[{"data":1,"prerenderedAt":614},["ShallowReactive",2],{"uc-ledger-and-payment-reconciliation":3,"uc-regulations":408},{"useCase":4,"evidence":196,"blitsAiDeployments":292,"benchmarks":293,"indicative":294,"related":297,"indexability":406,"includeUnpublished":202},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":23,"patterns":26,"channels":30,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":85,"feasibility":86,"implementation":99,"risk":138,"blitsAi":172,"faq":174,"related":184,"datePublished":191,"dateModified":191,"lastVerified":191,"changelog":192,"slug":195},"AI for ledger and payment reconciliation","Ledger and payment reconciliation","AI reconciliation for bank ledgers and payments","AI matches statements, settlement files and ledger entries and routes the breaks it cannot clear to an operator. See where banks and funds already run it.","published","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.",[12,13,14,15],"AI reconciliation","nostro reconciliation automation","intelligent transaction matching","exception management for reconciliations",[17,18,19,20,21,22],"banking","payments","capital-markets","cross-industry","wealth-and-asset-management","government",[24,25],"finance-and-accounting","operations",[27,28,29],"agentic-workflow","anomaly-detection","document-processing",[31,32],"internal-tools","api","back-office","supervised-agent","early-adopters","Every bank reconciles its own books against the outside world many times a day: nostro\nstatements against expected cash flows, card and scheme settlement files against authorised\ntransactions, clearing and suspense accounts against the general ledger. Rule based matching\nengines handle the clean cases, but timing differences, partial references, split and bulked\npayments, bank charges and FX conversions leave a steady stream of breaks that people clear by\nhand, often in spreadsheets.\n\nThose breaks are where the cost and the risk sit. Items ageing in suspense accounts can distort\nthe balance sheet, tie up capital and liquidity, hide fraud and lead to audit findings. Writing a new\nmatching rule for every new pattern is slow, so operations teams grow with volume instead of\nstaying flat.",[],"1. **Ingest every feed.** Statements (MT940, camt.053), settlement files, ledger extracts and\n   remittance advices arrive in one pipeline; document AI reads the unstructured ones.\n2. **Match beyond the rules.** Deterministic rules clear exact matches first. A learned matching\n   layer then proposes one to one, one to many and many to many matches using fuzzy references,\n   amounts within tolerance, value dates and FX, each with a confidence score.\n3. **Explain and propose.** For each proposed match or break the agent states why (for example\n   \"bank charge of 15 EUR deducted by the correspondent\") and drafts the clearing journal or\n   the adjustment.\n4. **Route the exceptions.** Low confidence items and anything above a materiality threshold go\n   to an operator queue with the evidence attached. Retrieval over prior resolutions suggests how\n   similar breaks were cleared before.\n5. **Learn under control.** Operator decisions feed back as candidate rules or training data,\n   which a reconciliation owner approves before they change production matching.",[40,41,42,43],"cost-to-serve","risk-reduction","speed","employee-productivity",[45,46,47,48,49],"automation-rate","processing-time-reduction","hours-saved","error-reduction","productivity-gain",{"referenceOrg":51,"inputs":52,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A mid sized bank that clears 300,000 reconciliation breaks by hand each year",[53,59,66,73],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"manualItems","Breaks cleared manually per year",300000,"items per year","The reference bank. Replace with the exception count from your reconciliation platform.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"automatedShare","Share of those breaks the AI clears or pre clears",0.3,0.6,"fraction of manual items","Editorial assumption; no verified public benchmark for AI match rates was found. Replace with a pilot result on your own data.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"minutesPerItem","Minutes an operator spends per break",6,12,"minutes per item","Editorial assumption, replace with your own time study.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerHour","Fully loaded operations cost per hour",35,60,"USD per hour","Editorial assumption for a blended onshore and offshore operations team.","manualItems * automatedShare * minutesPerItem / 60 * costPerHour","USD","per year","Manual reconciliation effort avoided","Labour only. It leaves out the value of fewer aged items in suspense (capital, liquidity and fraud exposure), fewer audit findings, and the cost of the platform and the integration work.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":93},"medium","The matching logic is well understood; the work is data. Feeds arrive in many formats and timings, references are inconsistent across systems, and every automated journal needs a control design that auditors accept.",[90,91,92],"Twelve months of history of matched items and cleared breaks, with the resolution chosen","Clean static data for accounts, counterparties and correspondent banks","Documented tolerances and materiality thresholds per reconciliation",[94,95,96,97,98],"Reconciliation platform or matching engine","General ledger and subledgers","SWIFT or bank statement feeds (MT940, MT950, camt.053)","Card scheme and acquirer settlement files","Workflow or case tool for exception queues",{"steps":100,"guardrails":116,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[101,104,107,110,113],{"title":102,"detail":103},"Start with the reconciliations that hurt most","Rank reconciliations by manual breaks per month and by aged value in suspense. Pick two or three with high volume and clear ownership, such as a card settlement or a busy nostro.",{"title":105,"detail":106},"Baseline the current rules","Measure what the existing engine already matches so the AI is credited only for the increment. Many teams find quick wins by fixing static data before any model is involved.",{"title":108,"detail":109},"Run in shadow mode","Let the AI propose matches and journals next to the operators for several cycles and compare. Only promote a match type to automatic once its precision on your data is proven.",{"title":111,"detail":112},"Set materiality and maker checker thresholds","Agree with finance and audit which proposals may post automatically and which need a second person, by amount, account type and confidence.",{"title":114,"detail":115},"Close the learning loop","Capture why operators accept or reject a proposal and review those patterns monthly with the reconciliation owner before they become new rules.",[117,118,119,120],"No automatic posting above the materiality threshold; those journals need a maker and a checker","Every match and journal carries the evidence and confidence it was based on","Tolerances and thresholds are configuration owned by finance, not learned by the model","Unmatched items are never forced to clear; low confidence always goes to a person","Operators own the exception queue and approve every journal above the threshold. The reconciliation owner approves new match types and tolerances, and internal audit samples automated matches each quarter.",[123,124,125,126,127],"Auto match rate per reconciliation, on top of the rule engine baseline","Precision of automated matches from a monthly sample","Number and value of items aged over 30 days in suspense","Operator minutes per break","Audit findings related to reconciliations",[129,132,135],{"title":130,"detail":131},"False matches that hide a real break","A plausible but wrong match clears an item that should have been investigated. Sample automated matches and keep tight tolerances on amount.",{"title":133,"detail":134},"Drift after an upstream change","A new file format or reference convention quietly lowers match quality. Monitor match rate per feed and alert on sudden drops.",{"title":136,"detail":137},"Credit for work the rules already did","Benefits are overstated because the baseline was not measured. Report the increment over the existing engine.",{"euAiAct":139,"regulations":142,"guidance":147,"controls":166,"incidents":171},{"tier":140,"basis":141},"minimal","Matching entries between internal financial records is not a use listed in Annex III and is not a practice prohibited by Article 5. Operators knowingly use an internal AI tool, so no Article 50(1) disclosure is needed. If a generative model drafts the explanations or journals, the provider of that system may have to mark its output as AI generated under Article 50(2). The AI literacy duty of Article 4 applies to the bank as deployer.",[143,144,145,146],"eu-ai-act","dora","apra-cps-230","iso-42001",[148,154,160],{"title":149,"issuer":150,"region":151,"url":152,"note":153},"Principles for effective risk data aggregation and risk reporting (BCBS 239)","Basel Committee on Banking Supervision","global","https://www.bis.org/publ/bcbs239.htm","Principle 3 expects risk data to be reconciled with the bank's sources, including accounting data where appropriate, and aggregated on a largely automated basis.",{"title":155,"issuer":156,"region":157,"url":158,"note":159},"SS1/23 Model risk management principles for banks","Bank of England, Prudential Regulation Authority","europe","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","Applies to UK banks, building societies and PRA designated investment firms with internal model approval for regulatory capital. A learned matching model that suggests or posts journals falls under its model definition and belongs in the model inventory with validation and monitoring.",{"title":161,"issuer":162,"region":163,"url":164,"note":165},"SR 26-2 Revised Guidance on Model Risk Management","Board of Governors of the Federal Reserve System, OCC and FDIC","north-america","https://www.federalreserve.gov/supervisionreg/srletters/SR2602.htm","Issued on 17 April 2026, it supersedes and replaces SR 11-7 and asks for a risk based approach tailored to each bank's model risk profile, size and complexity. The letter says it is most relevant to banking organizations with over $30 billion in total assets regulated by the Federal Reserve. A US bank that treats its learned matching model as a model under its policy validates and monitors it on this basis.",[167,168,169,170],"Model inventory entry with an owner, validation and drift monitoring per reconciliation","Maker checker on journals above the materiality threshold","Immutable log of every proposal, the evidence used and who approved it","Quarterly sample of automated matches reviewed by an independent team",[],{"howToBuild":173},"On Blits.ai this is an **agentic workflow** that runs on a schedule or is triggered through the\nAPI when a new file arrives. **Custom functions** read the feeds and the ledger through REST calls\nand SQL queries, **SQL knowledge bases** let the agent query reconciliation tables directly, the\nagent proposes matches and journals within a **tool execution policy**, and **human in the loop\napproval** holds any journal above the configured threshold until an operator approves or\nrejects it.\n\nPrior break resolutions and reconciliation procedures sit in the **knowledge base** with hybrid\nretrieval, so the explanation for each exception cites how similar items were cleared. Each run\nkeeps a **full audit trail**, **test suites** replay known breaks against the workflow before any\nchange goes live, and **monitors** run scheduled checks with email or webhook alerts when an\nexpectation fails. The platform is model agnostic and can run in EU or UAE regions for data\nresidency.",[175,178,181],{"question":176,"answer":177},"How is AI reconciliation different from a rule based matching engine?","Rules clear exact and near exact matches and should stay. AI adds matching on partial references, amounts within tolerance, one to many and many to many combinations, and a plain language explanation of each break, so fewer items reach an operator and those that do arrive with a suggested resolution.",{"question":179,"answer":180},"Can the AI post clearing journals on its own?","Only within limits agreed with finance and audit. A common design lets low value, high confidence matches post automatically and keeps a maker and a checker on anything above a materiality threshold, with every proposal logged with its evidence.",{"question":182,"answer":183},"Who is already using AI for reconciliation?","Public evidence is still thin on measured results. National Bank of Greece in Cyprus consolidated four reconciliation systems onto an AI enabled platform in 2026, and Comrade Trustee Services in Papua New Guinea went live on the same vendor platform, which says processing time fell from up to eight hours to under five minutes. A 2024 US federal AI inventory entry listed a World Food Programme machine learning tool for reconciling cash transfers at the implementation and assessment stage. (Ginnie Mae uses machine learning to find anomalies and exceptions in subledger transaction data before financial reporting, which is a related data quality use, not reconciliation matching.)",[185,186,187,188,189,190],"payment-investigations-and-exceptions","fee-and-interest-leakage-detection","supplier-invoice-processing","chargeback-and-representment","regulatory-report-assembly","treasury-cash-flow-forecasting","2026-09-27",[193],{"date":191,"note":194},"First published","ledger-and-payment-reconciliation",[197,229,256,276],{"title":198,"useCases":199,"organization":200,"vendors":204,"summary":208,"stage":209,"year":210,"channels":211,"languages":212,"metrics":214,"outcomeDisclosed":202,"sources":215,"verification":224,"grade":226,"id":227,"organizationSlug":228},"Ginnie Mae: machine learning to find exceptions in subledger transaction data",[195],{"name":201,"anonymized":202,"country":203,"region":163,"industry":22},"Ginnie Mae",false,"US",[205],{"name":206,"role":207},"Ernst & Young","integrator","Ginnie Mae, part of the US Department of Housing and Urban Development, analyses the transaction data of its master subservicers every month. Since April 2021 it has used machine learning models, built in house and with Ernst & Young as contractor, to detect anomalies, data inconsistencies and exceptions in that data. It says early detection reduces manual adjustments to financial reporting, which saves cost and time. The 2025 federal inventory still lists the system as deployed. No outcome figures are published.","production",2021,[31],[213],"en",[],[216,221],{"url":217,"title":218,"publisher":219,"date":220},"https://raw.githubusercontent.com/ombegov/2024-Federal-AI-Use-Case-Inventory/main/data/2024_consolidated_ai_inventory_raw_v2.csv","2024 consolidated AI use case inventory (raw data, version 2)","Office of Management and Budget (GitHub)","2025-01-23",{"url":222,"title":223,"publisher":219},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 federal agency AI use case inventory, individually reported use cases (raw data)",{"level":225,"checkedAt":191},"source-verified","B","ginnie-mae-subledger-data-quality-machine-learning",null,{"title":230,"useCases":231,"organization":232,"vendors":236,"summary":240,"stage":209,"year":241,"channels":242,"languages":243,"metrics":244,"outcomeDisclosed":245,"sources":246,"verification":252,"grade":254,"id":255,"organizationSlug":228},"Comrade Trustee Services: AI reconciliation for a defence force pension fund",[195],{"name":233,"anonymized":202,"country":234,"region":235,"industry":21},"Comrade Trustee Services","PG","asia-pacific",[237],{"name":238,"role":239},"Smartstream","platform","Comrade Trustee Services, trustee of the Defence Force Retirement Benefit Fund in Papua New Guinea, went live with Smartstream's Air AI reconciliation platform, which it uses to reconcile multiple file types, including fixed length files and PDFs that need advanced matching logic. The article says Air replaced manual data collection and spreadsheet preparation, and the vendor says processing time fell from up to eight hours to under five minutes.",2026,[31],[213],[],true,[247],{"url":248,"title":249,"publisher":250,"date":251},"https://www.assetservicingtimes.com/assetservicesnews/technologyarticle.php?article_id=18038","Comrade Trustee Services goes live with Smartstream’s Air","Asset Servicing Times","2026-06-11",{"level":225,"checkedAt":253},"2026-09-26","C","comrade-trustee-services-ai-reconciliation",{"title":257,"useCases":258,"organization":259,"vendors":262,"summary":264,"stage":209,"year":241,"channels":265,"languages":266,"metrics":267,"outcomeDisclosed":202,"sources":268,"verification":274,"grade":254,"id":275,"organizationSlug":228},"National Bank of Greece (Cyprus): four reconciliation systems consolidated on an AI enabled platform",[195],{"name":260,"anonymized":202,"country":261,"region":157,"industry":17},"National Bank of Greece (Cyprus)","CY",[263],{"name":238,"role":239},"National Bank of Greece in Cyprus consolidated four reconciliation systems into one on Smartstream's AI enabled Air platform (the Air Cash module), replacing both incumbent and standalone systems. The bank had a fragmented landscape that needed significant daily manual effort across systems and data formats. The platform matches groups of items at once and flags data quality issues in internal data and incoming bank statements. The project was completed in three months; no operational outcome figures were disclosed.",[31],[213],[],[269],{"url":270,"title":271,"publisher":272,"date":273},"https://thepaypers.com/fintech/news/national-bank-of-greece-in-cyprus-goes-live-with-smartstream-air","National Bank of Greece in Cyprus goes live with Smartstream Air","The Paypers","2026-05-28",{"level":225,"checkedAt":253},"national-bank-of-greece-cyprus-ai-reconciliation",{"title":277,"useCases":278,"organization":279,"vendors":281,"summary":282,"stage":283,"year":284,"channels":285,"languages":286,"metrics":287,"outcomeDisclosed":202,"sources":288,"verification":290,"grade":254,"id":291,"organizationSlug":228},"World Food Programme: DARTS machine learning reconciliation of cash transfers",[195],{"name":280,"anonymized":202,"region":151,"industry":22},"World Food Programme",[],"DARTS (Data Assurance and Reconciliation Tool Simplified) is a web application that uses machine learning to help World Food Programme country offices apply controls to large cash transfer datasets and generate reconciliation reports, so that humanitarian cash assistance is paid out accurately and accountably. A 2024 US federal AI use case inventory entry by the USAID Bureau for Humanitarian Assistance, dated April 2024, listed it at the implementation and assessment stage and said it was funded through the WFP Innovation Accelerator. It does not appear in the 2025 inventory, so its current status is unknown. No outcome figures are published.","pilot",2024,[31],[213],[],[289],{"url":217,"title":218,"publisher":219,"date":220},{"level":225,"checkedAt":191},"world-food-programme-darts-cash-transfer-reconciliation",0,[],{"low":295,"high":296},315000,2160000,[298,321,335,357,369,383],{"slug":185,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":303,"patterns":305,"audience":33,"autonomy":34,"adoptionStage":308,"segment":33,"evidenceCount":309,"publicEvidenceCount":309,"organizations":310,"bestGrade":226,"headline":313,"lastVerified":191,"indexable":245},"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.",[17,18],[25,304],"customer-service",[27,29,306,307],"classification-and-routing","content-generation","emerging",2,[311,312],"BNY","JPMorgan Chase",{"kpi":45,"label":314,"unit":315,"n":316,"nUpTo":292,"kind":317,"value":318,"qualifier":319,"claimant":320,"organization":311,"vendorReported":202},"Automation rate","percent",1,"reported",10,"at-least","organization",{"slug":186,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":326,"patterns":329,"audience":33,"autonomy":331,"adoptionStage":308,"segment":33,"evidenceCount":316,"publicEvidenceCount":316,"organizations":332,"bestGrade":226,"headline":228,"lastVerified":334,"indexable":245},"AI for fee and interest leakage detection","Fee and interest leakage","An independent verification layer that recomputes what each fee, FX margin, spread and interest charge should have been under the contract and pricing tables, compares it with what was actually billed, and surfaces overcharges and undercharges account by account for correction, customer remediation and revenue recovery.",[17,18,20],[24,327,328,25],"product-and-pricing","regulatory-compliance",[28,27,330],"rag-knowledge-assistant","copilot",[333],"State Bank of India","2026-09-28",{"slug":187,"title":336,"shortTitle":337,"definition":338,"status":9,"industries":339,"functions":342,"patterns":344,"audience":33,"autonomy":34,"adoptionStage":345,"segment":33,"evidenceCount":346,"publicEvidenceCount":347,"organizations":348,"bestGrade":226,"headline":353,"lastVerified":191,"indexable":245},"AI for supplier invoice processing in accounts payable","Supplier invoice processing","AI that captures supplier invoices from any format, extracts header and line data, matches them to purchase orders and goods receipts, proposes tax and cost centre coding, flags duplicates and suspected fraud, and routes them for approval and posting, leaving only exceptions to accounts payable staff.",[20,17,22,340,341],"retail-and-ecommerce","energy-and-utilities",[24,343],"procurement",[29,27,28,306],"mainstream",5,4,[349,350,351,352],"Federal Deposit Insurance Corporation","Kingfisher","U.S. Immigration and Customs Enforcement","Veolia",{"kpi":49,"label":354,"unit":315,"n":316,"nUpTo":316,"kind":317,"value":355,"qualifier":356,"claimant":320,"organization":350,"vendorReported":202},"Productivity gain",80,"exact",{"slug":188,"title":358,"shortTitle":359,"definition":360,"status":9,"industries":361,"functions":362,"patterns":364,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"evidenceCount":365,"publicEvidenceCount":309,"organizations":366,"bestGrade":226,"headline":228,"lastVerified":191,"indexable":245},"AI for chargeback and representment operations","Chargeback and representment","AI that runs the dispute engine room for issuers, acquirers and merchants: it maps each dispute to the network reason code, gathers the matching evidence, assembles a network compliant chargeback or representment package, drafts the rebuttal, tracks every deadline and processes pre dispute alerts so a refund can be issued before a chargeback lands.",[18,17,340],[25,363,304],"fraud-prevention",[27,29,307,306],3,[367,368],"GitHub","Visa",{"slug":189,"title":370,"shortTitle":371,"definition":372,"status":9,"industries":373,"functions":375,"patterns":377,"audience":379,"autonomy":331,"adoptionStage":308,"segment":33,"evidenceCount":309,"publicEvidenceCount":309,"organizations":380,"bestGrade":226,"headline":228,"lastVerified":191,"indexable":245},"AI for regulatory report assembly","Regulatory report assembly","AI that assembles periodic and data driven regulatory filings and returns, such as prudential and statistical returns, threshold and transaction reports and disclosure packs, by pulling data into the regulator's schema, validating it, reconciling figures to source, explaining movements against prior periods and drafting commentary, before a named officer reviews and submits. Narratives for individual suspicious activity cases are a separate use case.",[17,374,19,18],"insurance",[328,24,376],"financial-crime-compliance",[27,28,307,378],"summarization","employee-facing",[381,382],"Board of Governors of the Federal Reserve System","National Credit Union Administration",{"slug":190,"title":384,"shortTitle":385,"definition":386,"status":9,"industries":387,"functions":390,"patterns":393,"audience":379,"autonomy":396,"adoptionStage":35,"segment":397,"evidenceCount":346,"publicEvidenceCount":346,"organizations":398,"bestGrade":226,"headline":403,"lastVerified":191,"indexable":245},"AI cash flow forecasting for corporate treasury","Treasury cash forecasting","Machine learning and conversational analytics, offered by some banks inside their cash management platforms, that categorise a company's cash flows, forecast positions across accounts and currencies, and answer treasurers' questions in plain language, so the treasury team decides on funding and idle balances with better information and less spreadsheet work.",[17,20,388,340,389],"logistics-and-transportation","manufacturing",[391,24,392],"treasury","analytics-and-reporting",[394,306,395,27],"prediction-and-scoring","conversational-agent","assist","specialized-businesses",[399,400,401,312,402],"Amtrak","Bank of America","Domino's Pizza","Prysmian",{"kpi":49,"label":354,"unit":315,"n":309,"nUpTo":316,"kind":317,"value":404,"qualifier":405,"claimant":320,"organization":312,"vendorReported":202},90,"approximately",{"indexable":245,"reasons":407},[],[409,415,421,427,434,439,446,453,460,466,473,479,486,493,499,504,511,516,522,528,534,540,545,550,555,562,569,574,579,586,591,597,603,608],{"id":143,"label":410,"issuer":411,"region":157,"url":412,"description":413,"useCases":414,"indexable":245},"EU AI Act","European Union","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":416,"label":417,"issuer":411,"region":157,"url":418,"description":419,"useCases":420,"indexable":245},"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":146,"label":422,"issuer":423,"region":151,"url":424,"description":425,"useCases":426,"indexable":245},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":428,"label":429,"issuer":430,"region":163,"url":431,"description":432,"useCases":433,"indexable":245},"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":144,"label":435,"issuer":411,"region":157,"url":436,"description":437,"useCases":438,"indexable":245},"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":440,"label":441,"issuer":442,"region":157,"url":443,"description":444,"useCases":445,"indexable":245},"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":447,"label":448,"issuer":449,"region":157,"url":450,"description":451,"useCases":452,"indexable":245},"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":454,"label":455,"issuer":456,"region":235,"url":457,"description":458,"useCases":459,"indexable":245},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":145,"label":461,"issuer":462,"region":235,"url":463,"description":464,"useCases":465,"indexable":245},"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":467,"label":468,"issuer":469,"region":151,"url":470,"description":471,"useCases":472,"indexable":245},"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":474,"label":475,"issuer":476,"region":163,"url":477,"description":478,"useCases":472,"indexable":245},"us-sr-11-7","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":480,"label":481,"issuer":482,"region":157,"url":483,"description":484,"useCases":485,"indexable":245},"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":487,"label":488,"issuer":489,"region":151,"url":490,"description":491,"useCases":492,"indexable":245},"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":494,"label":495,"issuer":411,"region":157,"url":496,"description":497,"useCases":498,"indexable":245},"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":500,"label":501,"issuer":411,"region":157,"url":502,"description":503,"useCases":498,"indexable":245},"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":505,"label":506,"issuer":507,"region":163,"url":508,"description":509,"useCases":510,"indexable":245},"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":512,"label":513,"issuer":411,"region":157,"url":514,"description":515,"useCases":70,"indexable":245},"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.",{"id":517,"label":518,"issuer":519,"region":163,"url":520,"description":521,"useCases":70,"indexable":245},"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":523,"label":524,"issuer":525,"region":151,"url":526,"description":527,"useCases":70,"indexable":245},"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":529,"label":530,"issuer":411,"region":157,"url":531,"description":532,"useCases":533,"indexable":245},"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":535,"label":536,"issuer":537,"region":163,"url":538,"description":539,"useCases":533,"indexable":245},"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":541,"label":542,"issuer":456,"region":235,"url":543,"description":544,"useCases":318,"indexable":245},"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":546,"label":547,"issuer":411,"region":157,"url":548,"description":549,"useCases":318,"indexable":245},"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":551,"label":552,"issuer":411,"region":157,"url":553,"description":554,"useCases":318,"indexable":245},"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":556,"label":557,"issuer":558,"region":157,"url":559,"description":560,"useCases":561,"indexable":245},"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":563,"label":564,"issuer":565,"region":163,"url":566,"description":567,"useCases":568,"indexable":245},"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":570,"label":571,"issuer":411,"region":157,"url":572,"description":573,"useCases":568,"indexable":245},"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":575,"label":576,"issuer":411,"region":157,"url":577,"description":578,"useCases":69,"indexable":245},"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.",{"id":580,"label":581,"issuer":582,"region":583,"url":584,"description":585,"useCases":346,"indexable":245},"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.",{"id":587,"label":588,"issuer":589,"region":157,"url":158,"description":590,"useCases":347,"indexable":245},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","UK model risk management principles for banks, covering AI and machine learning models.",{"id":592,"label":593,"issuer":594,"region":157,"url":595,"description":596,"useCases":347,"indexable":245},"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":598,"label":599,"issuer":600,"region":235,"url":601,"description":602,"useCases":365,"indexable":245},"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":604,"label":605,"issuer":411,"region":157,"url":606,"description":607,"useCases":365,"indexable":245},"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":609,"label":610,"issuer":611,"region":163,"url":612,"description":613,"useCases":365,"indexable":245},"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.",1790598300409]