What problem does it solve?
A securities trade settles only when both sides have sent matching instructions, the seller has the securities and the buyer has the cash, all by the intended settlement date. When any of these is missing the trade fails. Fails tie up liquidity and collateral, create credit exposure between counterparties and, in the EU, trigger cash penalties under the settlement discipline regime of the Central Securities Depositories Regulation (CSDR), which applies since February 2022. BNY Mellon has described how on a typical day about two percent of US Treasury transactions fail to settle.
Many operations teams still find a fail after it has happened: they work through a pending and failing report in the morning, look up each trade in several systems and chase counterparties and custodians by email and phone. The window to fix a problem is getting shorter. The United States moved most broker dealer transactions to settlement one business day after the trade date (T+1) in May 2024, and the EU plans to follow in October 2027, which leaves hours rather than days to spot a mismatch and correct it. The step change is to predict which instructions are at risk while there is still time to act, and to take the reading, looking up and chasing out of the exception queue.
- BNY Mellon states that on a typical day approximately two percent of US Treasury transactions fail to settle.BNY Mellon and Google Cloud Collaborate to Help Transform U.S. Treasury Market Settlement and Clearance Process (2021)
- Markets Media reports that, according to Euroclear's settlement efficiency analysis, late matching fails represent around 25% of fails.Euroclear Updates EasyFocus+ to Ease T+1 Transition (2025)
- ESMA reports that in June 2024 settlement fails across all EEA CSDs ranged from about 2.5% of the number of settlement instructions for sovereign bonds to about 20% for ETFs.Final Report on Technical Advice on CSDR Penalty Mechanism (2024)
How does it work?
- Collect the instruction lifecycle. The system reads every pending instruction with its matching status, counterparty, instrument, market, place of settlement, amount and the securities and cash positions behind it, from the firm's own books and from the status messages of the central securities depository (CSD) or custodian.
- Score the fail risk. A model trained on historical settlement outcomes estimates the probability that each instruction settles on time and ranks the drivers, such as an unmatched instruction, a counterparty with a poor record, an illiquid bond or a short position. Some CSDs now offer this score to their participants as a service.
- Estimate the cost. For each instruction at risk it estimates the exposure: the late settlement penalty, the funding cost and the client impact, so the team works the most expensive problems first.
- Work the exception. For the top of the queue an AI agent gathers the facts (both instructions side by side, standing settlement instructions, inventory, previous correspondence), proposes the likely fix and drafts the query to the counterparty or custodian in the channel they use.
- Decide and act. An operator approves any amended instruction, securities borrow, partial settlement or cash movement. The agent sends approved queries, tracks answers, chases before the cutoff and records the root cause so the same break does not return.
- Audience
- Back office
- Autonomy
- Copilot
- Adoption
- Early adopters
- Channels
- Internal tools, API and system to system, Email
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
No public deployment has disclosed a measurable outcome yet.
Value drivers: Risk and loss reduction, Lower cost to serve, Employee productivity, Speed and cycle time.
Indicative value
A broker dealer settling 2 million securities instructions a year in EU markets
EUR 250,000 to EUR 3.8 million
Fail cost avoided per year
How this is calculated
Formula: instructions * failRate * preventedShare * costPerFail. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Settlement instructions per year instructions, instructions per year | 2,000,000 | 2,000,000 | The reference firm. Replace with your own instruction volume. |
| Share of instructions that fail today failRate, fraction of instructions | 0.025 | 0.05 | The low value follows ESMA's final report on the CSDR penalty mechanism (November 2024), where fails across all EEA CSDs in June 2024 were about 2.5 percent of the number of settlement instructions for sovereign bonds, the lowest asset class, and about 20 percent for ETFs. The high value of five percent is an editorial assumption for a mixed book, not a sourced figure. Replace both with your own fail rate. |
| Share of fails prevented by acting on the prediction preventedShare, fraction of fails | 0.1 | 0.25 | Editorial assumption, deliberately conservative. No deploying organization on this page has published a measured reduction in fails. |
| Cost of one fail costPerFail, EUR per fail | 50 | 150 | Editorial assumption covering cash penalties, funding cost and operations handling time. Replace with your own penalty and handling data. |
What it leaves out: Counts only fails that are prevented. It leaves out the operator time saved on fails that still happen, penalties received from counterparties, the capital and liquidity effect of fewer open fails, client impact, and the cost of the data, the models and the integration.
Who already uses it?
4 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Euroclear
Belgium · Capital markets · 2025
In June 2025 Euroclear announced EasyFocus+, the next generation of its EasyFocus service, built with Meritsoft and Taskize and running on a Microsoft cloud. It uses predictive analytics to flag likely mismatches, identify root causes and resolve exceptions, and gives clients a single view of their settlement instructions across the Euroclear CSDs, which represent over 60% of EU settlement. The collaboration platform of Taskize, a member of the Euroclear group of companies, is embedded for routing and resolving the exceptions with counterparties. Euroclear positions it as support for the EU move to T+1 in October 2027.
No outcome disclosed.
Clearstream
Luxembourg · Capital markets · 2022
Clearstream, the Luxembourg based international central securities depository of Deutsche Börse Group, launched an AI Settlement Prediction Tool for its clients in July 2022, together with a Settlement Dashboard. The tool estimates the likelihood that a specific instruction settles on time. The enhanced version released in July 2025 identifies potential failure drivers and at risk instructions up to four business days in advance and estimates potential penalty costs, to support clients' T+1 readiness. Clearstream's current product page adds that the tool names the three primary factors most likely to cause a fail and estimates daily penalty costs. Clients access it in the Xact Web Portal. Clearstream has not published measured outcomes.
No outcome disclosed.
BNY
United States · Capital markets · 2021
On 4 February 2021 BNY, then branded BNY Mellon, announced a collaboration with Google Cloud to predict settlement failures in the US Treasury market, where it provides clearance and settlement. It trains models on millions of trades on Google Cloud's data analytics and machine learning services. BNY Mellon's Clearance and Collateral Management head described the aim as helping clients predict approximately 40% of settlement failures in Fed eligible securities with 90% accuracy. The release describes a solution in development; no production results were published.
No outcome disclosed.
BNY
United States · Capital markets · 2026
BNY built its own AI platform, Eliza, on Microsoft Azure and Microsoft Foundry, and uses it across operations. In a Microsoft customer story, BNY's Head of AI Enablement says more than ten percent of the inquiries clients raise about BNY's transactions were resolved or assisted by AI, with eighty percent faster processing of those inquiries. Microsoft's summary calls them client settlement inquiries; BNY's own words are broader, so the link to settlement exception work is adjacent rather than direct. The same story describes a digital employee that repairs incomplete payment instructions and an agentic workflow for client onboarding research.
- Cycle time reduction: 80%, client transaction inquiries resolved or assisted by AI
"More than ten percent of these inquiries were resolved or assisted by AI and that has resulted in eighty percent faster processing of these inquiries."
Claimed by: organization
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- At least a year of instruction history with matching status changes and the final settlement outcome
- Standing settlement instructions and counterparty static data
- Securities inventory and cash positions per account and depot
- Penalty reports from each CSD, to price the cost of a fail
- Past exception cases with root cause and the correspondence that resolved them
Systems to integrate
- Settlement or post trade processing system (pending and failing instructions)
- CSD and custodian status feeds, and their prediction services where offered
- Inventory, securities lending and collateral systems
- Email and post trade query platforms used with counterparties
- Case or exception management tool for the operations queue
Complexity: High
The prediction needs clean, joined history of instructions, matching statuses and outcomes across every CSD and custodian the firm uses, and the model falls under model risk management. Using the score a CSD already provides lowers the entry cost; the exception work still needs integration with the settlement system, inventory and counterparty communication.
- 1
Measure the fails you have
Take six to twelve months of fails and penalties and group them by cause, market, counterparty and asset class. Late matching, wrong settlement details and lack of securities usually need different fixes, and the size of each group decides where to start.
- 2
Use the CSD score before you build your own
Where your CSD or custodian offers a settlement prediction, feed its score and drivers into the operations queue first. Build an internal model only for flows the service does not see, such as your own internal settlements and positions.
- 3
Rank the queue by cost, not by age
Combine the fail probability with the penalty, funding and client impact so operators start with the instructions that matter most, and show the drivers next to each score.
- 4
Automate the gathering and the first query
Let the agent assemble both instructions, the standing settlement instructions and the inventory, propose the fix and draft the counterparty query. Operators approve every outgoing message at first, then allow routine information requests to go out directly. From that point, tell recipients in each message that it was written and sent by an AI agent.
- 5
Close the loop on root causes
Record the confirmed cause of every fail and every prevented fail, feed it back into the model and fix the static data or counterparty set up that caused it.
Guardrails
- Maker checker approval on every amended instruction, borrow, partial settlement or cash movement
- The agent never changes standing settlement instructions or static data on its own
- Every score shows its drivers, so operators can see why an instruction is flagged
- Outgoing counterparty queries use approved templates and contain only the data needed for the trade
- Model monitoring for drift, with a documented fallback to the manual pending report
KPIs to instrument
- Settlement efficiency by value and volume, before and after, per market
- Late settlement penalties paid per month, net of penalties received
- Precision and recall of the fail prediction on a holdout period
- Median time from flag to resolution for instructions at risk
- Operator minutes per exception and share of queries sent without manual drafting
Human in the loop
Operators decide on every action that changes an instruction or moves securities or cash, and own escalations to the front office and clients. Team leads review a weekly sample of predicted and actual fails, and model owners validate the prediction model on a schedule.
Common failure modes
- A score nobody acts on
- The prediction lands in a dashboard outside the operations queue and changes nothing. Put the score and the drivers inside the tool operators already work in.
- Too many flags
- A model tuned for recall floods the team with instructions that would have settled anyway. Tune the threshold on cost and track precision per market.
- Stale static data behind the prediction
- Wrong standing settlement instructions cause fails the model cannot explain. Treat reference data fixes as part of the programme.
- A model that learns from a different cycle
- A model trained under T+2 behaviour misjudges risk after a move to T+1. Retrain and revalidate around every change in settlement cycle or market practice.
What are the risks and rules?
EU AI Act
Depends on design
Predicting settlement fails and handling post trade exceptions between professional market participants is not a use listed in Annex III and is not a prohibited practice under Article 5, so the tier depends on how the agent communicates. While an operator reviews and sends every message, the system is minimal risk: the messages are the firm's own correspondence and the firm as deployer owes AI literacy for staff (Article 4). Once the agent sends queries or chasers to counterparty or custodian staff itself, as the playbook recommends for routine information requests, it interacts directly with natural persons and Article 50(1) requires telling the recipients they are dealing with an AI system. In both designs the provider of the text generating system must mark its output as AI generated in a machine readable format under Article 50(2). Model risk and operational resilience controls apply on top.
Rules that apply
Guidance
- Regulation (EU) No 909/2014 on improving securities settlement in the European Union and on central securities depositories (European Union, Europe). The CSDR, whose settlement discipline measures include cash penalties for participants that fail to deliver securities or cash by the intended settlement date.
- ESMA finalises its advice on the CSDR Penalty Mechanism (European Securities and Markets Authority, Europe). ESMA's November 2024 technical advice proposes a moderate increase of penalty rates and says the penalty mechanism has improved settlement efficiency since February 2022.
- SEC Finalizes Rules to Reduce Risks in Clearance and Settlement (US Securities and Exchange Commission, North America). The SEC shortened the standard settlement cycle for most broker dealer transactions from two business days after the trade date to one (T+1).
Controls to put in place
- Model inventory entry and periodic validation for any internal fail prediction model
- Documented fallback to the manual pending and failing report if the model or feed is unavailable
- Full trail of scores, proposed fixes, approvals and counterparty messages per instruction
- Monthly review of penalties and fails against the prediction, per market and counterparty
- Third party risk assessment for CSD or vendor prediction services under DORA
Frequently asked questions
- Can AI really predict which trades will fail to settle?
- Central securities depositories already offer such predictions. Clearstream's product page says its Settlement Prediction Tool calculates the likelihood that an instruction settles on time and identifies the three primary factors most likely to cause a fail up to four business days in advance. In February 2021 BNY Mellon said its model with Google Cloud aimed to help clients predict about 40 percent of settlement failures in Fed eligible securities with 90 percent accuracy, a goal rather than a result. Neither states a measured accuracy or a measured reduction in fails on the pages cited here.
- Do we need to build our own model?
- Not to start. Clearstream offers its prediction to clients through its Xact Web Portal, and Euroclear offers EasyFocus+, announced in June 2025, which gives each pending instruction a matching score (the predictive likelihood that it will be matched) and shows the CSDR penalty impact across its CSDs. An internal model adds value for flows those services do not see, such as your own inventory and internal settlements.
- Where does generative AI help if the prediction is a classic model?
- In the exception work: reading both instructions, drafting counterparty queries and chasing answers. At BNY, more than ten percent of client inquiries about its transactions were resolved or assisted by AI on BNY's Eliza platform, and BNY says this brought eighty percent faster processing of those inquiries. The inquiries are not limited to settlement exceptions.
- Is this high risk under the EU AI Act?
- No. Settlement fail prediction between professional market participants is not an Annex III use, and the tier depends on the design. With an operator sending every message it is minimal risk. Once the agent sends queries to counterparty or custodian staff itself, Article 50(1) requires telling those recipients they are dealing with an AI system, and the provider of a text generating system must mark its output under Article 50(2) in either case. Beyond that, model risk management, operational resilience under DORA and good records of every approved action are the controls that matter.
How to cite this page
Blits.ai AI Use Case Library, "AI for settlement fail prediction and post trade exception management", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/settlement-fail-prediction-and-exception-management. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published