. This is not a failure of ambition. It fits an operational context where errors are not tolerable.

Global custody involves safeguarding nearly $200 trillion in assets. A hallucination in a NAV calculation, a misclassified corporate action, or a failed settlement instruction can trigger regulatory breaches, client losses, and reputational damage. BNY's digital employee framework, with defined escalation protocols, fits this context.

The path forward is probably not better technology. The technology works at Level 2 and shows promise at Level 3. The path forward is figuring out what is actually in the way.

Questions Worth Asking

Do your stakeholders share a common definition of what agentic AI means and what level of autonomy you are targeting? Misalignment here registers as project failure regardless of technical performance.

Are your line managers empowered to drive adoption, or is AI being pushed from central functions without business unit ownership? The MIT NANDA finding on vendor purchases versus internal builds suggests control counts.

Have your operations teams received adequate training to supervise AI agents? The BCG finding that only one-third have received adequate training suggests this is an industry-wide gap, not a failing of any particular firm.

Do your employees trust the AI systems you are deploying? The YouGov finding that only 19% of Americans trust AI in financial services suggests scepticism is the default, not the exception.

Can your employees see personal benefit from becoming agent supervisors? If they perceive only threat, framing alone will not resolve resistance.

That diagnosis is where most initiatives should start.

If you found this analysis useful

I write about AI transformation with more of a change management focus at brennanmcdonald.com. Subscribe to the newsletter for weekly analysis of how the industry is handling these challenges.

If you are facing a stalled initiative

The 5C Adoption Friction Model described in this article is the diagnostic framework I use with operations and technology teams. If you want to explore whether this approach might help identify what is blocking your initiative, you can book a conversation here.

The author previously spent 12 years working in technology and change roles in financial services, most recently at a global custodian.

Sources

Primary Sources

BNY Q3 2025 Earnings Release (16 October 2025)

State Street Q3 2025 10-Q Filing and Earnings Call (17 October 2025)

Northern Trust Press Release (9 December 2025)

EU AI Act Full Text and Implementation Timeline

FSOC Annual Report (December 2024)

Executive Statements

Robin Vince, CEO, BNY: Q3 2025 Earnings Call (October 2025)

Leigh-Ann Russell, CIO & Global Head of Engineering, BNY: AI in Finance Interview (2025)

Sarthak Pattanaik, Chief Data & AI Officer, BNY: OpenAI Case Study (2025)

Mostapha Tahiri, EVP & COO, State Street: Fortune COO Summit (June 2025)

Marina Danilevsky, Senior Research Scientist, IBM (November 2025)

Anushree Verma, Analyst, Gartner (June 2025)

Heather Hershey, Research Director, IDC (2025)

Research and Industry Reports

MIT NANDA Initiative (August 2025)

Gartner Agentic AI Predictions (June 2025, August 2025)

Deloitte Emerging Technology Trends Study (December 2025)

Edelman Trust Barometer Flash Poll: Trust and Artificial Intelligence (November 2025)

YouGov AI Trust Survey (December 2025)

BCG AI Training Survey (2025)

AICPA/CIMA Future-Ready Finance Survey (December 2025)

IBM CEO Study: Banking and Financial Markets (2024)

Federal Reserve Bank of Richmond: AI and Operational Losses (October 2025)

Financial Times, "European banks set to cut 200,000 jobs as AI takes hold" (December 2025), reporting Morgan Stanley analysis

Related Articles in This Series

The Complete Guide to AI in Global Custody 

AI in Digital Asset Custody 

AI in Clearing and Settlement 

Glossary

Agentic AI: Artificial intelligence systems that take action-based roles, operating independently or in collaboration with humans to achieve specific goals.

Agent-washing: A term used by industry observers for the practice of marketing AI products as "agentic" without delivering autonomous capabilities.

Digital employee: BNY's term for AI agents treated as colleagues with identities, managers, and defined roles, operating within guardrails with escalation protocols.

Autonomy Spectrum (L1-L4): A proposed framework for classifying AI systems by level of autonomous operation, from assisted (L1) through supervised autonomy (L2), conditional autonomy (L3), to full autonomy (L4). This is a practical alignment tool, not an industry standard.

5C Adoption Friction Model: A diagnostic framework identifying five categories of potential barriers to AI adoption: Clarity, Control, Capability, Credibility, and Consequences.

MIT NANDA Initiative: Research initiative at MIT studying AI adoption patterns in enterprise settings.

Article prepared January 2026. All figures as at Q3 2025 unless otherwise noted. Company-reported figures have not been independently verified.

This is the third instalment in our new series for Q1 2026. The first article, “The Complete 2026 Guide to AI in Global Custody”, achieved some of our highest email engagement ever. If implementing AI is part of your KPIs this year - share this email with a colleague now so they’re on the same page. We only grow through word of mouth and referrals.

Table of Contents

• Why AI in Clearing and Settlement Matters

• Key Use Cases

  • Settlement Fail Prediction

  • Client Query Handling

  • Liquidity Sourcing and Positioning (Potential Appl …

  • Netting Optimisation (Potential Application)

• What Market Infrastructure Operators Have Announce …

• The Path to Autonomy: Agentic AI

• What We Still Don't Know

• Implementation Challenges

• Getting Started

  • Applying the 5C Adoption Friction Framework

• What's Next

• Sources

• Glossary

Based on company disclosures from 2024 and 2025, AI in clearing and settlement now includes production deployments in specific functions. As of January 2026, European market participants have roughly 21 months before the EU moves to T+1 settlement on 11 October 2027. The United States made this shift on 28 May 2024, cutting the window for exception resolution from two days to one. According to the SIFMA/ICI/DTCC After Action Report, same-day affirmation rates for institutional equity trades in DTC scope improved from 73% to roughly 95%. Yet CNS settlement fail rates, as reported in the same document, stay at roughly 2% to 3%. Settlement fails for in-scope transactions can incur CSDR cash penalties of 0.25 to 1.0 basis points per day.

The question is whether AI will matter in your post-trade operations before T+1 makes current exception-handling methods untenable. Building prediction models, linking them to legacy systems, training operations teams, and setting up governance takes time. Firms starting now can still choose their approach; firms starting in late 2026 are more likely to accept constraints set by vendors, scope limits, or interim workarounds.

This guide reviews disclosed deployments at DTCC, Clearstream, Euroclear, CME, and LSEG based on company announcements and regulatory filings. It separates what companies have announced from what remains speculative. And it provides a framework for assessing where AI could add value in your operations. For broader context on how custodians are approaching AI across all functions, see our companion guide: The Complete Guide to AI in Global Custody.

A note on method: This guide relies on publicly available company disclosures, press releases, and regulatory filings. We have not independently verified these deployments. Where companies have announced capabilities, we report what they have claimed; where evidence is unavailable, we say so.

Why AI in Clearing and Settlement Matters

Three forces have made AI adoption increasingly relevant for clearing and settlement operations. The pressures differ somewhat for central counterparties (focused on margin and default management), central securities depositories (focused on settlement efficiency), and their participants (focused on exception handling and funding). But the underlying drivers affect all segments.

Compressed settlement cycles. The US moved to T+1 on 28 May 2024. The EU confirmed T+1 for 11 October 2027, with political agreement reached in June 2025 and publication in the EU Official Journal in October 2025. Each compression shrinks the window for manual exception resolution. What previously allowed overnight investigation now requires same-day action.

Penalty regimes with financial consequences. The CSDR settlement discipline regime, in effect since February 2022, imposes daily cash penalties for settlement fails on in-scope transactions. Rates range from 0.25 basis points for sovereign bonds to 1.0 basis point for liquid equities. According to ESMA data, fail rates by value have roughly halved since the regime took effect, from around 6.6% in January 2022 to roughly 3.8% by early 2024. While 81% of individual penalties are under EUR 50, total costs for large participants can be material.

Volume at scale. According to its 2024 Annual Report, DTCC holds roughly USD 85 trillion in securities and processes transactions valued at roughly three quadrillion dollars annually. The FICC Government Securities Division alone handles over USD 10 trillion in daily activity. At this scale, even small percentage drops in fail rates mean large reductions in operational cost, capital needs, and counterparty risk.

Terminology: Throughout this guide, a settlement fail refers to a settlement instruction that does not complete on the intended settlement date. This may occur at the instruction level (a single delivery or receipt) or be grouped at the trade level depending on context. Fail rates cited from DTCC sources refer to CNS (Continuous Net Settlement) and Non-CNS instruction populations as specified in the After Action Report.

Key Use Cases

Based on company announcements and logical inference from current pain points, several areas show potential for AI. The first two have announced deployments; the latter two are logical extensions that have not been publicly disclosed.

Settlement Fail Prediction

The most commonly announced AI use case in settlement is predicting which instructions are likely to fail before the settlement date. According to a July 2025 press release, Clearstream enhanced its Settlement Prediction Tool to provide predictions up to four business days in advance. The company states the tool provides settlement likelihood scores, root cause attribution, and estimated penalty costs. Euroclear announced the launch of EasyFocus+ in June 2025 with similar stated capabilities: matching scores, matching quantiles, root cause analysis, and CSDR penalty predictions.

The value is straightforward: if operations teams know which instructions are at risk, they can act before the settlement window closes. In a T+1 setting, this shifts from useful to essential. Neither Clearstream nor Euroclear has disclosed accuracy metrics, false positive rates, or precision/recall figures for their prediction models.

Client Query Handling

A large part of post-trade operations involves responding to client queries about settlement status, fails, and corporate actions. According to an AWS case study published in January 2025, LSEG deployed Amazon Q Business for post-trade client services, using a retrieval-augmented generation setup to answer queries across OTC rates, listed rates, fixed income, FX, CDS, and equities. The case study states the system uses Amazon Bedrock and Anthropic Claude to generate responses based on internal documentation.

This use case shows the stated application of generative AI to cut the burden on client service teams. The limit is that these systems answer questions but do not take actions: they operate at Level 1 autonomy, assisting human decision-making without executing changes. How well they work depends on the quality and consistency of underlying data and procedures.

Liquidity Sourcing and Positioning (Potential Application)

No market infrastructure operator has publicly disclosed AI-driven liquidity sourcing for settlement. However, the logic is clear: if a model can predict which instructions will fail due to insufficient securities or cash, it could also propose sourcing actions. An agent might identify a projected shortfall, query available inventory across accounts, propose a securities lending transaction or internal transfer, and present the solution for human approval.

No confirmed deployments exist. The complexity lies not only in technical integration but in control needs: approval workflows, audit trails, and segregation of duties. Any system proposing funding transactions would need governance comparable to existing treasury controls.

Netting Optimisation (Potential Application)

Multilateral netting reduces settlement obligations by offsetting buy and sell positions across participants. According to the SIFMA/ICI/DTCC After Action Report, NSCC Clearing Fund requirements fell by USD 3.0 billion (23%) following T+1, partly due to shorter exposure windows. No CCP or CSD has disclosed AI-driven netting optimisation, but the mathematical nature of the problem (finding optimal offsets across thousands of positions) is well suited to algorithmic approaches.

A potential application would be AI that identifies additional netting opportunities within existing constraints, or that sequences settlement instructions to maximise netting efficiency within a settlement cycle. Constraints may include risk limits, legal entity boundaries, and cut-off times. No public disclosures confirm such deployments.

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What Market Infrastructure Operators Have Announced

The major market infrastructure operators have disclosed varying levels of AI investment. What follows summarises their public announcements from 2024 and 2025. We have not independently verified these claims.

DTCC. The 2024 Annual Report describes an AI Council and AI Enablement Team to coordinate strategy, deployment of over 300 online AI courses for employees, and a four-pillar AI strategy covering productivity, client experience, risk mitigation, and research. DTCC reported net income of USD 482 million for 2024 and total revenue of USD 2.486 billion. The report does not disclose specific AI-driven productivity metrics or accuracy rates.

Clearstream. According to a July 2025 press release, Clearstream (Deutsche Boerse) enhanced its Settlement Prediction Tool to provide predictions up to four business days ahead, available through the Xact Web Portal. No accuracy metrics have been disclosed.

Euroclear. A June 2025 press release announced EasyFocus+ in partnership with Microsoft, Meritsoft (Cognizant), and Taskize, targeting improvement from 65% to 99% trade date matching. A separate Q1 2025 announcement described a seven-year partnership with Microsoft covering cloud, data, and AI. Investment amounts have not been disclosed.

LSEG/LCH. According to an AWS case study published in January 2025, LSEG deployed Amazon Q Business for post-trade client services, using a retrieval-augmented generation setup with Amazon Bedrock and Anthropic Claude.

CME Group. The February 2025 10-K filing describes SPAN 2, a VaR-based margin methodology deployed for energy products in Q3 2023, extended to equities in September 2024, and scheduled for agricultural products in H2 2025. The filing does not explicitly reference AI or machine learning in clearing operations beyond these risk management enhancements.

What these announcements suggest: Market infrastructure operators are investing in data and AI programs, with announced deployments concentrated in prediction and client-service knowledge retrieval. What they do not reveal: accuracy rates, false positive rates, or measured operational impact. This opacity could reflect competitive sensitivity, early-stage deployment, or results that do not yet justify public claims. For practitioners, these announcements should be treated as directional rather than definitive.

The Path to Autonomy: Agentic AI

The announced deployments operate mainly at Level 0 to Level 1 on the autonomy spectrum: humans perform tasks with AI providing information or recommendations. The question is whether and when clearing and settlement operations will move to higher autonomy levels. For a full treatment of the agentic AI spectrum and its implications for financial services, see our forthcoming guide: Agentic AI in Securities Services.

Level 0 (Manual): Human performs task without AI assistance. This remains the default for most exception handling.

Level 1 (Assisted): AI provides recommendations; human decides and executes. The Clearstream and Euroclear prediction tools, as described in their announcements, would operate here.

Level 2 (Augmented): AI proposes specific actions; human approves; system executes. This is where exception resolution could move with appropriate guardrails. A Level 2 system might gather evidence on a predicted fail, identify the root cause, draft a counterparty message or SSI correction, and queue the action for human approval.

Levels 3-4 (Supervised to Autonomous): At Level 3, AI executes within defined boundaries while humans monitor and handle exceptions. At Level 4, AI operates independently with human oversight of outcomes only. Both remain unlikely in the near term for critical market infrastructure given systemic importance and regulatory expectations around accountability and explainability.

The constraint on higher autonomy is not mainly technical but systemic. CCPs and CSDs are systemically important financial market infrastructures. Regulators require robust governance, clear accountability, and the ability to explain decisions. BIS Working Paper 1194, published in June 2024, identifies specific risks: herding behaviour if multiple institutions use similar models, concentration risk in AI providers, and lack of explainability in complex models. These concerns apply directly to clearing and settlement.

The more likely path is gradual expansion of Level 2 capabilities: AI agents that investigate exceptions, propose resolutions, and prepare actions for human approval. This approach maintains human accountability while cutting the manual burden of investigation. Firms that develop effective Level 2 systems will have optionality to move toward Level 3 as regulatory comfort and model performance improve.

What We Still Don't Know

Intellectual honesty requires acknowledging the limits of available evidence. The following questions cannot be answered from public disclosures:

Prediction accuracy. Neither Clearstream nor Euroclear has disclosed accuracy rates, false positive rates, or precision/recall metrics for their settlement prediction tools. Without this data, it is not possible to assess operational value or compare approaches.

Return on investment. No market infrastructure operator has disclosed the cost of AI development or the operational savings achieved. Business cases remain unverified.

Regulatory stance. ESMA has not published guidance on AI governance for CSDs. It is unclear whether regulators will require explainability, validation, or approval for AI systems in critical settlement functions.

Cross-border interoperability. With DTCC, Euroclear, and Clearstream each developing proprietary AI tools, no data exists on whether these systems can share predictions or learnings across jurisdictions.

Agentic deployments. No market infrastructure operator has publicly disclosed AI agents that take actions (Level 2 or above) in production settlement operations. Announced deployments are assistive, not agentic.

Liquidity and netting AI. The potential applications described above for liquidity sourcing and netting optimisation are logical inferences, not confirmed deployments. Treat them as hypotheses requiring validation.

Independent verification. We have not independently verified any of the deployments described in this guide. Our account is based on company disclosures.

Questions for vendor and infrastructure due diligence:

• What is your false positive rate, and how is it measured?

• What is your model drift monitoring approach?

• What happens when the model is wrong, and how are corrections handled?

• Can you provide backtested precision and recall by fail reason?

• What training data was used, and how is it refreshed?

• How do you handle regime changes such as T+1 transitions?

Implementation Challenges

Several barriers complicate AI adoption in clearing and settlement.

Data quality across counterparties. Prediction models are only as good as their inputs. Settlement involves multiple parties, each with their own data systems, formats, and quality standards. A model trained on one CSD's data may not transfer well to another jurisdiction or asset class.

Legacy system integration. Market infrastructure operates on systems built over decades, often with mainframe cores and complex integration layers. Deploying AI models that can access real-time data and trigger downstream actions requires significant architectural investment.

Risk-averse culture. Operations professionals in clearing and settlement are trained to be cautious. Settlement finality is a legal concept with real consequences. Introducing AI that might occasionally produce incorrect recommendations creates cultural friction, even if overall accuracy exceeds human performance.

Governance for critical infrastructure. CCPs and CSDs operate under regulatory frameworks that require clear accountability. Model risk management requirements, including those in US Federal Reserve SR 11-7 guidance, extend to AI systems. Explainability, validation, and ongoing monitoring are prerequisites, not afterthoughts.

Getting Started

For operations leaders considering AI investment in clearing and settlement, several diagnostic questions can clarify priorities.

Where are settlement fails concentrated? If 80% of fails come from 20% of counterparties or securities, prediction models can be trained on high-impact segments first.

What is your current false positive rate? If existing rules-based systems generate thousands of exceptions with low true positive rates, AI-based classification can cut the noise. According to State Street disclosures, its Alpha AI Data Quality capability cut exceptions from 31,000 to 4,000 while catching 100% of genuine issues in custody operations. Similar gains may be possible in settlement, though we have not verified this claim.

Do you control your operations? Third-party outsourcing arrangements create friction for AI deployment. You cannot easily change processes you do not own. State Street's stated decision to insource operations previously outsourced reflects this logic.

What is your governance framework? AI systems require validation, monitoring, and clear escalation protocols before production deployment. Building this infrastructure is a prerequisite, not an afterthought.

Where are you on the autonomy spectrum? Most organisations are at Level 0-1. The path to Level 2 requires investment in both technology and change management. Start by mapping your current state.

Applying the 5C Adoption Friction Framework

AI adoption friction in clearing and settlement typically stalls on one of five points. Identifying which one is blocking your initiative determines the intervention.

Clarity: Do operations teams understand what the AI will do? BIS Working Paper 1194 identifies lack of explainability as a systemic risk in AI deployments. If model governance cannot explain the prediction logic, operations teams will treat outputs as a black box. The friction is not resistance; it is reasonable uncertainty.

Capability: Do teams have the skills to act on AI outputs? DTCC's reported deployment of over 300 AI courses is consistent with capability building being a prerequisite, not an afterthought. Prediction tools are useless if the team receiving the output cannot investigate, validate, or act on it.

Credibility: Do teams trust the model? Neither Clearstream nor Euroclear has disclosed accuracy metrics for their settlement prediction tools. Without false positive rates or precision data, operations teams have no data-based reason to trust. Years of rules-based systems generating thousands of false exceptions have created justified scepticism.

Control: Do teams feel agency over the change? State Street's stated decision to insource operations reflects a harder truth: you cannot deploy AI on processes you do not control. Third-party arrangements, rigid vendor contracts, or centralised AI teams that bypass operations leadership all reduce perceived control and increase friction.

Consequences: Do teams see personal benefit? No market infrastructure operator has disclosed ROI or headcount impact from AI deployment. If leadership cannot explain what success looks like for the operations team (not just the P&L), adoption stalls. The question "what happens to my role?" remains unanswered.

Most AI initiatives are not stuck because of technology. They are stuck on one of these five friction points. Diagnosing which one is the first step toward movement.

What's Next

The EU T+1 transition date of 11 October 2027 provides a hard deadline. As of January 2026, European market participants have roughly 21 months to achieve operational efficiency comparable to what US firms have been building since May 2024. This is limited time to design, build, validate, and deploy sophisticated AI systems from scratch.

The open questions concern regulatory stance and competitive dynamics. Will ESMA provide guidance on AI governance for CSDs? Will CCPs begin disclosing AI-driven risk management capabilities as a differentiator? Will infrastructure operators share prediction data across jurisdictions to improve model performance? And will the compressed timelines of T+1 create sufficient pressure to push organisations from Level 1 to Level 2 autonomy?

The infrastructure exists to make clearing and settlement more intelligent. What remains to be built is the operational confidence to use it, and the governance frameworks that regulators and boards require.

Three questions for your organisation: What is your current settlement fail rate, and what would a 50% reduction be worth in penalty savings and operational cost? Do you have the governance framework to deploy AI in production settlement operations? And is your operations team ready to supervise AI agents, or are they still expecting to be the first line of defence?

I write about AI adoption at brennanmcdonald.com. If you found this useful, subscribe to my newsletter for weekly insights on AI transformational change.

Sources

Primary Sources

SIFMA, ICI, and DTCC, "T+1 After Action Report," 12 September 2024

DTCC, "2024 Annual Report," 2024

European Commission, "A Shorter Settlement Cycle," Directorate-General for Financial Stability, Financial Services and Capital Markets Union, 3 July 2025

Council of the European Union, "Securities Trading: Council and Parliament Agree on Shorter Settlement Cycle," press release, 18 June 2025.

ESMA, "Final Report: Technical Advice on the CSDR Penalty Mechanism," ESMA74-2119945925-2059, November 2024.

Bank for International Settlements, "Intelligent Financial System: How AI is Transforming Finance," Working Paper No. 1194, June 2024.

Corporate Disclosures (Not Independently Verified)

CME Group, "Form 10-K Annual Report," filed February 2025. Key data: SPAN 2 implementation timeline

Clearstream, "Clearstream Enhances its Settlement Prediction Tool," press release, July 2025.

Euroclear, "Euroclear Teams with Meritsoft and Taskize to Launch Next Generation AI Service," press release, June 2025.

Euroclear, "Euroclear and Microsoft Announce Seven-Year Partnership," press release, Q1 2025.

Amazon Web Services, "London Stock Exchange Group Uses Amazon Q Business to Enhance Post-Trade Client Services," case study, January 2025.

State Street, company disclosure on Alpha AI Data Quality, 2025.

Mostapha Tahiri (EVP and COO, State Street), remarks at Fortune COO Summit, June 2025.

Glossary

Domain Terms

Affirmation: The process by which counterparties confirm the details of a trade, typically required before settlement can proceed.

Central Counterparty (CCP): An entity that interposes itself between counterparties to trades, becoming the buyer to every seller and the seller to every buyer.

Central Securities Depository (CSD): An institution that holds securities in dematerialised form and processes the transfer of ownership.

Clearing: The process of establishing what is owed between counterparties after a trade executes, including calculating net obligations.

CSDR (Central Securities Depositories Regulation): EU regulation governing CSDs, including the settlement discipline regime that imposes cash penalties for settlement fails.

Delivery versus Payment (DVP): A settlement mechanism ensuring securities transfer occurs if and only if the corresponding payment occurs.

Settlement Fail: A settlement instruction that does not complete on the intended settlement date. May be measured at instruction level or grouped at trade level.

Netting: The process of offsetting buy and sell obligations to reduce the number and value of movements required for settlement.

Novation: The process by which a CCP replaces the original trade, becoming the counterparty to both buyer and seller.

Settlement: The exchange of securities for cash that completes a transaction.

T+1: A settlement cycle where transactions settle one business day after the trade date.

Agentic AI Terms

Agentic AI: AI systems that can reason, plan, use tools, and take actions with minimal human oversight. Unlike assistive AI that provides recommendations, agentic AI can complete multi-step tasks within defined guardrails.

AI Agent: A software system that uses AI to perceive its environment, make decisions, and take actions to achieve goals.

Autonomy Level: The degree of independence an AI system has to make decisions and take actions, ranging from Level 0 (fully human-controlled) to Level 4 (fully autonomous).

Guardrails: Constraints and boundaries that limit what an AI agent can do, such as transaction value limits or mandatory escalation triggers.

Human-in-the-Loop: An AI design pattern where humans review and approve AI decisions before execution.

Tool Use: The ability of an AI agent to interact with external systems, APIs, and data sources to complete tasks.

Article prepared January 2026. All figures as at most recent available disclosure unless otherwise noted. Illustrative calculations are hypothetical and should not be relied upon for business decisions. Deployments described are based on company disclosures and have not been independently verified.

This is the second instalment in our new series for Q1 2026. The first article, “The Complete 2026 Guide to AI in Global Custody”, achieved some of our highest email engagement ever. If implementing AI is part of your KPIs this year - share this email with a colleague now so they’re on the same page. We only grow through word of mouth and referrals.

Table of Contents

• The Operating Environment

• The Same Problems, Different Assets

• The Autonomy Spectrum

  • The Critical Boundary

• Three Layers, Three Maturity Levels

  • Compliance: Mature and Proven (L1-L2)

  • Security: Real but Uneven (L1-L2)

  • Operations: Largest Opportunity, Earliest Stage

  • The Agentic Trajectory

• Implementation Realities

• What We Know and What We Do Not

• Where to Start

• Governance as Competitive Advantage

• Sources

• Key Terms

AI in digital asset custody belongs in the layers surrounding cryptographic signing, not in the signing itself. Compliance investigation, security monitoring, and operational automation can all benefit from machine learning. Transaction authorisation cannot. The irreversibility of blockchain transactions makes this boundary non-negotiable: a compliance false positive wastes analyst hours, but a signing false positive loses client assets permanently.

This distinction matters because the major security incidents in digital asset custody share a pattern. In February 2025, approximately $1.5 billion was lost from Bybit when attackers compromised a developer environment to manipulate signing keys. In March 2022, approximately $625 million was lost from the Ronin Bridge when attackers used social engineering to compromise validator private keys. In both cases, the cryptographic primitives were not broken. The failures occurred in key management and the human processes surrounding transaction authorisation.

Institutional adoption is accelerating despite these risks. Coinbase holds approximately $300 billion in digital assets as of Q3 2025, including custody for eight of eleven spot Bitcoin ETFs. BitGo secures $90.3 billion. Fireblocks has processed over $10 trillion in cumulative volume across 2,300 institutional clients. These institutions are not holding keys themselves. They are hiring custodians, accepting the intermediary in exchange for segregated accounts, audit trails, and regulatory compliance.

If your compliance team is still manually triaging the majority of transaction alerts, your cost base and response times are now structurally uncompetitive. Blockchain analytics providers have offered AI-enhanced monitoring for several years and the capability has matured through multiple product cycles. Your crypto-native competitors are using it.

The Operating Environment

Digital asset custody means controlling the cryptographic private keys that authorise blockchain transactions. Lose the key, lose the asset. Compromise the key, lose the asset. There is no custodian of last resort and no settlement system to reverse errors. This constraint shapes every decision about where AI can and cannot operate.

The core infrastructure reflects this reality. Multi-party computation distributes key shares so no single entity possesses the complete private key. Hardware security modules store keys in tamper-resistant hardware. Policy engines govern what can be signed and under what conditions: transaction limits, whitelist enforcement, multi-signature requirements, time-based controls. These systems are deterministic and rule-based. They should stay that way.

The market has split between crypto-native custodians and traditional players entering the space. Coinbase, BitGo, Fireblocks, and Anchorage built technology stacks purpose-built for digital assets. BNY launched digital asset custody in October 2022 and now administers BlackRock's BUIDL tokenised fund. State Street partnered with Taurus. Northern Trust co-founded Zodia Custody with Standard Chartered. Citi targets 2026 for its platform launch.

Both groups face the same operational challenges. Readers from securities services will recognise these workflows; the difference is irreversibility.

The Same Problems, Different Assets

The workflows in digital asset custody will look familiar to anyone who has run traditional securities operations.

Consider corporate actions. In traditional custody, an announcement arrives from a German issuer overnight. It is written in German, references local securities law, and requires translation, validation against multiple data sources, client notification, and election processing within 24 hours. The operations team races against a deadline to interpret ambiguous information and execute correctly.

In digital custody, a fork announcement arrives on X (Twitter), Github, and a Medium post. The operations team must evaluate technical stability, assess market capitalisation and liquidity, determine whether to support the new chain, communicate the decision to clients, and execute. The timeline is often shorter. The information sources are less structured. But the workflow is the same: interpret ambiguous external information, make a decision, execute accurately, document everything.

Major custodians publish formal fork and airdrop policies with evaluation criteria typically covering technical stability, market capitalisation thresholds, liquidity, and operational cost. The SEC's December 2025 broker-dealer custody guidance addresses policies for forks and airdrops, formalising what was already operational necessity.

Staking income requires calculation and allocation across client accounts with different fee structures and tax treatments. This parallels income collection and allocation in securities custody. Compliance investigation means triaging transaction alerts and filing suspicious activity reports, the same AML and sanctions screening that traditional custody teams know well.

The tools developed for exception handling in traditional custody offer useful analogues. State Street's Alpha AI Data Quality reduced exception counts from 31,000 to 4,000 over six months in securities operations while catching 100% of genuine issues. SmartStream's Smart Agents use domain-trained AI to triage breaks and learn from human analysts. The problem structure is similar across asset classes: high volume, mostly noise, occasional genuine issues requiring human judgment. These are not proof that identical tools will work for digital assets, but they demonstrate that the approach transfers when the underlying workflow matches.

The Autonomy Spectrum

AI deployments in custody operations exist along a spectrum of autonomy. Understanding where each use case sits on this spectrum, and where it should sit, is essential for governance and risk management.

L0 (Manual): Humans perform all tasks. AI plays no role. This is where transaction signing must remain. The cost of error is total and irreversible.

L1 (Assisted): AI provides information and suggestions. Humans make all decisions and execute all actions. Example: AI flags a transaction as potentially suspicious; analyst investigates and decides whether to file a report.

L2 (Augmented): AI drafts outputs and proposes actions. Humans review, approve, and authorise execution. Example: AI pre-populates a suspicious activity report with transaction details and risk rationale; analyst reviews, edits if necessary, and submits.

L3 (Supervised): AI decides and executes within defined guardrails. Humans monitor outcomes and handle exceptions. Example: AI automatically rebalances hot wallet funding when balances fall below threshold; operations team reviews daily summaries and investigates anomalies.

L4 (Autonomous): AI operates independently. Humans intervene only for exceptions or policy changes. No custody function should operate at this level given current technology and regulatory expectations.

Most AI deployments in traditional custody operate at L1 or L2. BNY's digital employees, which propose actions for human approval, operate at L2. State Street's Alpha AI Data Quality, which flags exceptions for human review, operates at L1-L2. The same ceilings apply to digital asset custody.

The Critical Boundary

The governance question for each use case is: what autonomy level is appropriate given the consequences of error? But in digital asset custody, there is a harder constraint that overrides this analysis.

Any function that can directly cause an on-chain state change must have a human authorisation gate. This means the signing decision, the moment when a transaction is approved for broadcast to the blockchain, must remain at L0. No AI system should have discretion over whether a transaction is signed.

This does not prevent AI from operating at higher autonomy levels for functions that do not control signing. The distinction is between authorisation and orchestration. Authorisation, deciding whether to sign, stays at L0. Orchestration, preparing transactions, optimising parameters, managing workflows, can reach L2 or L3 if the signing step itself remains human-controlled or governed by deterministic policy without machine learning discretion.

Consider hot wallet rebalancing. An AI system at L3 could monitor balances, determine that a transfer is needed, calculate the optimal amount and timing, and queue the transaction. But the transaction itself would either require human approval before signing or execute through a deterministic policy engine that permits only pre-approved transfer types within pre-approved limits. The AI orchestrates. It does not authorise. If your architecture gives an AI system the ability to cause a signed transaction to be broadcast based on its own judgment, you have exceeded the L2 ceiling for on-chain actions, regardless of what you call the function.

Three Layers, Three Maturity Levels

The three layers where AI adds value are at different stages of maturity and appropriate for different autonomy levels.

Compliance: Mature and Proven (L1-L2)

Compliance investigation is the most mature use case, and the one where you should start if you have not already. The problem is identical to traditional custody: high volumes of alerts, most of them false positives, requiring human investigation and potential regulatory reporting.

At L1, AI flags suspicious transactions and analysts investigate each one. At L2, AI pre-populates case files with transaction graphs, entity information, and draft narratives; analysts review and approve. The shift from L1 to L2 is where the efficiency gains compound. Building a case file from scratch takes hours. Reviewing and editing a pre-populated file takes minutes.

The market has validated solutions. Independent research using seized server data as ground truth has found leading blockchain analytics providers achieving false positive rates below 1% with high coverage of known illicit addresses. Chainalysis data has been admitted in US federal court proceedings. TRM Labs and Elliptic offer competing products with different strengths: TRM has strong US government relationships; Elliptic has deeper DeFi protocol coverage and stronger presence in European markets.

Before selecting a provider, answer three questions. What is your current false positive rate? What reduction would justify the investment? Which provider performs best against your specific transaction profile? If you cannot answer the first question, you do not have the baseline to evaluate AI tools. Instrument your current process before you buy.

Security: Real but Uneven (L1-L2)

Security monitoring is less mature than compliance but accelerating. The tools exist. Custodian adoption and outcomes remain opaque.

At L1, AI detects anomalies and alerts the security team. At L2, AI provides threat context and recommended response actions; humans investigate and execute. Pre-signing simulation occupies a specific place in this framework: simulation as an advisory control, where AI analyses a transaction and presents findings for human review, operates at L1. Automatic blocking, where AI can prevent a transaction from proceeding without human intervention, would operate at L3 and requires careful governance given the potential for both security benefit and operational disruption.

Hexagate, now part of Chainalysis, provides pre-signing simulation across dozens of blockchain networks. Zodia Custody has partnered with CUBE3.AI for real-time threat intelligence. Coinbase has disclosed that AI supports almost every aspect of its security operations, though specifics on custody operations are not public.

The threat environment is intensifying. Chainalysis estimated $3.4 billion stolen in 2025, up 55% from 2024, with North Korea's Lazarus Group accounting for $2.02 billion. The attack pattern is increasingly supply chain compromise and social engineering rather than cryptographic attacks. AI can help with anomaly detection and behavioural monitoring, but independently validated detection rates for these attack vectors are not publicly available.

Security AI is a defensive spend. You are not reducing analyst hours; you are reducing the probability of catastrophic loss. That makes business cases harder to build but does not make the investment less important.

Operations: Largest Opportunity, Earliest Stage

Operational automation has the clearest business case but the least mature tooling.

Current wallet management is rule-based and operates at what might be called L0.5: automated but not intelligent. Fireblocks' Gas Station automatically replenishes native tokens when balances fall below threshold. Round-robin algorithms balance withdrawal requests across hot wallets. These are deterministic systems executing predefined logic, not AI systems learning from data.

The opportunity is in staking, and the economics can be substantial. Staking yields vary significantly by network, validator performance, and market conditions, but typically range from 3% to 5% annually for major proof-of-stake networks. For institutional custodians, staking represents a meaningful revenue line beyond safekeeping fees, with economics depending on stakeable asset mix, client mandates, and competitive fee structures.

AI applications are developing across the staking value chain. Validator selection could operate at L2: AI analyses uptime history, fee structures, and slashing incidents to recommend validators; humans approve the selection. Reward calculation and allocation could approach L3: AI automatically reconciles rewards across client accounts; humans review exception reports. Slashing risk prediction operates at L1: AI identifies validators showing early warning signs; humans decide whether to unstake.

The regulatory path is becoming clearer. The SEC clarified in May 2025 that protocol staking is administrative rather than a securities transaction. The IRS established a safe harbour for ETP trusts. But no custodian has disclosed AI-driven staking optimisation in production. The tools are being built. Deployment is lagging.

The Agentic Trajectory

The industry is moving toward agentic AI: systems that can execute multi-step workflows with minimal human oversight. In traditional custody, BNY describes its AI systems as digital employees with defined roles, managers, and escalation protocols. These systems operate at L2-L3: they can complete tasks autonomously within guardrails, escalating to humans when they encounter situations outside their training.

In digital asset custody, agentic capabilities will likely emerge first in operational functions where errors are recoverable and, critically, where the agent orchestrates but does not authorise. An agent that monitors gas prices, predicts optimal transaction timing, and queues non-urgent transactions for batch processing could operate at L3, provided the actual signing remains human-controlled or governed by deterministic policy. An agent that manages hot wallet rebalancing across multiple chains, preparing transfers within predefined limits, could operate at L3 under the same constraint.

Compliance and security will be slower to reach L3. The consequences of missed threats or incorrect filings create regulatory and reputational risk that most institutions will want humans to own. And signing will remain at L0 indefinitely. An agentic system that could approve transactions autonomously would represent an unacceptable concentration of risk, regardless of how sophisticated its guardrails.

The path from current state to supervised autonomy requires investment in three areas: data infrastructure to give agents the information they need, policy frameworks to define guardrails and escalation triggers, and monitoring systems to detect when agents are operating outside expected parameters. Custodians building these capabilities now will be positioned to deploy agentic systems as the technology matures.

Implementation Realities

Multi-chain complexity makes AI deployment harder than in traditional custody. BlackRock's BUIDL fund operates across multiple blockchains. Coinbase supports hundreds of cryptocurrencies. Each chain has unique consensus mechanisms, transaction formats, and fee structures. AI systems must either generalise across chains or be trained separately for each. Neither approach is straightforward.

Regulatory fragmentation adds difficulty. The GENIUS Act established a US federal stablecoin framework in July 2025. The OCC has taken actions enabling digital asset firms to operate with national bank status. MiCA reached full application in the EU. Cold storage requirements vary by jurisdiction, typically ranging from 90% to 98% of client assets. The EU AI Act becomes effective in August 2026, adding governance requirements for AI systems. Custodians operating globally must build for multiple regimes.

Disclosure asymmetry is the core problem for competitive benchmarking. Vendors publish capabilities. Custodians do not publish what they deploy or what outcomes they achieve. BNY's Eliza platform supports over 100 AI solutions across the firm, but none are specified as digital-asset-focused. State Street's Alpha AI reduces false positives in traditional custody; equivalent capabilities for digital assets have not been disclosed.

Talent scarcity constrains execution. The intersection of blockchain expertise, AI capability, and institutional custody knowledge is small. Traditional custody professionals lack crypto-native experience. Crypto-native professionals often lack governance and audit rigour. AI engineers lack domain knowledge in either. Building teams that bridge all three takes time.

What We Know and What We Do Not

Chainalysis attribution accuracy has been tested in court proceedings. Regulatory frameworks for custody and staking are codified. Core signing infrastructure remains deterministic and should stay that way.

We do not know custodian-specific false positive rates for transaction monitoring. We do not know the ratio of automated to human decisions in alert triage. We do not know assets under custody for several major players, making market share analysis unreliable. We do not know what AI, if any, traditional custodians are deploying specifically for their digital asset operations.

Several vendor claims are self-reported without independent verification. Detection rates, time savings, and efficiency improvements cited in marketing materials should be evaluated against your own baseline rather than accepted at face value.

The agentic AI trajectory is early. No custodian has disclosed agentic systems operating at L3 for any digital asset function. The realistic near-term ceiling is L2: AI proposes, humans approve.

Where to Start

Sequence matters.

Start with compliance. The tools are mature, the ROI is measurable, and implementation risk is low. Instrument your current false positive rate. Evaluate providers against your transaction profile. Deploy, measure the reduction, and use the results to build credibility for subsequent investments. Target L2: AI pre-populates case files, analysts review and approve.

Move to staking operations second. This is where the revenue upside is largest. The regulatory framework is becoming clearer. The complexity of validator selection, reward calculation, and cross-protocol comparison justifies automation. Expect build-heavy integration even if you buy components, since production-ready AI tools for staking optimisation are not yet widely available. Target L2 for validator selection, with L3 possible for reward calculation once you have confidence in the system.

Treat security as continuous investment. The threat environment is worsening. Pre-signing simulation and behavioural monitoring should be part of your architecture even though they will not produce the same measurable ROI as compliance triage. Target L1-L2: AI detects and recommends, humans investigate and respond.

Do not touch signing. The boundary between AI-assisted operations and transaction authorisation must be documented, audited, and enforced. AI can orchestrate. It cannot authorise. If your architecture allows AI systems to cause a signed transaction to be broadcast based on machine learning judgment, you have created a vulnerability that no efficiency gain can justify.

Governance as Competitive Advantage

The pattern from Bybit, Ronin, and other major incidents is consistent: cryptographic primitives work, human processes fail. AI can strengthen those human processes. It can triage alerts, detect anomalies, optimise validator selection, and automate documentation. It cannot be allowed discretion over transaction signing.

The firms that deploy AI in the right layers, with documented autonomy limits and clear escalation protocols, will reduce costs, improve accuracy, and capture the staking revenue opportunity. Those that move too slowly will lose ground to competitors who have already automated compliance operations. Those that move too fast, giving AI systems any role in signing decisions, will discover that blockchain errors do not have a remediation process.

The autonomy spectrum provides the governance framework. For each function, define the appropriate level. Document the rationale. Build the monitoring to detect drift. Enforce the boundary between orchestration and authorisation. The custodians that get this right will not just be more efficient. They will be more trustworthy. In a market where institutional adoption depends on trust, that is the competitive advantage that matters.

About the Author

Brennan McDonald writes about AI transformation and change at brennanmcdonald.com and publishes the Global Custody Pro newsletter.

Advisory

The AI Change Leadership Intensive helps custody and operations leaders diagnose adoption constraints and accelerate operating model change. The programme uses the 5C Adoption Friction Model to identify specific intervention points and build momentum with resistant stakeholder groups.

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Sources

Sources range from regulatory filings and company disclosures to vendor case studies and market research. Self-reported performance metrics should be validated against your own operational baseline.

Regulatory Documents

SEC Division of Corporation Finance, Statement on Protocol Staking (May 2025)

GENIUS Act, Public Law (July 2025)

OCC Interpretive Letters and Charter Actions (2025)

EU Markets in Crypto-Assets Regulation (MiCA)

SEC Broker-Dealer Custody Guidance (December 2025)

Company Disclosures

Coinbase Global Inc. Q3 2025 Earnings Release

BitGo Platform Documentation (2025)

Fireblocks Platform Documentation (2025)

BNY Digital Asset Platform Announcements (2022-2025)

State Street/Taurus Partnership Announcement (2024)

Research and Analysis

Chainalysis Crypto Crime Report (2025)

EY-Parthenon Institutional Investor Survey (January 2025)

Key Terms

Agentic AI: AI systems that can execute multi-step workflows with minimal human oversight, making decisions and taking actions within defined guardrails. Distinguished from traditional AI by the capacity to act, not just analyse or generate.

AI Agent: A software system that uses AI to perceive its environment, make decisions, and take actions to achieve goals. In custody, agents might resolve exceptions, manage wallet balances, or optimise staking, escalating to humans for decisions outside their authority.

Human-in-the-Loop: A design pattern where humans review and approve AI decisions before execution. Standard in L1-L2 deployments. At L3, human involvement shifts to exception handling and monitoring.

Guardrails: Constraints that limit what an AI agent can do. In custody, guardrails include transaction value limits, approved counterparty lists, permitted asset types, and mandatory escalation triggers.

Tool Use: The ability of an AI agent to interact with external systems, APIs, and data sources. Enables agents to query balances, check transaction status, access market data, and execute operations as part of completing tasks.

Autonomy Level (L0-L4): Framework for classifying AI deployment maturity. L0: human only. L1: AI assists. L2: AI proposes, human approves. L3: AI acts within guardrails, human monitors. L4: fully autonomous. In digital asset custody, any function that can cause an on-chain state change must have a human authorisation gate, limiting such functions to L2 or below.

Article prepared January 2026. All figures as at Q3 2025 unless otherwise noted.

The five largest global custodians now safeguard nearly $198 trillion in assets. This figure grows by roughly 10% annually. Yet while straight-through processing (STP) rates are high, exception handling remains labour-intensive. Reconciliation teams work through breaks one by one. Corporate actions specialists decipher announcements in dozens of languages. Settlement operations race against ever-tightening deadlines.

AI is being positioned by custodians and vendors alike as the next operational step-change, with early production deployments and selectively disclosed results. BNY reports 117 AI solutions in production. State Street has operationalised its Alpha AI Data Quality platform. Northern Trust states it has reduced certain custody tax operations from eight hours to thirty minutes.

Unless otherwise noted, the performance figures cited below are self-reported in public materials and should be treated as indicative rather than independently verified. Firms define "use case", "solution", and "production" differently, so figures are directionally informative rather than strictly comparable. Most disclosed metrics appear to reflect specific workflows rather than end-to-end custody operations.

The Scale of Global Custody

Global custody involves the safekeeping of assets and processing of cross-border securities trades for institutional clients. Core functions include settlement, corporate actions processing, income collection, and reporting. The industry is dominated by a handful of global players, each managing tens of trillions in assets under custody and administration (AuC/A).

As at September 2025, the top five US custodians by AuC/A are: BNY ($57.8 trillion, up 11% year over year), State Street ($51.7 trillion, up 10%), JPMorgan ($40.1 trillion, up 12%), Citi (approximately $30 trillion, up 13%), and Northern Trust ($18.2 trillion, up 9%). Combined, these five institutions custody nearly $198 trillion in assets, making them critical infrastructure for global capital markets.

Why AI Matters Now

Three structural forces have made AI adoption imperative for custody operations. The first is accelerated settlement cycles. The United States transitioned to T+1 settlement (where trades settle one business day after execution) on 28 May 2024. According to the SIFMA/ICI/DTCC After Action Report, affirmation rates improved from 73% to 95%, but the compressed timeline leaves less room for manual exception processing. The European Union is expected to follow with its own T+1 transition in 2027, while CSDR (Central Securities Depositories Regulation) settlement discipline regimes already impose cash penalties for fails.

The second force is persistent fee pressure. Custody fees have compressed for decades. Institutional investors have become sophisticated buyers, expecting efficiency gains to translate into lower costs or enhanced service levels. The third is regulatory complexity. ISO 20022 messaging migration requires translation and validation capabilities. The EU AI Act mandates governance frameworks from August 2026. In the United States, Federal Reserve SR Letter 11-7 extends model risk management requirements to AI systems. Custodians must deploy AI to manage complexity while ensuring their AI systems meet emerging regulatory standards.

How Leading Custodians Are Approaching AI

The major custodians are taking distinct approaches to AI deployment, shaped by their existing technology stacks, operational philosophies, and disclosure strategies. Two firms stand out for the depth of their public commitments.

BNY: The Eliza Platform

BNY has emerged as perhaps the most aggressive AI adopter among major custodians. The firm's Eliza platform, a proprietary AI environment, now supports over 125 live use cases with 117 AI solutions in production as at Q3 2025, though the firm has not disclosed scope or scale of individual deployments. The approach emphasises democratisation: 98% of BNY's employees have received generative AI training, and BNY states that up to 20,000 employees are building or experimenting with agents. BNY has been notably forthcoming about its AI metrics; peers have disclosed less, making direct comparison difficult.

Crucially, BNY has begun disclosing quantified outcomes. According to Leigh-Ann Russell, Chief Information Officer and Global Head of Engineering, the firm reports a 40% reduction in false positives during NAV (net asset value) calculation. Legal contract review time has reportedly decreased by 25%. A digital employee handling payment repairs is reported to process approximately 2,000 repairs per week, though scope and baseline were not disclosed. In code security, roughly 90 digital engineers contribute tens of thousands of lines of code, autonomously fixing simple issues while escalating complex ones to human supervisors.

The Eliza platform is designed to be model-agnostic, allowing BNY to swap in different underlying models as AI capabilities evolve. In December 2025, BNY announced the integration of Google Cloud's Gemini Enterprise to advance agentic research capabilities for market analysis. The firm has also established a collaboration with Carnegie Mellon University to create the BNY AI Lab for research and development.

State Street: Alpha and Operational Control

State Street's AI strategy centres on its Alpha platform, which combines custody, accounting, and middle-office services with data analytics capabilities. As at Q2 2025, the firm reported $380 billion in Alpha platform AuC/A wins, with 28 clients live on the platform. The broader Charles River ecosystem, which provides front-office investment management technology, manages over $58 trillion in assets.

A distinctive element of State Street's approach is the Alpha AI Data Quality (AADQ) capability. According to company disclosures, traditional rules-based exception processing flagged over 31,000 exceptions over a six-month period, of which only 250 were true positives. The AI-enabled system identified just 4,000 exceptions while reportedly capturing all genuine issues identified by their evaluation process, dramatically reducing the false positive burden on operations teams.

Perhaps more significantly, State Street has articulated a clear operational prerequisite for AI deployment. In 2023, the firm made the counterintuitive decision to insource operations previously outsourced to vendors in India. Mostapha Tahiri, Executive Vice President and Chief Operating Officer, explained the rationale at the Fortune COO Summit in June 2025:

Tahiri's answer: operational control is a prerequisite for change. Third-party arrangements create friction due to compliance complexity and cultural misalignment. Custodians cannot effectively change operations they do not directly control.

The remaining major custodians have disclosed less about their AI initiatives. JPMorgan Securities Services ($40.1 trillion AuC) won Best Data Analytics Provider in the 2025 Waters Rankings, suggesting meaningful integration. Citi (~$30 trillion) has announced a digital asset custody platform targeting 2026. Northern Trust has deployed AI within its Digital Partner Platform. All major players are investing; what remains opaque is the depth and outcomes of those investments.

Core AI Use Cases in Custody

These firm-level strategies are deployed across core operational functions, where AI is achieving its most measurable impact.

Reconciliation

Reconciliation (matching internal records against external sources) generates enormous exception volumes in custody operations. This is where AI has achieved the most mature deployments. Solutions use supervised machine learning for pattern recognition and observational learning algorithms that adapt to historical matching decisions. SmartStream claims auto-match rates exceeding 97% and exception reductions of 67%, with processing that previously took weeks now completing in seconds. BNY has integrated reconciliation automation into Eliza as a core use case. The technology is proven; the challenge now is scaling across asset classes and counterparties. Vendor case study results may not generalise across asset classes or data quality regimes.

Corporate Actions Processing

Corporate actions remain one of the most challenging areas of custody operations. Announcements arrive in dozens of languages, via multiple channels, with varying levels of structure. Natural language processing and optical character recognition enable automated extraction and classification. According to industry analysis presented at SIBOS 2024, corporate actions data costs firms $3 to $5 million annually, with 75% of firms manually revalidating data. BNP Paribas Securities Services processes approximately 30,000 messages monthly through its ML translation tool, while a Chainlink, Swift, and Euroclear initiative is applying AI to structured corporate actions data delivery.

Settlement Prediction

Predictive analytics for settlement failure represents a natural AI application in a T+1 environment. Clearstream claims 80% accuracy in predicting settlement failures with up to four days advance warning through its Settlement Prediction Tool, launched in July 2025. Accenture research suggests machine learning techniques can achieve 83% to 97% prediction accuracy depending on methodology. These tools enable operations teams to prioritise interventions on at-risk instructions before deadlines lapse.

Client Service

Generative AI interfaces from BNY, State Street, and BNP Paribas are claimed by these banks to now handle some routine client inquiries, escalating more complex cases to humans. This seems very small in scope at the moment, given the enormous complexity involved when things go wrong in the global custody world, but over time as AI agents get better, we might see changes in how clients experience service from global custodians.

Agentic AI: Current State

The industry is moving beyond reactive chatbots toward agentic AI: systems that can execute multi-step tasks with minimal human oversight. According to the World Economic Forum and Cambridge Centre for Alternative Finance, agentic AI remains nascent in financial services. Vendor projections are optimistic, but such forecasts should be treated with caution given the high failure rates discussed below.

The industry's progress can be mapped against an autonomy spectrum: from Level 0 (no AI) through Level 1 (AI-assisted, human executes) and Level 2 (AI executes, human approves) to Level 3 (AI executes within guardrails) and Level 4 (full autonomy). Most disclosed custody deployments sit at Level 1 or Level 2. The "digital employee" model represents early Level 3 ambitions, with agents operating within defined boundaries and escalating exceptions to human supervisors.

BNY's approach to agentic AI centres on the concept of "digital employees": AI agents treated like staff members with identities, managers, specific personas, and defined roles. These agents operate within guardrails with escalation protocols. Sarthak Pattanaik, Chief Data and AI Officer, described the shift: "Now, instead of handling certain tasks in the first instance, the role of the human operator is to be the trainer or the nurturer of the digital employee."

No major custodian has publicly disclosed fully autonomous agentic systems in production custody operations. The current state is best characterised as supervised autonomy: agents that handle routine tasks independently while escalating complexity to humans.

Implementation Realities

For every successful AI deployment, there are many that fail to reach production. Understanding why is essential for practitioners.

The Failure Rate

Industry statistics are sobering. According to aggregated research, 42% of companies are now abandoning a majority of their AI initiatives, up from 17% in 2024. An average of 46% of generative AI proofs-of-concept are scrapped before reaching production. MIT's NANDA Initiative found that 95% of generative AI pilots fail to achieve revenue acceleration.

Banking-specific data is equally challenging. Among banks, 52% are piloting agentic AI but only 16% have fully deployed use cases. Gartner predicts more than 40% of agentic AI projects will be cancelled by 2027. The gap between experimentation and production remains vast.

Key Barriers

Infrastructure challenges dominate. According to NVIDIA, approximately 40% of financial services experts cite data quality as the main AI challenge. Legacy system integration compounds the problem: 68% of CTOs in an EY survey cited legacy systems as the most significant adoption obstacle, with initiatives experiencing 12 to 18 month delays due to compatibility challenges.

Change management may be the most underestimated barrier. McKinsey's State of AI 2025 report found that nearly two-thirds of organisations have not begun scaling AI across the enterprise. Only 38% of AI projects meet or exceed ROI expectations, according to Deloitte. BCG research indicates 74% of companies struggle to achieve and scale value from AI. Governance remains immature: a Smarsh survey found only 32% of financial services firms have formal AI governance programmes.

Beyond data and legacy systems, the expertise gap is acute. Custody operations require domain specialists who understand settlement, corporate actions, and regulatory requirements. AI implementation requires data engineers, ML specialists, and model risk professionals. Finding individuals or teams who bridge both worlds is exceptionally difficult. Most firms must either build this capability over years or source it externally.

The Investment Reality

The efficiency gains are real, but they follow (not precede) substantial investment. Meaningful AI deployment requires: talent (data engineers, ML operations, model validation, AI governance); data remediation (often the majority of project time); vendor and infrastructure costs (platforms, integration, monitoring); governance infrastructure (model inventories, risk tiering, audit trails); and change management (training, role redesign, cultural adaptation). Firms expecting AI to reduce costs in year one are misreading the investment profile.

The Timeline Question

Realistic timelines for meaningful AI deployment in custody operations run 18 to 36 months from initiation to scaled production. The first 6 to 12 months typically focus on data remediation, infrastructure, and governance framework design. Pilot results emerge in months 12 to 18. Scaled deployment, if it happens, follows in years two and three. Firms expecting quick wins are disproportionately represented among the 42% abandoning initiatives.

The Governance Reality

Effective AI governance in custody operations means: documented model inventories with defined risk tiers for each use case; baseline performance metrics established before deployment; ongoing drift monitoring to detect degradation; clear escalation protocols when AI outputs fall outside confidence thresholds; and audit trails sufficient for regulatory examination under SR 11-7 or the EU AI Act. Few firms have this infrastructure in place. Building it is often more work than building the AI itself.

Perhaps most concerning is the operational risk dimension. Research from the Federal Reserve Bank of Richmond found that "banks with higher AI intensity incur greater operational losses than their less AI-intensive counterparts." The mitigating factor: strong risk management. Global custody is a high-stakes environment where errors are not tolerable. A hallucination in a NAV calculation or a failed settlement instruction can trigger regulatory breaches and reputational damage that no efficiency gain can offset. According to an ACA Group survey, only 28% of financial services firms test or validate AI outputs.

Success Factors

MIT research identified patterns distinguishing successful AI implementations. Generic tools struggle because they do not adapt to specific workflows. More than half of generative AI budgets go to sales and marketing tools, yet the biggest ROI appears in back-office automation. Purchasing AI tools from specialised vendors succeeds about 67% of the time; internal builds succeed only one-third as often. Empowering line managers rather than central AI labs to drive adoption correlates with success.

Adoption friction typically falls into five categories: Clarity, Capability, Credibility, Control, and Consequences. Successful implementations address all five; failed ones typically stall on one or two undiagnosed friction points.

BNY's approach aligns with these findings. Russell emphasised "Learning Quotient": the ability to learn and adapt. The firm's focus on training 98% of staff on generative AI reflects an understanding that cultural adoption matters as much as technical deployment.

What We Still Don't Know

What remains unclear from public disclosures:

• How "production" is defined and what percentage of operational volume is covered

• Baselines, test design, and methodology behind reported efficiency metrics

• Independent validation or assurance of claimed outcomes

• Operational risk and error rates post-automation over time

When a custodian cites an efficiency gain, the operational question is: what is the exception rate after automation, and what is the human escalation load?

Reflections for Global Custody Leaders

Before committing budget to AI initiatives, custody leaders should be able to answer these questions clearly. Uncertainty on several suggests the initiative is not yet ready for production investment.

Operational Control: Do you directly control the operations you intend to transform, or are they managed by third-party vendors? If outsourced, what contractual and practical barriers exist?

Data Readiness: Is your data clean, standardised, and accessible across the workflows you intend to automate? What percentage of project time should you allocate to data remediation?

Expertise and Talent: Do you have people who understand both custody operations and AI implementation? Where will model validation and governance expertise come from: internal build, external hire, or advisory partnership?

Governance Infrastructure: Do you have a model inventory, risk tiering framework, and escalation protocols sufficient for regulatory examination? How will you document AI-assisted decisions for audit?

Investment and Timeline: Have you budgeted for 18 to 36 months before expecting scaled efficiency gains? Does your business case account for talent, data, infrastructure, governance, and change management?

Change Readiness: Is your operations leadership prepared to shift from first-line processing to supervising AI agents? Have you identified which friction points (clarity, capability, credibility, control, consequences) are most likely to stall adoption?

Use Case Selection: Are you starting with high-volume, rules-based processes where AI performance is easiest to validate? Have you identified which use cases require human-in-the-loop review versus those suitable for higher autonomy?

What's Next: The Fee Expectations Question

A fundamental tension is emerging. If AI enables efficiency gains, what do institutional investors expect: fee reduction, service enhancement, or value migration to new premium services? The industry is positioning for the latter two. There is also the edge case problem: AI excels at automating the predictable 80% of volume, but custody operations are defined by the unpredictable 20%. If AI handles volume but humans still handle complexity, does the operating model actually change?

Conclusion

The implementation path remains challenging. High failure rates, data quality barriers, legacy system friction, and change management complexity separate successful deployments from abandoned initiatives. The Richmond Fed's finding that AI intensity correlates with operational losses absent strong risk management underscores the governance imperative.

For custody practitioners, the implications are clear. AI capability is becoming a competitive requirement. The firms that navigate these challenges successfully will define the next era of global custody. Those that do not will find themselves outpaced by competitors who invested earlier, and more realistically.

Your AI initiative is not stuck because of technology.

It is stuck because of the humans around it. The AI Change Leadership Intensive uses the 5C Adoption Friction Model to identify which friction point is actually in the way, and the specific moves that would shift your first resistant group to active adoption within 90 days.

Learn more about the AI Change Leadership Intensive

I write about AI adoption in financial services at brennanmcdonald.com. If you found this useful, subscribe to my newsletter for weekly insights on leading change in post-trade operations.

Sources

Note: Sources range from regulatory filings and earnings releases (highest credibility) to vendor case studies and analyst surveys (indicative but methodology-dependent).

Primary Sources

• BNY Q3 2025 Earnings Release (16 October 2025)

• State Street Q3 2025 10-Q Filing and Earnings Call (17 October 2025)

• JPMorgan Q3 2025 Earnings Presentation (14 October 2025)

• Citi Q3 2025 Earnings Press Release (14 October 2025)

• Northern Trust Press Release (9 December 2025)

• SIFMA/ICI/DTCC T+1 After Action Report (12 September 2024)

• ESMA Final Report on CSDR Penalty Mechanism (November 2024)

Executive Interviews

• Leigh-Ann Russell, CIO & Global Head of Engineering, BNY (AI in Finance with BNY: Advanced Insights S2E8, 2025)

• Mostapha Tahiri, EVP & COO, State Street (Fortune COO Summit, June 2025)

Research and Industry Reports

• McKinsey State of AI 2025 (November 2025)

• MIT NANDA Initiative (August 2025)

• Federal Reserve Bank of Richmond: AI and Operational Losses (October 2025)

• World Economic Forum / Cambridge Centre for Alternative Finance (December 2024)

• EY/MIT Technology Review: Agentic AI in Banking (2025)

• Deloitte Financial AI Adoption Report (2024)

• BCG AI Value Realisation Study (October 2024)

• NVIDIA State of AI in Financial Services (January 2024)

• EY Financial Services CTO Survey (2024)

• Smarsh AI Governance Survey (August 2024)

• ACA Group AI Validation Survey (2025)

Vendor and Technology Sources

• OpenAI Case Study: BNY Eliza Platform (2025)

• BNY/Google Cloud Joint Press Release (8 December 2025)

• Clearstream Settlement Prediction Tool Press Release (July 2025)

• SmartStream / WatersTechnology (2025)

• Chainlink / SIBOS 2024 Corporate Actions Initiative

Glossary

AuC/A (Assets under Custody and Administration): The total market value of assets held by a custodian on behalf of clients, including securities, cash, and other financial instruments.

STP (Straight-Through Processing): Automated processing of transactions from initiation to settlement without manual intervention. High STP rates indicate operational efficiency.

T+1 Settlement: A settlement cycle where securities transactions settle one business day after the trade date. The US adopted T+1 in May 2024; the EU is targeting 2027.

CSDR (Central Securities Depositories Regulation): EU regulation governing central securities depositories, including mandatory buy-ins and cash penalties for settlement failures.

NAV (Net Asset Value): The per-share value of a fund, calculated by dividing total assets minus liabilities by the number of shares outstanding. NAV calculations are a core custody function.

AADQ (Alpha AI Data Quality): State Street's AI-enabled exception processing capability within its Alpha platform.

Agentic AI: AI systems that can execute multi-step tasks with minimal human oversight, making decisions and taking actions autonomously within defined guardrails.

Human-in-the-loop: A deployment model where AI systems require human approval before executing actions, ensuring oversight of critical decisions.

Supervised autonomy: AI agents that handle routine tasks independently while escalating exceptions or complex scenarios to human operators.

Digital employee: BNY's framework for AI agents treated as staff members with identities, managers, and defined roles.

Guardrails: Constraints and boundaries programmed into AI systems to limit their actions to approved parameters.

Escalation protocol: Defined rules for when and how AI systems hand off decisions to human supervisors.

Article prepared January 2026. All figures as at Q3 2025 unless otherwise noted.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. This is the last Global Custody Pro edition for 2025. Thanks for your support this year. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• The thesis: this time it’s actually different

• Why previous automation failed

• Why this time is different

• The evidence from 2025

• What convergence means

• What would prove this wrong

• The timeline

• The strategic question

• Custody is becoming software-defined

The thesis: this time it’s actually different

Custody is converging. Not bit by bit, but structurally.

For decades, the industry has been organised around asset class distinctions. Equities settle differently from bonds. Funds need different handling from derivatives. Each asset class has its own teams, systems, and expertise. This fragmentation reflects genuine differences in how these assets behave.

That fragmentation is ending. Two forces are combining to collapse the operational distinctions between asset classes, and 2025 is the year this became visible.

Tokenisation is creating a common layer: different assets represented in common data structures, settling through common mechanics, held through common interfaces. Agentic AI will enable workflow convergence: the judgment calls and exception handling that keep humans in the loop will be handled as AI capability rises.

Neither force alone achieves this. Tokenisation without AI agents gives you a new layer but still requires humans for exceptions. AI agents without tokenisation give you smarter automation of fragmented processes. Together, they enable a structurally different operating model.

A note on timing: 2025 is not the year convergence happened. It is the year the pieces started fitting together. Tokenisation reached production in specific use cases. AI moved from experiment to approved deployment in narrow areas. The mechanism became visible. The full transformation is years away.

Why previous automation failed

The custody industry has heard transformation promises before. Each previous wave attacked one part of the problem. None solved the fundamental fragmentation.

Straight-through processing automated the happy path. A trade that matched cleanly could settle without human intervention. But the exception rate in custody is high. Every failed trade, every corporate action, every data mismatch required human judgment. STP captured the routine work. The exceptions still needed people.

Robotic process automation promised to capture what STP missed. RPA could handle repetitive tasks: extracting data, copying between systems, generating reports. The robots proved brittle. When formats changed or edge cases appeared, they broke. RPA cut headcount in specific functions but did not change the operating model.

Distributed ledger technology offered a better layer. Shared ledgers could eliminate reconciliation. Smart contracts could automate corporate actions. But technology alone does not drive adoption. Without network effects, a better layer remains a pilot.

Early AI tools have been deployed for specific tasks: document processing, anomaly detection, code assistance. Useful, but incremental. They make existing processes faster without changing what those processes are.

The pattern: each technology addressed one constraint while leaving others intact. Fragmentation survived because different asset classes genuinely required different handling.

Why this time is different

For convergence to happen, you need both layer convergence and workflow convergence occurring together.

Layer convergence is what tokenisation provides. When equities, bonds, and funds are all represented as tokens on programmable ledgers, they share common data structures, common settlement mechanics, and common custody interfaces. The operational differences between asset classes shrink. Not because a bond becomes an equity, but because the handling becomes the same.

Today, a custodian needs different teams and systems for each asset class. If those assets share a common layer, those distinctions become unnecessary overhead.

Workflow convergence is what agentic AI can provide. But we need to be honest about where the industry is today. Most custodians are using AI in narrow, approved use cases: document processing, code assistance, specific client service tasks. Broad deployment of AI agents handling exceptions and judgment calls across operations is not happening yet. The governance frameworks do not exist. The liability questions are unresolved. The technology is promising; the organisational readiness is not there.

What changed in 2025 is that the path became clearer. Agentic AI can reason about ambiguous situations, plan multi-step solutions, and execute with minimal oversight. Not perfectly. Not without supervision. But well enough that, once governance catches up or competitive pressure becomes too strong, the human role can shift from doing the work to reviewing what the agent proposes and deciding whether to approve it.

The combination is what matters. Tokenisation provides a uniform layer. Agentic AI provides the ability to manage that layer without asset-class-specific human expertise. Neither is fully deployed today. But both are now past the proof-of-concept stage.

The evidence from 2025

Several developments signal that convergence is becoming plausible.

Tokenised collateral reached production. Protocols now allow institutions to use tokenised money market funds as collateral without moving the assets. This is live infrastructure. If it works for money market funds, it can work for other asset classes. The layer is extensible.

AI proved itself in narrow use cases. Major custody banks deployed AI across specific functions. The results were positive enough to justify expansion. The path from "approved pilot" to "operational standard" is now visible, even if governance constraints slow the pace.

Regulatory frameworks adapted. US regulators confirmed that banks can intermediate crypto transactions. Pilots accept tokenised assets as margin collateral. Regulators are building frameworks for tokenised assets to function within the banking system.

Hybrid custody models emerged. Traditional banks and crypto-native firms started integrating rather than competing. The organisational distinction between "traditional" and "digital" is collapsing.

None of these alone proves convergence is happening. Together, they suggest the mechanism is becoming operational.

What convergence means

If this thesis plays out, several things follow.

Asset class expertise depreciates. The knowledge that makes someone a fixed income specialist or an alternatives administrator becomes less valuable. The operational distinctions those roles exist to manage are shrinking.

Scale economics change. Today, expanding into a new asset class requires building capability for that class. Expensive and slow. If the layer is common and AI handles workflows, expansion becomes configuration rather than construction.

The "digital assets desk" is just the beginning. The industry treats digital assets as a new asset class to support. That framing is too narrow. If tokenisation spreads, there is no separate digital function because eventually most assets are digital. The question becomes "how do we rebuild operations for a tokenised world."

This integration will not be smooth. The people who built digital assets businesses as separate empires have incentives to keep them separate. There will be turf wars. But clients do not want to pay for two operating models when one will do. The politics will slow things down. They will not stop them.

What would prove this wrong

Tokenisation adoption stalls. If tokenisation stays confined to niche asset classes, layer convergence does not happen. If progress is CSD, by CSD, that’s a very different scenario.

AI governance proves impossible. If governance constraints prevent deployment beyond narrow use cases, workflow convergence does not happen. Yet already, firms that are using AI tools want to use them more - not less.

Regulatory fragmentation persists. If each jurisdiction requires different handling regardless of the technology, operational complexity remains. This could be the persistent value add of global custodians - handling cross-border gaps in automation.

Incumbents successfully defend. Firms built around asset class expertise have incentives to maintain the distinctions that justify their value. They may slow this wave as they slowed previous ones. They also have cash and scrip for strategic M&A of challengers.

The timeline

2025 is not the year convergence happened. It is the year convergence became plausible.

Tokenised collateral in production. AI proving itself in narrow deployments. Regulatory frameworks adapting. Hybrid models emerging. The mechanism became visible.

2026-2028 will test the thesis. Does tokenisation expand? Do AI governance frameworks mature? Do firms betting on convergence outperform those defending fragmentation?

2030 is when we will know. If convergence is real, asset class silos will have collapsed and AI agents will handle most workflows. If not, we will have another round of transformation that delivered incremental improvement.

The direction seems right. The speed is uncertain.

The strategic question

Is your firm building toward the converged model, or defending the fragmented one?

Signs you are building toward convergence:

• Tokenisation treated as infrastructure, not a product line

• AI deployment focused on workflows, not isolated tasks

• Organisational structure moving away from asset class silos

• Investment in common platforms rather than asset-class-specific systems

Signs you are defending fragmentation:

• Tokenisation treated as another asset class to support

• AI used for efficiency within existing processes

• Organisational structure reinforcing asset class distinctions

• Continued investment in asset-class-specific infrastructure

Both approaches will work for the next few years. The divergence comes later. The firms that built toward convergence will have operating models that scale. The firms that defended fragmentation will have cost structures that cannot compete.

The window to choose is now.

Custody is becoming software-defined

The custody industry spent decades building expertise around the operational differences between asset classes. That expertise was valuable because those differences were real.

Tokenisation and agentic AI are erasing those differences. Not the legal differences, but the operational ones. The work of holding, settling, and servicing assets is converging toward a common model.

2025 was the year this became visible. The next five years will determine who understood what they were seeing. None of the legal and regulatory friction that exists today may survive what hyper-competition brings all industries by 2030.

Global Custody Pro exists to map this transition: not to report it, but to explain it.

If you are interested in discussing what this means for your business, reply to this email.

Do you enjoy Global Custody Pro? Share it with a colleague today:

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RiskISR is our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • Northern Trust Wins $385B in New Mandates In 2025

  • Citi report: cyber-crime costs near $10.5 trillion …

  • Citi Wins Nasdaq Post-Trade Mandate

• 🚀 Digital Asset News

  • OCC Approves Bank Crypto-Asset Principal Trading

  • Standard Chartered partners GFO-X on digital custo …

  • CFTC Launches Digital Assets Pilot Program

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🌏 Global Custody News

Northern Trust Wins $385B in New Mandates In 2025

Northern Trust announced it has secured more than 100 new mandates from institutional asset owners globally in 2025, representing over US$385 billion in assets under custody. The Chicago-based custodian said the new business spans pensions, endowments and other institutional investors across North America, Europe, the Middle East, Africa and Asia-Pacific.

The company attributed the growth to its service model and investments in technology platforms for asset owners. Melanie Pickett, head of Asset Servicing Americas, said the firm's client experience model provides "direct access to specialists, consistent service delivery, and strategic engagement." Northern Trust cited its front office solutions platform, which now supports more than $1 trillion in assets, and integrated trading solutions aimed at asset owners insourcing investment management.

Leon Stavrou, head of Australia and New Zealand, noted that superannuation funds in particular are insourcing investment management to gain greater control and efficiency. As of 30 September 2025, Northern Trust reported total assets under custody and administration of US$18.2 trillion and assets under management of US$1.8 trillion.

Citi report: cyber-crime costs near $10.5 trillion globally

According to a recently released Citi report, Cyber-crime now ranks as the second-highest concern for financial institutions after geopolitical risk, with global costs estimated at $10.5 trillion and projected to reach $15.63 trillion by 2029, according to a new industry analysis. Nearly half of compromised companies have paid ransoms, with average payments of approximately $5 million for firms with revenues exceeding $1 billion.

Regulatory responses are intensifying across jurisdictions. The SEC adopted rules in 2023 requiring public companies to disclose material cyber incidents within four business days, while the EU's Digital Operational Resilience Act (DORA) and Network and Information Systems Directive 2 (NIS2) seek to harmonise standards. Third-party risk remains acute, with 96% of firms reporting impact from vendor breaches.

Industry experts emphasise that cyber risk differs fundamentally from credit risk in its unpredictability, making resilience and recovery capabilities essential. Institutions are also contending with criminals leveraging artificial intelligence for sophisticated social engineering, while simultaneously developing quantum-resistant encryption methods in anticipation of technological advances expected within the decade.

Citi Wins Nasdaq Post-Trade Mandate

Nasdaq has selected Citigroup's Investor Services division to provide Account Operator Services in the Nordics and Euroclear Bank, the company announced. The mandate enables Nasdaq to outsource post-trade processing and operational functions to Citi's global network and technology infrastructure.

The arrangement provides Nasdaq with immediate harmonised access to relevant Financial Market Infrastructures whilst simplifying operational requirements and converting from a fixed to variable cost structure. Citi is the only bank in the Nordics to offer Account Operator services, according to the announcement. The mandate allows Nasdaq to dedicate resources to client service, transformation and innovation-focused priorities.

Matthew Bax, Head of Sales and Client Services at Citi Investor Services, said the digital-led solutions are creating greater efficiencies for clients and enabling connectivity across the financial ecosystem. Roland Chai, President of Nasdaq Europe, said Citi was the preferred choice as Nasdaq sought to gain efficiencies across post-trade activities through harmonised solutions and innovation.

Deeper PFMI insights, all in one place.

Our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

OCC Approves Bank Crypto-Asset Principal Trading

The Office of the Comptroller of the Currency confirmed in Interpretive Letter #1188, dated 9 December 2025, that national banks have the authority to engage in riskless principal transactions involving crypto-assets. In such transactions, a bank purchases a crypto-asset from one counterparty for immediate resale to another counterparty, with execution occurring effectively simultaneously and the bank holding no inventory except in rare settlement defaults. The OCC stated the activity is permissible under the "business of banking" powers clause in 12 U.S.C. § 24(Seventh), applying to both crypto-assets that are securities and those that are not securities.

The interpretive letter, signed by Adam J. Cohen, Senior Deputy Comptroller and Chief Counsel, concluded that the activity is functionally equivalent to recognised bank brokerage activities and represents a logical outgrowth of crypto-asset custody services that national banks may already provide. The regulator noted that banks acting as riskless principals face primarily counterparty credit risk rather than market risk, similar to risks in securities transactions and perfectly-matched derivative transactions. The OCC stated that offering riskless principal crypto-asset transactions would benefit customers by providing access to crypto-asset services through highly regulated banks rather than unregulated or less regulated options.

The determination requires banks to conduct riskless principal crypto-asset transactions in a safe and sound manner and in compliance with applicable law. The OCC stated it would examine such activities as part of its ongoing supervisory process. The regulator noted that state banks have long engaged in riskless principal transactions with respect to securities, and whilst state regulatory frameworks concerning crypto-asset activities by state banks continue to develop, none expressly prohibit such transactions.

Standard Chartered partners GFO-X on digital custody

Standard Chartered and GFO-X, operator of the UK's first FCA-regulated and centrally cleared digital asset derivatives trading venue, announced a partnership to provide collateral management and custodian services for digital assets to support the development of GFO-X Abu Dhabi CCP Limited. Under the arrangement, institutional clients will be able to utilise cryptocurrencies, tokenised money market funds, and other forms of digital assets as collateral for trading in a fully centrally cleared and credit intermediated model, with Standard Chartered acting as an independent, regulated custodian.

The partnership follows GFO-X Abu Dhabi's receipt of In-Principle Approval from the Financial Services Regulatory Authority of ADGM in September 2025 to operate as a Recognised Investment Exchange and a Recognised Clearing House for digital assets and related derivatives. That approval has recently been extended to include securities and ETFs. Arnab Sen, CEO and Co-Founder of GFO-X, said the partnership would "unlock a significant amount of pent-up demand for trading" in a highly regulated and cleared trading venue.

The companies stated that go-live is expected in the second half of 2026. Margaret Harwood-Jones, Global Head of Financing and Securities Services at Standard Chartered, said the partnership demonstrates "how trusted institutions can help shape a secure and scalable market structure" combining custody with central clearing and credit intermediation to enhance risk management across the digital asset ecosystem.

CFTC Launches Digital Assets Pilot Program

The Commodity Futures Trading Commission launched a digital assets pilot program allowing registered futures commission merchants to accept bitcoin, ether, and USDC as customer margin collateral in derivatives markets. Acting Chairman Caroline D. Pham announced the initiative, which includes new guidance on tokenized collateral and establishes a three-month initial period during which participating firms must provide weekly reporting to CFTC staff. The announcement follows Pham's September tokenized collateral initiative as part of the CFTC's implementation of recommendations in the President's Working Group on Digital Asset Markets report.

The CFTC's Market Participants Division issued a no-action position providing regulatory clarity on application of segregation and capital requirements to futures commission merchants accepting non-securities digital assets as margin collateral. The guidance applies to tokenized real-world assets including U.S. Treasury securities and money market funds, and addresses eligible tokenized assets, legal enforceability, custody arrangements, and operational risks. The regulator simultaneously withdrew Staff Advisory No. 20-34, which placed restrictions on futures commission merchants' ability to accept virtual currencies as customer collateral, stating the advisory had become outdated following passage of the GENIUS Act.

Industry executives including Paul Grewal, Coinbase Chief Legal Officer, and Heath Tarbert, President of Circle, said the guidance provides regulatory certainty for digital assets in derivatives markets. The no-action position requires participating futures commission merchants to notify CFTC staff promptly of any significant issues during the pilot period, providing the agency an opportunity to monitor developments associated with non-securities digital asset collateral whilst maintaining robust risk management practices.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • SIX Consolidates CCPs, Headquarters in Madrid

• 🚀 Digital Asset News

  • UK Grants Property Rights to Crypto Assets

  • SemiLiquid Launches Custody-Native Credit Protocol

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🌏 Global Custody News

SIX Consolidates CCPs, Headquarters in Madrid

SIX announced plans to integrate its two clearing houses, SIX x-clear in Switzerland and BME Clearing in Spain, into a single multi-asset central counterparty named SIX Clearing. The consolidated CCP will be headquartered in Madrid with operations in Zurich and Oslo.

The integration builds on SIX's 2020 acquisition of BME, which established its pan-European growth platform. The combined entity will transfer SIX x-clear's interoperability links to the new CCP while leveraging BME Clearing's existing EU licence, providing access to European Central Bank EUR liquidity, T2 and T2S systems, and other EU regulated markets and MTFs.

The company stated that SIX Clearing will continue promoting interoperability adoption across Europe. The transaction remains subject to regulatory approvals, with the timeline and execution form dependent on these approvals.

Deeper PFMI insights, all in one place.

Our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

UK Grants Property Rights to Crypto Assets

The UK Government announced that digital assets such as cryptocurrency and non-fungible tokens can now be recognised as personal property following Royal Assent of the Property (Digital Assets etc) Act. England, Wales and Northern Ireland are among the first jurisdictions globally to provide this legal clarification, according to the Ministry of Justice.

The legislation responds to a 2023 Law Commission report that identified barriers to recognising digital assets under private law. By establishing a third category of property that acknowledges the unique characteristics of digital assets, the government said owners will gain clearer protections against fraud and the ability to pass assets through inheritance or recover them during bankruptcy proceedings.

The legal clarification strengthens the framework for digital asset custody, collateralisation and creditor recovery in insolvency proceedings. Institutional holders and custodians operating under English law now have explicit statutory recognition that digital assets can be subject to property-based claims and security interests.

SemiLiquid Launches Custody-Native Credit Protocol

SemiLiquid announced the launch of its Programmable Credit Protocol at Abu Dhabi Finance Week 2025, introducing custody-native infrastructure that enables institutions to secure credit against digital and tokenised assets without moving collateral. The protocol was developed in Abu Dhabi and is planned for global rollout.

The announcement follows a pilot conducted with Franklin Templeton, Zodia Custody, Avalanche, Presto Labs, M11 Credit, Oasis Foundation, and law firm CMS. The pilot used Franklin Templeton's daily-yielding tokenised money-market fund, BENJI, as collateral under pre-agreed terms and automated triggers. The company noted that more than 70% of institutional bilateral financing currently involves bespoke paperwork and collateral transfers across fragmented systems.

SemiLiquid indicated Phase II will launch in early 2026 with expanded custodian and jurisdictional coverage. Rico van der Veen, Co-Founder and CEO of SemiLiquid, said the protocol delivers "a standardised, custody-native and shared legal framework that merges the trust of traditional finance with the efficiency of programmable assets."

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • NBFIs now exceed banks in major economies

  • ACSA Charts Custody's Digital Future

  • Millennium Joins JSCC Yen Clearing Service

• 🚀 Digital Asset News

  • Polkadot, Zodia Deepen Institutional Custody Acces …

  • Bybit Joins Komainu Connect Custody Platform

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🌏 Global Custody News

NBFIs now exceed banks in major economies

Source: BIS

Non-bank financial institutions have grown to hold assets averaging around 400% of GDP in major advanced economies, surpassing traditional banks in size, according to a Bank for International Settlements research bulletin. The expansion has been driven primarily by investment funds, including hedge funds, alongside bond market growth reflecting increased government debt issuance. The bulletin, authored by Ryan Banerjee, Boris Hofmann, Ding Xuan Ng and Gabor Pinter, examined how this structural shift affects monetary policy effectiveness.

The research found that NBFIs influence transmission in complex ways depending on institutional type and market conditions. Other financial institutions, which include money market funds, hedge funds and finance companies, appear to amplify monetary policy transmission to long-term yields and financial conditions. However, insurance companies and pension funds may dampen these effects due to more stable balance sheets. The bulletin noted that transmission strength also depends on market conditions, with policy rate changes having stronger effects when hedge fund trading activity and leverage are high, whilst central bank asset purchases prove more effective when such activity is low.

The findings suggest aggregate monetary transmission may have strengthened as other financial institutions have grown in relative importance, though considerable statistical uncertainty remains. The bulletin concluded that this uncertainty reinforces the case for gradual policy adjustments combined with high flexibility to respond to changing conditions. The research also highlighted that NBFIs amplify cross-border monetary spillovers, with US monetary policy shocks exerting stronger effects on investment fund flows compared with total portfolio flows, particularly affecting emerging market economies.

ACSA Charts Custody's Digital Future

The Australian Custodial Services Association has released a whitepaper marking its 30th anniversary that outlines a transformation in the custody sector from transactional service providers to embedded strategic partners. The paper notes custodians are expanding into middle-office functions, data analytics, and liquidity management as asset owners seek to streamline operations.

Digital transformation is central to the industry's outlook, with nearly 40% of financial market participants currently using digital assets or distributed ledger technology, according to research cited from Clearstream, DTCC, and Euroclear. The report forecasts that assets will be digitally custodied and tokenised on DLT within 30 years, with settlement becoming instant and operations fully automated.

The whitepaper identifies alternative investments as the top priority for institutional investors, with average portfolio allocations now approaching 25%. Industry executives, including Northern Trust's Trent Richardson, point to Project Acacia, a joint initiative with the Reserve Bank of Australia, as evidence of political support for developing Australian wholesale tokenised asset markets.

Millennium Joins JSCC Yen Clearing Service

Japan Securities Clearing Corporation announced that Millennium has become the first U.S. customer for its yen-denominated interest rate swap clearing service following regulatory approval. The milestone marks the first U.S. customer since the Commodity Futures Trading Commission granted approval in September 2025 under a no-action letter and amended exemption order.

JSCC became Japan's first licensed clearing organisation for cash equity transactions in January 2003 and now provides clearing for listed cash equities, financial and commodity derivatives, over-the-counter derivatives, and OTC Japanese Government Bond transactions. The company's interest rate swap clearing services reached a monthly record of JPY 1,526 trillion in October 2025, driven by uncertainty around the Bank of Japan's interest rate policy.

Yasuyuki Konuma, President and CEO of JSCC, said the company remains committed to providing efficient clearing services to global market participants and delivering innovative clearing solutions whilst contributing to financial market stability. JSCC continues to work with partners worldwide to expand its reach and support market participants seeking access to yen derivatives market liquidity.

Deeper PFMI insights, all in one place.

Our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

Polkadot, Zodia Deepen Institutional Custody Access

Polkadot Capital Group and Zodia Custody announced a strategic initiative to expand institutional access to Polkadot ecosystem custody and staking solutions. The partnership pairs Polkadot's multi-chain technology with Zodia's bank-first security infrastructure, which enables institutions to stake DOT tokens directly from cold, segregated custody accounts.

Zodia Custody, backed by Standard Chartered in association with Northern Trust, SBI Holdings, National Australia Bank, and Emirates NBD, is registered with multiple regulators including the UK Financial Conduct Authority, Central Bank of Ireland, and Luxembourg's CSSF. The platform implements 5AMLD requirements and applies Standard Chartered's standards relating to anti-money laundering, financial crime compliance, and know-your-customer protocols. Polkadot Capital Group serves as the capital markets-focused division of Polkadot, headquartered in the Cayman Islands with teams in North America and London.

The initiative aligns with Polkadot's ongoing network evolution aimed at institutional and enterprise adoption. "Institutions are eager to participate in ecosystems like Polkadot, but only without compromising on security," said Anoosh Arevshatian, Chief Product Officer at Zodia Custody. Dave Sedacca, Lead at Polkadot Capital Group, stated the relationship "directly addresses" institutional security requirements by pairing Polkadot's technology with custody solutions that meet regulatory standards across key jurisdictions.

Bybit Joins Komainu Connect Custody Platform

Komainu, the regulated digital asset custodian backed by Laser Digital and Blockstream, announced that Bybit has joined its Komainu Connect collateral management platform. The partnership enables institutional clients to trade continuously whilst maintaining assets in secure custody, with regular automated off-exchange settlement removing the requirement to pre-fund trading accounts.

The platform provides 100% collateral mirroring of delegated assets in collateral wallets, with all holdings maintained in on-chain, bankruptcy-remote segregated wallets. Bybit serves over 60 million users globally, whilst Komainu operates under multi-jurisdictional regulatory oversight, with Komainu (Jersey) Limited regulated by the Jersey Financial Services Commission and Komainu MEA FZE regulated by the Dubai Virtual Assets Regulatory Authority.

Paul Frost Smith, Co-CEO at Komainu, said institutional investors increasingly demand the ability to act on market opportunities without compromising security or compliance. Yoyee Wang, Head of Business to Business Unit at Bybit, said the partnership strengthens the exchange's capabilities by offering secure, regulated custody alongside flexibility and scale.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • Fed Overhauls Bank Capital and Supervision Framewo …

• 🚀 Digital Asset News

  • MoonPay Secures New York Trust Charter

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🌏 Global Custody News

Fed Overhauls Bank Capital and Supervision Framework

Federal Reserve Vice Chair for Supervision Michelle W. Bowman told Congress on 2 December that the central bank is modernising its regulatory framework for banks of all sizes, including modifications to stress testing, the supplementary leverage ratio, Basel III implementation, and the G-SIB surcharge for large institutions. Bowman stated the banking system remains sound and resilient with strong capital ratios, significant liquidity buffers, and declining non-performing loans across most categories, though non-bank financial institutions continue to increase their market share without facing equivalent prudential standards.

The Fed recently proposed enhanced transparency for stress testing including public disclosure of models and scenario frameworks, and finalised changes to the enhanced supplementary leverage ratio for U.S. G-SIBs to ensure leverage requirements serve as a backstop rather than binding constraint. Bowman emphasised the need to address mortgage capital treatment under the U.S. standardised approach, noting current requirements have resulted in banks reducing participation in mortgage lending. For community banks, the agency proposed changes to the community bank leverage ratio and released new capital options for mutual banks, whilst supporting Congressional efforts to increase statutory asset thresholds that have not been updated for inflation.

The Fed officially ended the use of reputational risk in bank supervision and is considering regulations to prevent personnel from encouraging banks to refuse service to customers based on constitutionally protected political or religious beliefs. Bowman stated the agency is developing capital, liquidity, and diversification regulations for stablecoin issuers as required by the GENIUS Act and working to provide clarity on digital asset treatment. The Fed is also reviewing its CAMELS ratings framework, which has been in place since 1979 with minimal modification, and recently finalised revisions to the Large Financial Institution ratings system.

Interesting Podcast Episode with BNY’s Leigh-Ann Russell

This was an interesting insight into how a global custody bank is thinking about and implementing AI in a regulated environment. Well worth a listen.

Deeper PFMI insights, all in one place.

Our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

MoonPay Secures New York Trust Charter

MoonPay announced on 25 November 2025 that the New York State Department of Financial Services has authorised MoonPay Trust Company, LLC as a New York Limited Purpose Trust Company. The approval enables MoonPay to provide digital asset custody and over-the-counter trading services under one of the world's most respected regulatory frameworks.

The trust charter approval builds on MoonPay's BitLicense, which the company secured from the NYDFS in June 2025 and permits operations in all 50 U.S. states. MoonPay also holds registrations in the UK, Australia, Canada, Italy, Ireland, and Jersey, as well as a crypto asset service provider licence under the European Union's Markets in Crypto Assets regulation. "Receiving our New York Trust Charter reflects our commitment to meeting the highest standards of compliance, security, and governance," said Ivan Soto-Wright, co-founder and CEO of MoonPay.

With the new authorisation, MoonPay joins a select group of digital asset companies that have obtained both New York credentials, including Coinbase, PayPal, Ripple, and NYDIG. The company stated the milestone supports its mission to deliver secure, compliant, and scalable financial infrastructure for institutional and enterprise partners.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • ISDA backs BoE CCP rules, seeks stronger capital b …

  • ISDA backs UK CCP reforms with cautions

• 🚀 Digital Asset News

  • Standard Chartered custodian for 21shares crypto E …

  • Mayer Brown analyses FCA tokenisation proposals

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🌏 Global Custody News

ISDA backs BoE CCP rules, seeks stronger capital buffers

ISDA has broadly endorsed the Bank of England's consultation proposals to establish a UK rulebook for central counterparty clearing houses, whilst calling for significantly stronger capital buffers and expanded collateral eligibility. The trade body, representing over 1,000 member institutions across 76 countries, supported the majority of the proposals but raised concerns about implementation timelines and capital sizing methodologies.

ISDA argued the second tranche of skin in the game—capital that CCPs must contribute to cover losses—should be calibrated at a minimum of 8-10% of the default fund rather than simply matching the first tranche as proposed. The association presented data showing UK CCPs maintain substantially lower capital ratios than international peers, with skin in the game representing just 1-7% of default funds at major UK CCPs compared to ratios exceeding 200% at some overseas counterparties. ISDA questioned whether the proposed two-year implementation period for the measure was necessary, noting the change does not involve IT system development.

The response advocated for regulatory changes to permit tokenised assets, exchange-traded funds and money market funds as eligible collateral, subject to robust risk management standards including appropriate haircuts and concentration limits. ISDA recommended a phased sandbox approach for testing tokenised collateral and called for enabling cross-CCP portfolio margining arrangements to support broader gilt repo market resilience. The association emphasised that any cost increases from regulatory changes should be subject to rigorous transparency frameworks to avoid general clearing cost increases in the UK.

ISDA backs UK CCP reforms with cautions

ISDA has endorsed His Majesty's Treasury's proposed reforms to the UK's central counterparty regulatory framework, according to feedback submitted on two draft statutory instruments. The association, representing over 1,000 member institutions from 78 countries, supports the shift from equivalence-based assessments towards a deference-based approach for overseas CCPs, though it notes the draft regulations do not reflect CCP views and many CCPs disagree with the positions.

ISDA welcomed the simplification whilst raising concerns about specific provisions in the CCPs (Amendment) Regulations 2025. The association questioned whether the Bank of England should be able to revoke CCP recognition where "desirable" rather than "necessary" for financial stability, and sought clarification on requirements for overseas CCPs to provide unconditional consent for information sharing. The feedback acknowledged that shorter time-to-market for CCPs benefits the industry but emphasised the importance of adequate supervision given clearing members' limited insight into CCP operations.

The association proposed an alternative approach to Qualifying CCP status that would allow firms to determine QCCP eligibility based on their own analysis of compliance with Principles for Financial Market Infrastructures, removing the Bank from the critical path. ISDA noted uncertainty about whether the new overseas CCP regime will replace or supplement the current framework, with further HMT publications expected to bring the changes into force alongside wider reforms.

Deeper PFMI insights, all in one place.

Our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

Standard Chartered custodian for 21shares crypto ETPs

Standard Chartered announced its appointment as digital asset custodian for 21shares, one of the world's largest issuers of crypto Exchange Traded Products, the company said in a statement. The partnership will utilise Standard Chartered's newly established digital asset custody service based in Luxembourg and registered with The Commission de Surveillance du Secteur Financier (CSSF).

The collaboration addresses institutional client demand for secure digital asset market participation. 21shares, which listed the world's first physically-backed crypto ETP in 2018 and offers one of the largest suites of crypto ETPs globally, will leverage Standard Chartered's global expertise as a leading international cross-border bank. 21shares is a subsidiary of FalconX, one of the world's largest digital asset prime brokers.

Margaret Harwood-Jones, Global Head of Financing and Securities Services at Standard Chartered, said the bank is excited to offer digital asset custody services to ETP providers and other institutions. Mandy Chiu, Global Head of Product Development at 21shares, described the partnership as an important milestone in bringing institutional-grade infrastructure to the digital asset ecosystem, noting Standard Chartered's expertise in cross-border banking, risk management and custody.

Mayer Brown analyses FCA tokenisation proposals

Law firm Mayer Brown has published a detailed analysis of the UK Financial Conduct Authority's consultation paper on fund tokenisation and direct-to-fund dealing, which was issued on 14 October. The analysis, authored by partners Musonda Kapotwe, Tim Nosworthy and Oliver Yaros, outlines 10 key implications for UCITS management companies, UK Alternative Investment Fund Managers managing authorised funds, and their depositaries. The regulator confirms that existing collective investment schemes rules in its Handbook and the open ended investment company regime remain sufficient to support tokenised unitholder registers for authorised funds, Mayer Brown noted.

The consultation introduces an optional direct-to-fund dealing regime that removes the AIFM's principal intermediation for unit dealing, instead allowing the fund or depositary to issue and cancel units directly with investors through a dedicated issues and cancellations bank account, according to Mayer Brown's analysis. This approach aligns the UK with models used in Ireland and Luxembourg, reducing AIFM capital and client money complexity whilst dovetailing with tokenised dealing. The regime requires robust controls including smart contract whitelisting, aggregate position monitoring across multiple wallets, and enhanced anti-money laundering verification processes, the law firm said.

Mayer Brown highlights that the FCA is considering interim waivers or sandbox routes to permit usage of qualifying stablecoins or tokenised deposits for on-chain cash settlement whilst broader stablecoin regulation is finalised. The regulator is also contemplating rule changes to create narrowly scoped permissions for holding ancillary digital assets strictly for operational purposes, with tokenised money market fund units as collateral identified as a near-term use case, the analysis states.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • ESMA Chair: T+1 Deadline Fixed, Act Now

• 🚀 Digital Asset News

  • BitGo Powers TAO ETP Custody on SIX

  • ISDA Backs Tokenised Collateral for Derivatives Ma …

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🌏 Global Custody News

ESMA Chair: T+1 Deadline Fixed, Act Now

European Securities and Markets Authority Chair Verena Ross delivered an unequivocal message to the post-trade industry: the EU's transition to T+1 settlement will proceed on schedule with no possibility of delay. Speaking at the ECSDA Conference in Brussels on 18 November 2025, Ross confirmed that all regulatory requirements would be finalised by early 2026, with ESMA having already published amendments to the settlement discipline technical standards in October.

Ross emphasised that CSDs will play a "pivotal" role in the transition, with their implementation decisions shaping how other market participants plan their own adjustments. She called on firms to view T+1 not as a compliance exercise but as a "strategic investment" in competitiveness, urging them to review end-to-end processes and explore automation opportunities. The ESMA Chair also highlighted the need for coordination with the UK and Switzerland, which are pursuing parallel transitions.

Looking beyond 2027, Ross advocated for making the EU's DLT Pilot Regime permanent, calling for "a full-fledged framework for the tokenisation of financial instruments." She noted that while uptake has been slow, projects including CSD Prague, Clearstream's D7 platform and Euroclear's D-FMI initiative demonstrate how distributed ledger technology could reshape post-trade infrastructure.

Deeper PFMI insights, all in one place.

Our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

BitGo Powers TAO ETP Custody on SIX

BitGo announced a partnership with Deutsche Digital Assets GmbH to provide institutional-grade custody and staking for the Safello Bittensor Staked TAO ETP (ISIN: DE000A4APQY4), which commenced trading on the SIX Swiss Exchange on 19 November 2025. The product was developed in collaboration with Safello.

BitGo Europe GmbH, operating under Germany's BaFin regulatory framework, serves as the appointed custodian, holding all underlying TAO in segregated cold storage. The arrangement provides institutional clients with multi-signature wallet technology and staking infrastructure. "The collaboration with Deutsche Digital Assets marks another important step in BitGo's mission to secure the digital asset economy," said Mike Belshe, CEO and Co-Founder of BitGo.

Maximilian Lautenschläger, CEO and Founder of Deutsche Digital Assets, said the ETP "marks an important step in connecting decentralised AI to traditional financial markets under a regulated, institutional-grade framework." The product enables investor exposure to Bittensor, a decentralised protocol designed as a global marketplace for machine intelligence.

ISDA Backs Tokenised Collateral for Derivatives Markets

ISDA has urged the Commodity Futures Trading Commission to support expanded use of tokenised collateral and payment stablecoins in derivatives markets, according to a 21 November letter responding to the regulator's request for input. The trade association, which represents over 1,000 member institutions from 78 countries, endorsed the GMAC Digital Asset Markets Subcommittee recommendation to expand non-cash collateral use through distributed ledger technology, focussing initially on GENIUS Act-compliant stablecoins and tokenised versions of already-eligible assets.

ISDA stated that tokenisation could substantially reduce counterparty, settlement, and market risk through near-instantaneous and atomic settlement, replacing the current T+1/T+2 timeframe. The association noted that instantaneous posting of tokenised collateral could eliminate current processes requiring cash posting followed by replacement with money market fund units or securities. ISDA has collaborated with other trade associations on DLT implementation guidance and recently introduced Digital Asset Definitions to support documentation of digital asset derivatives, whilst co-sponsoring a U.S. Tokenised Money Market Fund Working Group.

The letter requested CFTC guidance on multiple technical issues including haircut calculations for stablecoins and tokenised assets, segregation requirements under Regulation 1.44, and treatment of digital assets under eligible depository rules. ISDA recommended that for GENIUS Act-compliant stablecoins backed 100 per cent by high-quality liquid assets, haircuts should be calibrated to reserve composition and redemption characteristics rather than applied uniformly. The association emphasised the need for coordination between CFTC, SEC, Treasury, and U.S. Prudential Regulators to prevent market fragmentation and called for international harmonisation with ESMA and UK FCA on tokenised collateral standards. Mark New, ISDA's Co-Head of Digital Transformation, signed the submission.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • FSB flags Basel gaps, nonbank finance risks

  • Northern Trust Wins Osmosis Netherlands Mandate

• 🚀 Digital Asset News

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FSB flags Basel gaps, nonbank finance risks

Financial Stability Board Chair Andrew Bailey warned G20 leaders that implementation of post-crisis Basel III banking reforms remains significantly incomplete, according to an interim monitoring review delivered to finance ministers in October. The FSB said it will examine where implementation gaps occurred and make recommendations to strengthen monitoring processes, as member countries have not achieved full, timely and consistent adoption of the global standards.

Bailey highlighted growing financial stability concerns related to nonbank financial intermediation, including increased leverage in government bond markets and the expansion of private credit to an estimated $2 trillion globally. The organisation noted that private assets remain opaque and require continued assessment due to their scale and interconnections with the banking system, particularly as the form and significance of bank-nonbank linkages evolve.

Looking ahead, the FSB said cross-border payments remain slow and expensive despite some progress, with national policies including capital controls constraining advancement towards G20 targets. The organisation will prioritise work on stablecoins and digital payment forms in its upcoming programme, emphasising the need for consistent regulatory frameworks across jurisdictions to support safe innovation whilst maintaining public trust in payment systems.

Northern Trust Wins Osmosis Netherlands Mandate

Northern Trust announced on 17 November that Osmosis Investment Management NL B.V. has appointed the firm to provide middle office support services. The mandate includes investment operations outsourcing, collateral management, and currency management for the Netherlands-based sustainable fixed income manager.

Osmosis NL is an affiliate of UK-based Osmosis Investment Management UK Limited and member of the Osmosis Group of Companies. Clive Bellows, head of Europe, Middle East and Africa at Northern Trust, said the firm is proud to strengthen its collaboration with Osmosis to support their growth in the Netherlands. Albert Wisgerhof, COO and CFO of Osmosis NL, cited Northern Trust's flexibility, scale, and technology as key selection factors.

Northern Trust provides fund administration, global custody, investment operations outsourcing, and data solutions to global investment managers across asset classes. As of 30 September 2025, the firm had assets under custody/administration of $18.2 trillion and assets under management of $1.8 trillion. Automated currency management solutions are offered through Northern Trust's Global Foreign Exchange business.

Deeper PFMI insights, all in one place.

Our proprietary database of PFMI quantitative disclosures now covers 46 FMIs with selective historical depth. Coupled with our custom analytics and calculated measures, it enables sharper, more meaningful comparisons across global FMIs. If this capability aligns with your needs, contact brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

ClearToken Adopts Nasdaq Clearing for Digital Assets

ClearToken and Nasdaq announced that ClearToken has adopted Nasdaq Eqlipse Clearing technology to support its digital asset clearing and settlement service. ClearToken's CT Settle service, which recently received authorisation from the UK Financial Conduct Authority, will provide delivery-versus-payment and netting capabilities for digital assets and fiat currencies, including cryptoassets and stablecoins.

The technology addresses settlement risk and capital inefficiency in digital asset markets, where bilateral trading conducted on an unnetted basis requires market participants to prefund the total transaction amount before entering trades. Steve Briscoe, Chief Information Officer of ClearToken, stated the service allows financial institutions to net settlement exposures and settle digital assets with reduced settlement risk. Nasdaq's cloud-native clearing platform supports real-time, multi-asset clearing and risk management for both traditional and digital markets, with 24/7 processing capabilities.

ClearToken is developing a full central counterparty clearing house for digital assets that would connect multiple trading venues, custodians and settlement systems. The company is also developing digital depository capabilities within the Bank of England's Digital Securities Sandbox and preparing its CT Clear service as a recognised clearing house, subject to regulatory approvals from the Bank of England and planned recognition from the CFTC in the United States and the FSRA in the Abu Dhabi Global Market.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • SIX Surpasses CHF 1 Trillion Custody Milestone

  • ECB holds 2026 bank capital requirements steady

• 🚀 Digital Asset News

  • Deutsche Börse Integrates SG-FORGE Stablecoins

  • AMINA Launches Hong Kong Crypto Trading Services

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🌏 Global Custody News

SIX Surpasses CHF 1 Trillion Custody Milestone

SIX announced its international custody business has surpassed CHF 1 trillion in assets under custody for the first time, reaching CHF 1,008 billion and marking 6% growth so far this year. The Swiss financial infrastructure company's total assets under custody, covering both domestic and international holdings, now stand at CHF 7.2 trillion.

The milestone was driven primarily by equity assets, which increased CHF 44 billion year to date, with Foreign Registered Shares growing 10% and International ETFs expanding 5%. Other asset classes also recorded significant gains, with bonds under custody rising 12% and warrants and structured products jumping 31%. The growth reflected increased holdings from clients in the US and Germany, along with heightened ETF market activity in the UK and additional portfolios from new clients.

"This milestone in our international custody business is a testament to our growth strategy and an incentive to continue offering the best service to our clients in all asset classes in the 50 countries SIX operates in," said Rafael Moral Santiago, Head Securities Services and Member of the Executive Board of SIX. The company provides fully integrated international custody services covering equities, bonds, funds, hedge funds and structured products.

ECB holds 2026 bank capital requirements steady

The European Central Bank kept capital requirements broadly stable for 2026 following its annual supervisory review of 105 banks, with overall CET1 requirements and guidance maintained at 11.2% despite persisting global challenges. Pillar 2 requirements remained at 1.2% of risk-weighted assets, whilst non-binding Pillar 2 guidance decreased from 1.3% to 1.1%, reflecting improved stress test outcomes. Ten banks were subject to add-ons for insufficiently provisioned non-performing exposures, down from 18 in the previous cycle.

Euro area banks maintained robust capital and liquidity positions in the second quarter of 2025, with weighted average CET1 capital at 16.1%, a leverage ratio of 5.9%, and total capital ratio of 20.2%. Profitability remained strong, with annualised return on equity improving to 10.1% in the second quarter from 9.5% in the fourth quarter of 2024. The aggregate liquidity coverage ratio stood at 158%, whilst the net stable funding ratio remained broadly stable at 127%. Asset quality remained solid, with the non-performing loan ratio at 1.9%, though commercial real estate and small and medium-sized enterprise loans showed higher ratios at 4.6% and 4.9% respectively.

The ECB's supervisory priorities for 2026-28 emphasise banks' resilience to geopolitical risks and macro-financial uncertainties, including maintaining sound credit standards and implementing the Capital Requirements Regulation. The second priority targets operational resilience and ICT capabilities, with focus on risk management frameworks and data aggregation. The ECB issued roughly 30% fewer qualitative measures compared with the previous year, with 40% targeting credit risk, 17% internal governance, 11% capital adequacy and 10% operational risk.

PFMI PQD Database Update

Our proprietary database of PFMI public quantitative disclosures has reached 46 FMIs of coverage with select historic data available. We have also built our own analytics and calculated measures to enable richer comparison between different FMIs globally. If this is something you’re interested in, message brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

BNY Launches Stablecoin Reserves Money Fund

BNY announced the launch of the BNY Dreyfus Stablecoin Reserves Fund (BSRXX), a money market fund created to support institutional adoption of digital assets by holding reserves for U.S. stablecoin issuers under the GENIUS Act regulatory framework. Anchorage Digital, the first federally chartered crypto bank in the United States, secured the initial investment in the fund. The fund does not invest in stablecoins but is designed to hold reserves for stablecoins to be issued under the regulatory framework.

The GENIUS Act, enacted in July 2025, established a federal regulatory framework for U.S. stablecoins and specified eligible reserve assets for stablecoin issuers. BNY Investments Dreyfus, a top 10 U.S. money market fund sponsor, provides regulated money market funds as eligible reserves for stablecoin issuers. As of 30 September 2025, BNY oversees $57.8 trillion in assets under custody and/or administration and $2.1 trillion in assets under management. The company provides fund services for over 80% of digital asset exchange-traded products in the U.S., Canada and EMEA, and provides fund administration and custody for over 50% of tokenised fund assets globally.

Analysis cited by BNY suggests the stablecoin market could reach $1.5 trillion by 2030, driven by the regulatory framework and increasing client adoption. Stephanie Pierce, Deputy Head of BNY Investments, stated that stablecoins are at the forefront of the transformation towards an always-on, 24/7 capital markets environment. Nathan McCauley, Co-Founder and Chief Executive of Anchorage Digital, said the initiative marks a new chapter for stablecoin infrastructure in the United States, bridging trust, transparency and regulatory rigour for digital finance.

Deutsche Börse Integrates SG-FORGE Stablecoins

Deutsche Börse Group, Societe Generale Group, and Societe Generale-FORGE announced an agreement to integrate token-based cash solutions with traditional financial market infrastructure. The partnership will initially focus on strengthening CoinVertible's collateral management structure and usability as a settlement instrument for securities processes, collateral management, and treasury functions at Clearstream, the post-trade business of Deutsche Börse Group. The collaboration will also improve CoinVertible's liquidity through a new listing on Deutsche Börse Group's digital trading platforms.

The partnership offers regulated stablecoins under the EU's Markets in Crypto-Assets Regulation (MiCA), with SG-FORGE positioned as a European stablecoin issuer serving both crypto-native players and established financial market infrastructures. Deutsche Börse Group has nearly 16,000 employees and operates across major financial centres globally, whilst Societe Generale-FORGE is a regulated subsidiary licensed to provide investment services and crypto-asset services under supervision of French financial authorities.

Further steps will include jointly analysing the possibility of integrating the EUR and USD CoinVertible stablecoins across Deutsche Börse Group's entire service portfolio. The collaboration occurs alongside wholesale Central Bank Digital Currency project initiatives involving both groups, which aim to digitise capital market activity by modernising issuance, settlement, and custody of financial instruments using Distributed Ledger Technologies within a regulated framework.

AMINA Launches Hong Kong Crypto Trading Services

AMINA (Hong Kong) Limited, a subsidiary of Swiss FINMA-regulated AMINA Bank AG, announced it has received a Securities and Futures Commission Type 1 license uplift to provide crypto spot trading and asset safeguarding services to professional investors in Hong Kong. The company stated it is the first international banking group to offer comprehensive digital asset trading and custody services under Hong Kong's regulatory framework, with services now live for institutional clients, family offices, and high-net-worth individuals.

The firm launched 24/7 crypto spot trading across 13 digital assets including Bitcoin, Ethereum, stablecoins USDC and USDT, and DeFi tokens, accessible via mobile applications, web portals, and relationship management channels. The company's custody infrastructure meets SFC compliance standards with SOC 1 Type 2 and SOC 2 Type 2 certification, allowing professional investors to move cryptocurrencies between AMINA and external non-custodial wallets through whitelisted addresses. AMINA (Hong Kong) Limited originally received Type 1, Type 4, and Type 9 licenses from the SFC in November 2023, with the Type 1 license uplift for digital asset dealing services approved in October 2025.

Michael Benz, Head of AMINA Hong Kong and APAC, said the license positions the firm to serve accelerating demand from professional investors seeking regulated crypto access, with plans to expand the product range to include private fund management, structured products, derivatives, and tokenised real-world assets. The parent company AMINA Bank AG received its Swiss banking license from FINMA in August 2019 and has expanded to Abu Dhabi and Austria, with the Austrian entity receiving a CASP license under the MiCAR framework in October 2025.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • HKEX invests HK$455M in CMU OmniClear

  • FMIs Show Business Risk Management Variations

• 🚀 Digital Asset News

  • Tokenisation remains small-scale despite regulator …

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🌏 Global Custody News

HKEX invests HK$455M in CMU OmniClear

Hong Kong Exchanges and Clearing Limited will invest up to HK$455 million to acquire a 20% stake in CMU OmniClear Holdings Limited, the company announced on 12 November 2025. The Exchange Fund managed by the Hong Kong Monetary Authority will hold the remaining 80% of CMU OmniClear Holdings, which was established in October 2025. A signing ceremony was held today to formalise the strategic partnership.

The partnership follows a Memorandum of Understanding signed between CMU OmniClear and HKEX in March 2025. CMU OmniClear operates the Central Moneymarkets Unit, the central securities depository for debt securities, whilst HKEX operates the CSD platform for equities in Hong Kong. Together, the entities offer access to securities in Hong Kong and the Chinese Mainland through Stock and Bond Connect links, with combined assets under custody exceeding US$5.1 trillion. By the end of September 2025, assets under custody of CMU stood at around HK$5 trillion equivalent.

The partnership will support the continued commercialisation of CMU and business development initiatives including expansion of investor CSD services, asset class coverage and collateral management services. The stated goal is to transform CMU into a competitive, multi-asset class platform and enhance cross-asset class efficiency in Hong Kong, facilitating efficient investment flows between the Chinese Mainland, Hong Kong and international markets.

FMIs Show Business Risk Management Variations

A November 2025 assessment by CPMI-IOSCO identified variations in how 34 financial market infrastructures implement general business risk management standards, with the peer review noting seven areas where some FMIs' practices differed from PFMI Principle 15 expectations. The assessment found some FMIs determine capital requirements primarily through a six-month expense calculation without incorporating broader risk profile considerations or resources needed for recovery and wind-down scenarios.

The Level 3 review revealed differences in how FMIs across 27 jurisdictions approach LNAFE requirements and recovery planning. According to the report, some infrastructures include resources allocated for other risks in their business risk calculations, while others have not established board-approved plans for raising additional equity in stress scenarios. The assessment also noted variations in identification of business risk categories, with some FMIs not including certain legal, operational, custody, or investment risks in their frameworks.

The report indicates authorities are expected to work with FMIs to address the identified areas. The assessment, based on surveys conducted in August 2023 with follow-ups through June 2024, examined payment systems, central counterparties, securities settlement systems, and trade repositories, excluding central-bank operated infrastructures. The findings reflect variations in business risk management practices across the global FMI sector.

PFMI PQD Database Update

Our proprietary database of PFMI public quantitative disclosures has reached 46 FMIs of coverage with select historic data available. We have also built our own analytics and calculated measures to enable richer comparison between different FMIs globally. If this is something you’re interested in, message brennan@globalcustodypro.com to discuss further.

🚀 Digital Asset News

Tokenisation remains small-scale despite regulatory support

IOSCO concluded that tokenisation of financial assets remains in early stages despite growing adoption, with existing regulatory frameworks generally adequate but new risks emerging. The international securities regulator's Fintech Task Force found that whilst tokenisation does not alter the economic substance of assets, it introduces operational, technological and legal uncertainties that require regulatory attention.

The report shows adoption concentrated in fixed-income products and tokenised money market funds, with firms including BlackRock, Franklin, Spiko and Ondo launching tokenised products. However, most tokenised bonds continue trading on traditional exchanges with thin liquidity on distributed ledger technology-native venues. IOSCO noted that atomic settlement capabilities remain under-utilised due to pre-funding burdens and operational unfamiliarity, whilst hybrid custody models requiring reconciliation between on-chain and off-chain registers limit efficiency gains.

Regulators across jurisdictions including Germany, Italy, Switzerland, Spain and Japan have adopted technology-neutral approaches, applying existing securities frameworks whilst establishing sandboxes and specific legislation where needed. IOSCO identified cross-chain interoperability, availability of wholesale central bank digital currencies or tokenised deposits, and managing cyber risks as critical challenges ahead, particularly as tokenised money market funds increasingly serve as collateral in cryptocurrency markets.

© Global Custody Pro. Published by Digital Content Operations LLC. All Rights Reserved.

📰 Welcome to the Newsletter

Welcome to Global Custody Pro, read by custody professionals like you. I'm Brennan McDonald, Managing Editor. I write about the global custody industry, having spent over 12 years in financial services, including working at a global custody bank. An AI voice reads the audio version of this newsletter. Reply to this email with feedback or connect with us on LinkedIn.

Table of Contents

• 🌏 Global Custody News

  • Asian Asset Owners Remain Positive on ESG

  • ESMA quantifies total EU fund costs

• 🚀 Digital Asset News

  • Episode Six, Fireblocks Launch Unified Payments So …

  • Mantle Gains Institutional Custody Through Anchora …

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🌏 Global Custody News

Asian Asset Owners Remain Positive on ESG

ASIFMA reported that Asian institutional investors maintain strong commitment to sustainable investing despite global headwinds, with 85% expressing positive sentiment in a survey conducted between June and September 2025. The survey of 55 asset owners across eight Asian jurisdictions - including insurance companies, pension funds, sovereign wealth funds, and family offices - found that risk management considerations drive sustainable investing strategies for two-thirds of respondents, whilst 75% have integrated ESG factors into their investment processes.

The survey revealed significant implementation challenges, with limited investment opportunities and insufficient ESG data cited as the primary barriers. These challenges prove more acute for Asian investments, where corporate sustainability disclosures remain inconsistent beyond major companies. Survey respondents indicated that 60% find sustainable investment labels only moderately or slightly useful, with some preferring to develop internal frameworks rather than rely on European-centric classification systems.

Looking ahead, all surveyed asset owners expect to maintain or increase their sustainable investing allocations over the next 12-24 months, with 33% planning to increase focus on private assets including infrastructure and real estate. Survey participants expect sustainable investments to generate similar or superior returns compared to conventional strategies over the long term, though 27% anticipate lower returns in the short term due to higher compliance costs and emerging technology investments.

ESMA quantifies total EU fund costs

ESMA released a one-off market report quantifying total investor costs for UCITS and AIFs across the EEA for the 2023 calendar year. The dataset covers UCITS with approximately EUR 7.2 trillion in assets, representing 66% of the EU UCITS market, and AIFs with EUR 2.6 trillion, or 40% of the EU AIF market, subject to first-time collection caveats. For a EUR 10,000 one-year investment, retail UCITS costs range from roughly EUR 50 for passive bond funds to EUR 200 for active equity funds, whilst retail AIFs range from EUR 144 for "Other" strategies to EUR 280 for real estate funds.

The report found that distribution is a major component of the investor burden, averaging 48% of total UCITS costs and 27% for AIFs. For UCITS that pay inducements, the authority reported that inducements represent 45% of ongoing costs on average, with a representative EUR 10,000 one-year UCITS investment entailing median ongoing costs of EUR 127, of which EUR 62 goes to the distributor and EUR 65 is retained by the manufacturer.

ESMA noted that distribution remains dominated by credit institutions and investment firms, with 86% of UCITS assets distributed indirectly and more than 60% of that channel share attributable to traditional intermediaries, whilst actual one-off fees charged by manufacturers are between 38% and 96% below the maxima disclosed in PRIIPs Key Information Documents.

The report stated that distribution cost information is not fully harmonised across EU rules and that PRIIPs and MiFID II disclosures provide complementary but fragmented views. The authority emphasised that without comprehensive distribution cost reporting, an exhaustive analysis of total investor-borne costs is not possible and highlighted the ad-hoc, one-time nature of the collection and resulting limitations, including the absence of national breakdowns and potential under-representation of certain distribution channels.

🚀 Digital Asset News

Episode Six, Fireblocks Launch Unified Payments Solution

Episode Six and Fireblocks announced the co-development of a unified payments solution that bridges traditional and digital finance, the companies said at Singapore Fintech Festival. The platform enables financial institutions to issue, fund, and process fiat, stablecoins, loyalty points, and other digital assets from a single system.

The solution combines Episode Six's enterprise-grade card issuing and ledger infrastructure with Fireblocks' digital asset custody and blockchain connectivity. Through the Fireblocks Network, which is used by more than 2,400 institutional counterparties, the platform connects 120+ blockchains, 35+ digital asset exchanges, and global card networks. The system supports both pre-funded and credit-based payment options, allowing institutions to configure products ranging from instantly funded digital wallets to traditional credit offerings.

"Financial institutions are increasingly looking for ways to connect the worlds of fiat and digital assets without the complexity of running parallel systems," said John Mitchell, CEO and Co-Founder of Episode Six. The companies positioned the solution as addressing growing demand for interoperable infrastructure to support tokenised deposits, stablecoins, and other digital assets. The platform can operate as a standalone system or integrate with existing core banking infrastructure whilst introducing capabilities including instant virtual card generation, programmable stablecoin issuance, and cross-network connectivity.

Mantle Gains Institutional Custody Through Anchorage Digital

Mantle, a distribution and liquidity layer for real-world assets built on Ethereum Layer 2 network, announced that its native token $MNT on Ethereum is now supported by Anchorage Digital's custody platform. Anchorage Digital, which operates the first and only federally chartered crypto bank in the United States, will provide secure custody through its platform and self-custody wallet Porto for regulated institutions seeking to hold the token.

The partnership enables financial institutions to hold $MNT on Ethereum as a treasury asset whilst accessing Anchorage Digital's custody services. $MNT serves as the governance and utility token powering the Mantle network, which manages over $4 billion in community-owned assets. The ecosystem includes partnerships with issuers and protocols including Ethena USDe, Ondo USDY, OP-Succinct and EigenLayer.

Nathan McCauley, Co-Founder and CEO of Anchorage Digital, said the company was built to give institutions a secure, regulated way to participate in blockchain networks. The partnership reinforces Mantle's strategy to enhance liquidity, market depth and institutional adoption whilst strengthening its position as a gateway for traditional finance institutions to access on-chain liquidity and real-world assets.

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