[{"data":1,"prerenderedAt":624},["ShallowReactive",2],{"uc-settlement-fail-prediction-and-exception-management":3,"uc-regulations":413},{"useCase":4,"evidence":216,"blitsAiDeployments":337,"benchmarks":338,"indicative":339,"related":342,"indexability":411,"includeUnpublished":222},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"problem":36,"problemStats":37,"howItWorks":53,"valueDrivers":54,"kpis":59,"indicativeValue":65,"macroEstimates":100,"feasibility":101,"implementation":116,"risk":159,"blitsAi":193,"faq":195,"related":208,"datePublished":211,"dateModified":211,"lastVerified":211,"changelog":212,"slug":215},"AI for settlement fail prediction and post trade exception management","Settlement fail prediction","AI settlement fail prediction for T+1","AI scores which securities trades will fail to settle and works the exceptions early. Clearstream flags at risk instructions up to four business days in advance.","published","AI that scores each pending securities settlement instruction for its likelihood of failing, names the probable cause (unmatched instruction, wrong settlement details, lack of securities or cash), and helps operations teams work the exceptions and counterparty queries before the intended settlement date, so fewer trades fail and fewer late settlement penalties are paid.",[12,13,14,15,16],"settlement fail prediction","post trade exception management AI","trade settlement exceptions automation","CSDR penalty prediction","T+1 settlement fail prevention",[18,19,20],"capital-markets","banking","wealth-and-asset-management",[22,23],"operations","risk-management",[25,26,27,28],"prediction-and-scoring","classification-and-routing","agentic-workflow","content-generation",[30,31,32],"internal-tools","api","email","back-office","copilot","early-adopters","A securities trade settles only when both sides have sent matching instructions, the seller\nhas the securities and the buyer has the cash, all by the intended settlement date. When any of\nthese is missing the trade fails. Fails tie up liquidity and collateral, create credit exposure\nbetween counterparties and, in the EU, trigger cash penalties under the settlement discipline\nregime of the Central Securities Depositories Regulation (CSDR), which applies since February\n2022. BNY Mellon has described how on a typical day about two percent of US Treasury\ntransactions fail to settle.\n\nMany operations teams still find a fail after it has happened: they work through a pending\nand failing report in the morning, look up each trade in several systems and chase\ncounterparties and custodians by email and phone. The window to fix a problem is getting\nshorter. The United States moved most broker dealer transactions to settlement one business day\nafter the trade date (T+1) in May 2024, and the EU plans to follow in October 2027, which leaves\nhours rather than days to spot a mismatch and correct it. The step change is to predict which\ninstructions are at risk while there is still time to act, and to take the reading, looking up\nand chasing out of the exception queue.",[38,43,48],{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"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","https://www.bny.com/corporate/global/en/about-us/newsroom/company-news/bny-mellon-and-google-cloud-collaborate-to-help-transform-us-treasury-market-settlement-and-clearance-process.html",2021,{"statement":44,"sourceTitle":45,"sourceUrl":46,"year":47},"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","https://www.marketsmedia.com/euroclear-updates-easyfocus-to-ease-t1-transition/",2025,{"statement":49,"sourceTitle":50,"sourceUrl":51,"year":52},"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","https://www.esma.europa.eu/sites/default/files/2024-11/ESMA74-2119945925-2059_Final_Report_on_Technical_Advice_on_CSDR_Penalty_Mechanism.pdf",2024,"1. **Collect the instruction lifecycle.** The system reads every pending instruction with its\n   matching status, counterparty, instrument, market, place of settlement, amount and the\n   securities and cash positions behind it, from the firm's own books and from the status\n   messages of the central securities depository (CSD) or custodian.\n2. **Score the fail risk.** A model trained on historical settlement outcomes estimates the\n   probability that each instruction settles on time and ranks the drivers, such as an\n   unmatched instruction, a counterparty with a poor record, an illiquid bond or a short\n   position. Some CSDs now offer this score to their participants as a service.\n3. **Estimate the cost.** For each instruction at risk it estimates the exposure: the late\n   settlement penalty, the funding cost and the client impact, so the team works the most\n   expensive problems first.\n4. **Work the exception.** For the top of the queue an AI agent gathers the facts (both\n   instructions side by side, standing settlement instructions, inventory, previous\n   correspondence), proposes the likely fix and drafts the query to the counterparty or\n   custodian in the channel they use.\n5. **Decide and act.** An operator approves any amended instruction, securities borrow,\n   partial settlement or cash movement. The agent sends approved queries, tracks answers,\n   chases before the cutoff and records the root cause so the same break does not return.",[55,56,57,58],"risk-reduction","cost-to-serve","employee-productivity","speed",[60,61,62,63,64],"handling-time-reduction","accuracy","error-reduction","cost-reduction","automation-rate",{"referenceOrg":66,"inputs":67,"formula":95,"currency":96,"period":97,"resultLabel":98,"caveat":99},"A broker dealer settling 2 million securities instructions a year in EU markets",[68,74,81,88],{"key":69,"label":70,"low":71,"high":71,"unit":72,"note":73},"instructions","Settlement instructions per year",2000000,"instructions per year","The reference firm. Replace with your own instruction volume.",{"key":75,"label":76,"low":77,"high":78,"unit":79,"note":80},"failRate","Share of instructions that fail today",0.025,0.05,"fraction of instructions","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.",{"key":82,"label":83,"low":84,"high":85,"unit":86,"note":87},"preventedShare","Share of fails prevented by acting on the prediction",0.1,0.25,"fraction of fails","Editorial assumption, deliberately conservative. No deploying organization on this page has published a measured reduction in fails.",{"key":89,"label":90,"low":91,"high":92,"unit":93,"note":94},"costPerFail","Cost of one fail",50,150,"EUR per fail","Editorial assumption covering cash penalties, funding cost and operations handling time. Replace with your own penalty and handling data.","instructions * failRate * preventedShare * costPerFail","EUR","per year","Fail cost avoided","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.",[],{"complexity":102,"complexityNote":103,"dataPrerequisites":104,"integrations":110},"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.",[105,106,107,108,109],"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",[111,112,113,114,115],"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",{"steps":117,"guardrails":133,"humanInTheLoop":139,"kpisToInstrument":140,"failureModes":146},[118,121,124,127,130],{"title":119,"detail":120},"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.",{"title":122,"detail":123},"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.",{"title":125,"detail":126},"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.",{"title":128,"detail":129},"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.",{"title":131,"detail":132},"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.",[134,135,136,137,138],"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","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.",[141,142,143,144,145],"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",[147,150,153,156],{"title":148,"detail":149},"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.",{"title":151,"detail":152},"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.",{"title":154,"detail":155},"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.",{"title":157,"detail":158},"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.",{"euAiAct":160,"regulations":163,"guidance":168,"controls":186,"incidents":192},{"tier":161,"basis":162},"context-dependent","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.",[164,165,166,167],"eu-ai-act","dora","us-sr-11-7","iso-42001",[169,175,180],{"title":170,"issuer":171,"region":172,"url":173,"note":174},"Regulation (EU) No 909/2014 on improving securities settlement in the European Union and on central securities depositories","European Union","europe","https://eur-lex.europa.eu/eli/reg/2014/909/oj","The CSDR, whose settlement discipline measures include cash penalties for participants that fail to deliver securities or cash by the intended settlement date.",{"title":176,"issuer":177,"region":172,"url":178,"note":179},"ESMA finalises its advice on the CSDR Penalty Mechanism","European Securities and Markets Authority","https://www.esma.europa.eu/press-news/esma-news/esma-finalises-its-advice-csdr-penalty-mechanism","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.",{"title":181,"issuer":182,"region":183,"url":184,"note":185},"SEC Finalizes Rules to Reduce Risks in Clearance and Settlement","US Securities and Exchange Commission","north-america","https://www.sec.gov/newsroom/press-releases/2023-29","The SEC shortened the standard settlement cycle for most broker dealer transactions from two business days after the trade date to one (T+1).",[187,188,189,190,191],"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",[],{"howToBuild":194},"On Blits.ai the prediction itself comes from the CSD or custodian service or from the firm's own\nmodel; the platform does the exception work around it. An **agentic workflow**, started through\nthe API by the settlement system or on a schedule before each cutoff, uses **custom functions**\nto read the at risk instructions, the matching status, standing settlement instructions and\ninventory through REST or SQL, and a **knowledge base** with hybrid retrieval holds market\nrules, CSD procedures and internal runbooks. The agent proposes the fix and drafts the\ncounterparty query with **structured output**.\n\n**Human in the loop approval** holds every amended instruction, borrow or cash movement until an\noperator approves it, and the inbound and outbound **email channel** can carry approved\ncounterparty queries and their replies. Every run keeps a **full audit trail**, **guardrails**\ncheck outgoing text, and **test suites** run the agent and workflow against sample cases before\na new market or exception type goes live. The platform is model\nagnostic and can run in EU or UAE regions for data residency.",[196,199,202,205],{"question":197,"answer":198},"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.",{"question":200,"answer":201},"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.",{"question":203,"answer":204},"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.",{"question":206,"answer":207},"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.",[209,210],"ledger-and-payment-reconciliation","payment-investigations-and-exceptions","2026-09-27",[213],{"date":211,"note":214},"First published","settlement-fail-prediction-and-exception-management",[217,258,286,309],{"title":218,"useCases":219,"organization":220,"vendors":224,"summary":232,"stage":233,"year":47,"channels":234,"languages":235,"metrics":237,"outcomeDisclosed":222,"sources":238,"verification":253,"grade":255,"id":256,"organizationSlug":257},"Euroclear: EasyFocus+ predictive analytics for matching and settlement exceptions",[215],{"name":221,"anonymized":222,"country":223,"region":172,"industry":18},"Euroclear",false,"BE",[225,228,230],{"name":226,"role":227},"Meritsoft (Cognizant)","platform",{"name":229,"role":227},"Taskize",{"name":231,"role":227},"Microsoft","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.","production",[30],[236],"en",[],[239,244,247,251],{"url":240,"title":241,"publisher":242,"date":243},"https://www.meritsoft.com/euroclear-teams-with-meritsoft-and-taskize-to-launch-next-generation-ai-service/","Euroclear teams with Meritsoft and Taskize to launch next generation AI service","Meritsoft","2025-06-16",{"url":245,"title":241,"publisher":221,"date":243,"archivedUrl":246},"https://www.euroclear.com/newsandinsights/en/press/2025/mr-16-meritsoft-and-taskize-to-launch-next-generation-ai-service.html","https://web.archive.org/web/2026/https://www.euroclear.com/newsandinsights/en/press/2025/mr-16-meritsoft-and-taskize-to-launch-next-generation-ai-service.html",{"url":248,"title":249,"publisher":221,"archivedUrl":250},"https://www.euroclear.com/services/en/settlement/settlement-euroclear-bank/easyfocus.html","Euroclear EasyFocus+","https://web.archive.org/web/20260831082713/https://www.euroclear.com/services/en/settlement/settlement-euroclear-bank/easyfocus.html",{"url":46,"title":45,"publisher":252},"Markets Media",{"level":254,"checkedAt":211},"source-verified","B","euroclear-easyfocus-plus-settlement-analytics",null,{"title":259,"useCases":260,"organization":261,"vendors":264,"summary":267,"stage":233,"year":268,"channels":269,"languages":270,"metrics":271,"outcomeDisclosed":222,"sources":272,"verification":284,"grade":255,"id":285,"organizationSlug":257},"Clearstream: AI Settlement Prediction Tool for settlement fails and penalties",[215],{"name":262,"anonymized":222,"country":263,"region":172,"industry":18},"Clearstream","LU",[265],{"name":262,"role":266},"in-house","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.",2022,[30],[236],[],[273,277,281],{"url":274,"title":275,"publisher":262,"date":276},"https://www.clearstream.com/clearstream-en/newsroom/220711-3153554","Clearstream launches data solutions to predict settlement failures and to foster settlement efficiency","2022-07-11",{"url":278,"title":279,"publisher":262,"date":280},"https://www.clearstream.com/clearstream-en/newsroom/250728-4582598","Clearstream Enhances its Settlement Prediction Tool to Manage Settlement Risk and Support T+1 Client Readiness","2025-07-28",{"url":282,"title":283,"publisher":262},"https://www.clearstream.com/clearstream-en/res-library/connectivity/optimize-your-settlement-efficiency-with-our-predictive-data-services-3446132","Optimize your Settlement Efficiency with our Predictive Data Services",{"level":254,"checkedAt":211},"clearstream-settlement-prediction-tool",{"title":287,"useCases":288,"organization":289,"vendors":292,"summary":296,"stage":297,"year":42,"channels":298,"languages":299,"metrics":300,"outcomeDisclosed":222,"sources":301,"verification":306,"grade":255,"id":307,"organizationSlug":308},"BNY Mellon: machine learning to predict US Treasury settlement fails, with Google Cloud",[215],{"name":290,"anonymized":222,"country":291,"region":183,"industry":18},"BNY","US",[293,295],{"name":294,"role":227},"Google Cloud",{"name":290,"role":266},"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.","announced",[],[236],[],[302,304],{"url":41,"title":40,"publisher":290,"date":303},"2021-02-04",{"url":305,"title":40,"publisher":294,"date":303},"https://www.googlecloudpresscorner.com/22021-02-04-BNY-Mellon-and-Google-Cloud-Collaborate-to-Help-Transform-U-S-Treasury-Market-Settlement-and-Clearance-Process",{"level":254,"checkedAt":211},"bny-mellon-treasury-settlement-fail-prediction","bny",{"title":310,"useCases":311,"organization":312,"vendors":313,"summary":316,"stage":233,"year":317,"channels":318,"languages":319,"metrics":320,"outcomeDisclosed":330,"sources":331,"verification":334,"grade":335,"id":336,"organizationSlug":308},"BNY: Eliza AI platform resolves client transaction inquiries faster",[215],{"name":290,"anonymized":222,"country":291,"region":183,"industry":18},[314,315],{"name":290,"role":266},{"name":231,"role":227},"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.",2026,[30],[236],[321],{"kpi":322,"value":323,"unit":324,"qualifier":325,"period":326,"claimant":327,"quote":328,"sourceUrl":329},"processing-time-reduction",80,"percent","exact","client transaction inquiries resolved or assisted by AI","organization","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.","https://www.microsoft.com/en/customers/story/27322-bny-microsoft-365-copilot",true,[332],{"url":329,"title":333,"publisher":231},"Frontier Firm BNY resolves client inquires 80% faster with Microsoft AI powered Eliza",{"level":254,"checkedAt":211},"C","bny-eliza-client-settlement-inquiries",0,[],{"low":340,"high":341},250000,3750000,[343,363,381,396],{"slug":209,"title":344,"shortTitle":345,"definition":346,"status":9,"industries":347,"functions":351,"patterns":353,"audience":33,"autonomy":356,"adoptionStage":35,"segment":33,"evidenceCount":357,"publicEvidenceCount":357,"organizations":358,"bestGrade":255,"headline":257,"lastVerified":211,"indexable":330},"AI for ledger and payment reconciliation","Ledger and payment reconciliation","AI that matches entries across nostro and vostro statements, card and scheme settlement files, the general ledger and suspense accounts, proposes matches and clearing journals, and routes only the genuine breaks to an operator with a plain language explanation.",[19,348,18,349,20,350],"payments","cross-industry","government",[352,22],"finance-and-accounting",[27,354,355],"anomaly-detection","document-processing","supervised-agent",4,[359,360,361,362],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme",{"slug":210,"title":364,"shortTitle":365,"definition":366,"status":9,"industries":367,"functions":368,"patterns":370,"audience":33,"autonomy":356,"adoptionStage":371,"segment":33,"evidenceCount":372,"publicEvidenceCount":372,"organizations":373,"bestGrade":255,"headline":375,"lastVerified":211,"indexable":330},"AI for payment investigations and exceptions","Payment investigations and exceptions","AI that works the payments that fall out of straight through processing: it reads the failure, repairs or enriches the message, drafts the ISO 20022 or SWIFT investigation, chases the counterparty bank and proposes a return, recall or correction, while an operator approves anything that moves money.",[19,348],[22,369],"customer-service",[27,355,26,28],"emerging",2,[290,374],"JPMorgan Chase",{"kpi":64,"label":376,"unit":324,"n":377,"nUpTo":337,"kind":378,"value":379,"qualifier":380,"claimant":327,"organization":290,"vendorReported":222},"Automation rate",1,"reported",10,"at-least",{"slug":382,"title":383,"shortTitle":384,"definition":385,"status":9,"industries":386,"functions":387,"patterns":389,"audience":33,"autonomy":34,"adoptionStage":371,"segment":390,"evidenceCount":357,"publicEvidenceCount":391,"organizations":392,"bestGrade":255,"headline":257,"lastVerified":211,"indexable":330},"portfolio-drift-monitoring-and-rebalancing","AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[20,19],[22,23,388],"analytics-and-reporting",[354,27,25,28],"middle-office",3,[393,394,395],"Morgan Stanley","SimCorp","Vanguard",{"slug":397,"title":398,"shortTitle":399,"definition":400,"status":9,"industries":401,"functions":403,"patterns":405,"audience":33,"autonomy":356,"adoptionStage":371,"segment":406,"evidenceCount":391,"publicEvidenceCount":391,"organizations":407,"bestGrade":255,"headline":257,"lastVerified":211,"indexable":330},"continuous-controls-testing","AI for continuous controls testing and control self assessment","Continuous controls testing","AI that moves control testing from periodic samples to continuous, full population assurance: it collects evidence from source systems, maps each artefact to the control it supports, tests every transaction or record against the control's rule, flags exceptions for a human to judge and prepares the risk and control self assessment from incident and loss data for the business to review.",[349,19,402,18,350],"insurance",[23,404,22],"regulatory-compliance",[27,355,354,26],"second-line",[408,409,410],"Federal Deposit Insurance Corporation","U.S. Department of the Interior","Pension Benefit Guaranty Corporation",{"indexable":330,"reasons":412},[],[414,419,425,432,439,444,451,458,466,473,480,485,492,499,505,510,517,523,529,535,541,547,552,557,562,569,576,581,587,595,601,607,613,618],{"id":164,"label":415,"issuer":171,"region":172,"url":416,"description":417,"useCases":418,"indexable":330},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":420,"label":421,"issuer":171,"region":172,"url":422,"description":423,"useCases":424,"indexable":330},"gdpr","GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":167,"label":426,"issuer":427,"region":428,"url":429,"description":430,"useCases":431,"indexable":330},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":433,"label":434,"issuer":435,"region":183,"url":436,"description":437,"useCases":438,"indexable":330},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":165,"label":440,"issuer":171,"region":172,"url":441,"description":442,"useCases":443,"indexable":330},"DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":445,"label":446,"issuer":447,"region":172,"url":448,"description":449,"useCases":450,"indexable":330},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":452,"label":453,"issuer":454,"region":172,"url":455,"description":456,"useCases":457,"indexable":330},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":459,"label":460,"issuer":461,"region":462,"url":463,"description":464,"useCases":465,"indexable":330},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":467,"label":468,"issuer":469,"region":462,"url":470,"description":471,"useCases":472,"indexable":330},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":474,"label":475,"issuer":476,"region":428,"url":477,"description":478,"useCases":479,"indexable":330},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":166,"label":481,"issuer":482,"region":183,"url":483,"description":484,"useCases":479,"indexable":330},"SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":486,"label":487,"issuer":488,"region":172,"url":489,"description":490,"useCases":491,"indexable":330},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":493,"label":494,"issuer":495,"region":428,"url":496,"description":497,"useCases":498,"indexable":330},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":500,"label":501,"issuer":171,"region":172,"url":502,"description":503,"useCases":504,"indexable":330},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":506,"label":507,"issuer":171,"region":172,"url":508,"description":509,"useCases":504,"indexable":330},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":511,"label":512,"issuer":513,"region":183,"url":514,"description":515,"useCases":516,"indexable":330},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":518,"label":519,"issuer":171,"region":172,"url":520,"description":521,"useCases":522,"indexable":330},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":524,"label":525,"issuer":526,"region":183,"url":527,"description":528,"useCases":522,"indexable":330},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":530,"label":531,"issuer":532,"region":428,"url":533,"description":534,"useCases":522,"indexable":330},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":536,"label":537,"issuer":171,"region":172,"url":538,"description":539,"useCases":540,"indexable":330},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":542,"label":543,"issuer":544,"region":183,"url":545,"description":546,"useCases":540,"indexable":330},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":548,"label":549,"issuer":461,"region":462,"url":550,"description":551,"useCases":379,"indexable":330},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":553,"label":554,"issuer":171,"region":172,"url":555,"description":556,"useCases":379,"indexable":330},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":558,"label":559,"issuer":171,"region":172,"url":560,"description":561,"useCases":379,"indexable":330},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":563,"label":564,"issuer":565,"region":172,"url":566,"description":567,"useCases":568,"indexable":330},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":570,"label":571,"issuer":572,"region":183,"url":573,"description":574,"useCases":575,"indexable":330},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":577,"label":578,"issuer":171,"region":172,"url":579,"description":580,"useCases":575,"indexable":330},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":582,"label":583,"issuer":171,"region":172,"url":584,"description":585,"useCases":586,"indexable":330},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":588,"label":589,"issuer":590,"region":591,"url":592,"description":593,"useCases":594,"indexable":330},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":596,"label":597,"issuer":598,"region":172,"url":599,"description":600,"useCases":357,"indexable":330},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":602,"label":603,"issuer":604,"region":172,"url":605,"description":606,"useCases":357,"indexable":330},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":608,"label":609,"issuer":610,"region":462,"url":611,"description":612,"useCases":391,"indexable":330},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":614,"label":615,"issuer":171,"region":172,"url":616,"description":617,"useCases":391,"indexable":330},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":619,"label":620,"issuer":621,"region":183,"url":622,"description":623,"useCases":391,"indexable":330},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598301580]