[{"data":1,"prerenderedAt":503},["ShallowReactive",2],{"uc-fee-and-interest-leakage-detection":3,"uc-regulations":294},{"useCase":4,"evidence":187,"blitsAiDeployments":221,"benchmarks":222,"indicative":223,"related":226,"indexability":292,"includeUnpublished":193},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":26,"channels":30,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"problem":36,"problemStats":37,"howItWorks":43,"valueDrivers":44,"kpis":49,"indicativeValue":54,"macroEstimates":82,"feasibility":83,"implementation":97,"risk":136,"blitsAi":165,"faq":167,"related":177,"datePublished":182,"dateModified":182,"lastVerified":182,"changelog":183,"slug":186},"AI for fee and interest leakage detection","Fee and interest leakage","AI for bank fee and interest leakage detection","How AI can help banks recompute fees, interest and FX margins against contract terms and flag overcharges and undercharges for correction and remediation.","published","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.",[12,13,14,15,16],"fee leakage detection","revenue assurance for banks","interest and fee recalculation","pricing error detection","income leakage detection",[18,19,20],"banking","payments","cross-industry",[22,23,24,25],"finance-and-accounting","product-and-pricing","regulatory-compliance","operations",[27,28,29],"anomaly-detection","agentic-workflow","rag-knowledge-assistant",[31,32],"internal-tools","api","back-office","copilot","emerging","Banks charge through many systems: core banking, card platforms, loan servicing, trade finance,\nFX and payments engines, each with its own pricing tables, waivers and exceptions. Over time the\nconfigured prices drift from what contracts, product terms and negotiated deals say. Some\ncustomers are overcharged, which is a conduct risk that can end in remediation programmes and fines.\nOthers are undercharged, which can leak revenue across many transactions.\n\nSome of these errors come to light only when a customer complains, an audit samples the right\naccounts or a regulator investigates, sometimes years after the error began. Recomputing every\ncharge independently was too expensive to do by hand. Cheap compute, contract reading with\nlanguage models and anomaly detection on fee lines can make continuous checking practical.",[38],{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"The CFPB ordered Wells Fargo in 2022 to pay more than USD 2 billion in redress across more than 16 million consumer accounts, for harms that included fees and interest improperly charged on auto and mortgage loans and incorrect charges on deposit accounts.","CFPB Orders Wells Fargo to Pay $3.7 Billion for Widespread Mismanagement of Auto Loans, Mortgages, and Deposit Accounts","https://www.consumerfinance.gov/archive/newsroom/cfpb-orders-wells-fargo-to-pay-37-billion-for-widespread-mismanagement-of-auto-loans-mortgages-and-deposit-accounts/",2022,"1. **Build the price book.** Contract terms, product disclosure documents, negotiated pricing and\n   waivers are read and turned into a structured, versioned price book. Language models help\n   extract terms from contracts; people approve every entry.\n2. **Recompute independently.** For each account and period, the engine recomputes what should\n   have been charged (fees, interest accruals, FX margins, spreads) from the price book and the\n   transaction data, outside the billing systems.\n3. **Compare and detect.** It compares expected with actual charges line by line and runs anomaly\n   detection on fee lines to catch patterns the rules miss, such as a waiver that never expired.\n4. **Explain.** For each discrepancy it produces an explanation (which term, which system, since\n   when, how many accounts) so the product owner can decide quickly.\n5. **Correct and remediate.** Confirmed errors go to the owners of the billing configuration for\n   a fix, and to a remediation process that refunds customers or recovers undercharges, with\n   human approval.",[45,46,47,48],"risk-reduction","compliance","revenue-growth","customer-experience",[50,51,52,53],"detection-rate-improvement","error-reduction","cost-savings","accuracy",{"referenceOrg":55,"inputs":56,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A bank with USD 500 million in annual fee and commission income",[57,63,70],{"key":58,"label":59,"low":60,"high":60,"unit":61,"note":62},"feeIncome","Annual fee and commission income",500000000,"USD per year","The reference bank. Replace with your own fee and commission income.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"leakageRate","Share of fee income lost to undercharging",0.002,0.01,"fraction of fee income","Editorial assumption, replace with the results of a sample recomputation on your own accounts. No verified public benchmark was found.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"recoveryShare","Share of leakage found and fixed going forward",0.3,0.6,"fraction of leakage","Editorial assumption; some leakage is found but deliberately left in place, for example commercial waivers.","feeIncome * leakageRate * recoveryShare","USD","per year","Fee income recovered from undercharging","Undercharging only. It leaves out the benefit of finding overcharges early (smaller remediation programmes, fewer penalties), and the cost of building the price book, the platform and the remediation process.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":91},"high","The recompute logic must reproduce interest and fee conventions exactly (day count, rounding, tiering, value dates), and the contract terms are scattered across documents and systems. The AI helps with reading and detecting; the hard part is a trusted, versioned price book.",[87,88,89,90],"Contracts, product terms and negotiated pricing, with their effective dates","Transaction, balance and charge data per account from each billing system","Waiver and exception records with approvals and expiry dates","Past remediation cases as labelled examples",[92,93,94,95,96],"Core banking, card, loan servicing and payments systems (read only)","Contract and document management","Pricing and deal management tools","Remediation and complaints case management","General ledger for recovered income",{"steps":98,"guardrails":114,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[99,102,105,108,111],{"title":100,"detail":101},"Start with one product and a sample","Pick a product with complex pricing and many accounts, such as business accounts or trade finance, and recompute a sample by hand and by machine to prove the logic.",{"title":103,"detail":104},"Build the price book with owners","Extract terms with AI assistance, but have product owners approve every entry and its effective dates. The price book becomes a control in its own right.",{"title":106,"detail":107},"Run continuously, report monthly","Recompute every account each cycle and report discrepancies by root cause, not only by account, so configuration errors are fixed once.",{"title":109,"detail":110},"Connect to remediation","Agree with compliance how confirmed overcharges become remediation cases, and how customers are contacted and refunded.",{"title":112,"detail":113},"Extend to more systems","Add products and systems one by one, reusing the price book structure and the recompute engine.",[115,116,117,118],"The recompute logic is deterministic, versioned and documented; the model never calculates charges","Every price book entry has an owner, a source document and an effective date","Corrections to customer accounts need human approval and are logged","Overcharges are always escalated to remediation, never netted against undercharges","Product owners approve the price book and decide on each class of discrepancy. Remediation teams approve refunds and customer contact, and finance approves recovery of undercharged income. Internal audit reviews the recompute logic periodically.",[121,122,123,124,125],"Discrepancies found per product and root cause","Value of overcharges refunded and undercharges recovered","Time from error start to detection","Share of discrepancies confirmed as real on review","Remediation cases opened from the control versus from complaints",[127,130,133],{"title":128,"detail":129},"False alarms from convention mismatches","The recompute uses a different day count or rounding than the billing system, so every account looks wrong. Validate conventions per product before scaling.",{"title":131,"detail":132},"Findings without owners","Discrepancies pile up because no one owns the fix. Assign each product and system an accountable owner before switching on.",{"title":134,"detail":135},"Quietly keeping overcharges","Commercial pressure favours recovering undercharges over refunding overcharges. Make overcharge remediation a mandatory, audited path.",{"euAiAct":137,"regulations":140,"guidance":146,"controls":159,"incidents":164},{"tier":138,"basis":139},"minimal","Verifying charges against contracts is not listed in Annex III and is not a practice prohibited by Article 5. The system is internal, so the Article 50(1) duty to tell people they are dealing with AI does not arise; the Article 50(2) duty to mark generated text, such as the discrepancy explanations, falls on the provider of the generative model or system. It supports, but does not take, decisions about individual customers; remediation decisions stay with people.",[141,142,143,144,145],"eu-ai-act","uk-consumer-duty","gdpr","apra-cps-230","us-sr-11-7",[147,153],{"title":148,"issuer":149,"region":150,"url":151,"note":152},"RG 277 Consumer remediation","Australian Securities and Investments Commission","asia-pacific","https://asic.gov.au/regulatory-resources/find-a-document/regulatory-guides/rg-277-consumer-remediation/","ASIC guidance, issued in 2022, for financial services and credit licensees on running consumer remediation, including identifying affected customers and returning money, for example after wrong fees or charges.",{"title":154,"issuer":155,"region":156,"url":157,"note":158},"FCA Consumer Duty","Financial Conduct Authority","europe","https://www.fca.org.uk/firms/consumer-duty/about","The FCA's overview of the Duty, including the price and value outcome and its fair value assessments; charges above the agreed terms work against fair value.",[160,161,162,163],"Price book under change control with owners and effective dates","Versioned recompute logic with documented conventions and independent validation","Log of every discrepancy, decision, correction and refund","The control itself inventoried and monitored, with coverage reported by product",[],{"howToBuild":166},"On Blits.ai the scheduled part runs as an **agentic workflow**: **custom functions** run the\ndeterministic recompute in isolated custom code or call the bank's own engine, **SQL knowledge\nbases** give read access to charge and transaction data, and an **AI agent** with **structured\noutput** explains each discrepancy and groups them by root cause. Contract terms and product\ndocuments sit in the **knowledge base** with hybrid retrieval, so the agent can retrieve the\nclause behind each explanation and include it in its structured output.\n\n**Human in the loop approval** holds every correction or refund for the product owner, each run\nkeeps a **full audit trail**, and **agentic tasks** recheck open discrepancies on a schedule\nuntil they are resolved. The platform is model agnostic and runs in EU or UAE regions where\ndata must stay local.",[168,171,174],{"question":169,"answer":170},"Why not rely on the billing systems to charge correctly?","Because configuration can drift from contracts over years, across many systems, and errors can be found only through complaints, audits or regulators. Enforcement cases such as the CFPB's 2022 order against Wells Fargo, which covered fees and interest improperly charged on loans, show how large the consequences can become.",{"question":172,"answer":173},"Does the AI calculate the correct charges?","No. The recompute logic is deterministic and versioned. AI helps read contracts into the price book, detect unusual fee patterns and explain discrepancies, but the numbers come from rules that product owners approve.",{"question":175,"answer":176},"Are there public examples of banks doing this with AI?","Few, and they disclose little. State Bank of India's 2019-20 annual report lists models to identify income leakage among the machine learning models its Analytics Department built in house, and an article by an SBI chief manager reports processing and facility fees recovered in two fiscal years. Revenue assurance and pricing vendors describe similar work, but the case studies we checked either do not name the bank or do not say the detection uses AI.",[178,179,180,181],"ledger-and-payment-reconciliation","complaints-root-cause-analysis","chargeback-and-representment","continuous-controls-testing","2026-09-28",[184],{"date":182,"note":185},"First published","fee-and-interest-leakage-detection",[188],{"title":189,"useCases":190,"organization":191,"vendors":195,"summary":198,"stage":199,"year":200,"channels":201,"languages":202,"metrics":204,"outcomeDisclosed":205,"sources":206,"verification":216,"grade":218,"id":219,"organizationSlug":220},"State Bank of India: machine learning models to identify income leakage",[186],{"name":192,"anonymized":193,"country":194,"region":150,"industry":18},"State Bank of India",false,"IN",[196],{"name":192,"role":197},"in-house","State Bank of India's Analytics Department builds machine learning models in house, and the bank's 2019-20 annual report lists models to identify income leakage among them, next to fraud, early warning and lead models. An article by an SBI chief manager in the journal of the Indian Institute of Banking and Finance (October to December 2021), citing the bank's Analytics Department, reports processing fees and facility fees recovered through this work in fiscal years 2018-19 and 2019-20. Neither source explains how the models work, how many accounts they cover or whether customers who were overcharged were also identified.","production",2020,[31],[203],"en",[],true,[207,211],{"url":208,"title":209,"publisher":192,"date":210},"https://sbi.bank.in/corporate/AR1920/download_center/english/11-4.2-Information%20Technology.pdf","Annual Report 2019-20, Directors' Report: Information Technology","2020-06-23",{"url":212,"title":213,"publisher":214,"archivedUrl":215},"https://www.iibf.org.in/documents/BankQuest/Articles/5.Role%20of%20Aritificial%20Intelligence%20and%20Analytics%20in%20Banking%20-%20Kommana%20V%20Ganesh%20Kumar.pdf","Role of Artificial Intelligence & Analytics in Banking","Indian Institute of Banking & Finance (Bank Quest)","https://web.archive.org/web/20230314164128/http://www.iibf.org.in/documents/BankQuest/Articles/5.Role%20of%20Aritificial%20Intelligence%20and%20Analytics%20in%20Banking%20-%20Kommana%20V%20Ganesh%20Kumar.pdf",{"level":217,"checkedAt":182},"source-verified","B","state-bank-of-india-income-leakage-models",null,0,[],{"low":224,"high":225},300000,3000000,[227,247,266,280],{"slug":178,"title":228,"shortTitle":229,"definition":230,"status":9,"industries":231,"functions":235,"patterns":236,"audience":33,"autonomy":238,"adoptionStage":239,"segment":33,"evidenceCount":240,"publicEvidenceCount":240,"organizations":241,"bestGrade":218,"headline":220,"lastVerified":246,"indexable":205},"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.",[18,19,232,20,233,234],"capital-markets","wealth-and-asset-management","government",[22,25],[28,27,237],"document-processing","supervised-agent","early-adopters",4,[242,243,244,245],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme","2026-09-27",{"slug":179,"title":248,"shortTitle":249,"definition":250,"status":9,"industries":251,"functions":254,"patterns":257,"audience":33,"autonomy":34,"adoptionStage":35,"segment":260,"evidenceCount":261,"publicEvidenceCount":261,"organizations":262,"bestGrade":218,"headline":220,"lastVerified":246,"indexable":205},"AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[20,18,252,19,253,234],"insurance","telecommunications",[24,255,256],"customer-service","analytics-and-reporting",[258,259,28,29],"classification-and-routing","summarization","second-line",3,[263,264,265],"Centers for Medicare and Medicaid Services","Board of Governors of the Federal Reserve System","Federal Trade Commission",{"slug":180,"title":267,"shortTitle":268,"definition":269,"status":9,"industries":270,"functions":272,"patterns":274,"audience":33,"autonomy":238,"adoptionStage":239,"segment":33,"evidenceCount":261,"publicEvidenceCount":276,"organizations":277,"bestGrade":218,"headline":220,"lastVerified":246,"indexable":205},"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.",[19,18,271],"retail-and-ecommerce",[25,273,255],"fraud-prevention",[28,237,275,258],"content-generation",2,[278,279],"GitHub","Visa",{"slug":181,"title":281,"shortTitle":282,"definition":283,"status":9,"industries":284,"functions":285,"patterns":287,"audience":33,"autonomy":238,"adoptionStage":35,"segment":260,"evidenceCount":261,"publicEvidenceCount":261,"organizations":288,"bestGrade":218,"headline":220,"lastVerified":246,"indexable":205},"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.",[20,18,252,232,234],[286,24,25],"risk-management",[28,237,27,258],[289,290,291],"Federal Deposit Insurance Corporation","U.S. Department of the Interior","Pension Benefit Guaranty Corporation",{"indexable":205,"reasons":293},[],[295,301,306,314,322,328,335,339,346,352,359,364,371,378,384,389,396,402,408,414,420,426,432,437,442,449,456,461,467,475,481,487,493,498],{"id":141,"label":296,"issuer":297,"region":156,"url":298,"description":299,"useCases":300,"indexable":205},"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":143,"label":302,"issuer":297,"region":156,"url":303,"description":304,"useCases":305,"indexable":205},"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":307,"label":308,"issuer":309,"region":310,"url":311,"description":312,"useCases":313,"indexable":205},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":315,"label":316,"issuer":317,"region":318,"url":319,"description":320,"useCases":321,"indexable":205},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":323,"label":324,"issuer":297,"region":156,"url":325,"description":326,"useCases":327,"indexable":205},"dora","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":329,"label":330,"issuer":331,"region":156,"url":332,"description":333,"useCases":334,"indexable":205},"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":142,"label":154,"issuer":155,"region":156,"url":336,"description":337,"useCases":338,"indexable":205},"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":340,"label":341,"issuer":342,"region":150,"url":343,"description":344,"useCases":345,"indexable":205},"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":144,"label":347,"issuer":348,"region":150,"url":349,"description":350,"useCases":351,"indexable":205},"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":353,"label":354,"issuer":355,"region":310,"url":356,"description":357,"useCases":358,"indexable":205},"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":145,"label":360,"issuer":361,"region":318,"url":362,"description":363,"useCases":358,"indexable":205},"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":365,"label":366,"issuer":367,"region":156,"url":368,"description":369,"useCases":370,"indexable":205},"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":372,"label":373,"issuer":374,"region":310,"url":375,"description":376,"useCases":377,"indexable":205},"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":379,"label":380,"issuer":297,"region":156,"url":381,"description":382,"useCases":383,"indexable":205},"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":385,"label":386,"issuer":297,"region":156,"url":387,"description":388,"useCases":383,"indexable":205},"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":390,"label":391,"issuer":392,"region":318,"url":393,"description":394,"useCases":395,"indexable":205},"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":397,"label":398,"issuer":297,"region":156,"url":399,"description":400,"useCases":401,"indexable":205},"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":403,"label":404,"issuer":405,"region":318,"url":406,"description":407,"useCases":401,"indexable":205},"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":409,"label":410,"issuer":411,"region":310,"url":412,"description":413,"useCases":401,"indexable":205},"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":415,"label":416,"issuer":297,"region":156,"url":417,"description":418,"useCases":419,"indexable":205},"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":421,"label":422,"issuer":423,"region":318,"url":424,"description":425,"useCases":419,"indexable":205},"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":427,"label":428,"issuer":342,"region":150,"url":429,"description":430,"useCases":431,"indexable":205},"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.",10,{"id":433,"label":434,"issuer":297,"region":156,"url":435,"description":436,"useCases":431,"indexable":205},"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":438,"label":439,"issuer":297,"region":156,"url":440,"description":441,"useCases":431,"indexable":205},"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":443,"label":444,"issuer":445,"region":156,"url":446,"description":447,"useCases":448,"indexable":205},"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":450,"label":451,"issuer":452,"region":318,"url":453,"description":454,"useCases":455,"indexable":205},"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":457,"label":458,"issuer":297,"region":156,"url":459,"description":460,"useCases":455,"indexable":205},"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":462,"label":463,"issuer":297,"region":156,"url":464,"description":465,"useCases":466,"indexable":205},"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":468,"label":469,"issuer":470,"region":471,"url":472,"description":473,"useCases":474,"indexable":205},"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":476,"label":477,"issuer":478,"region":156,"url":479,"description":480,"useCases":240,"indexable":205},"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":482,"label":483,"issuer":484,"region":156,"url":485,"description":486,"useCases":240,"indexable":205},"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":488,"label":489,"issuer":490,"region":150,"url":491,"description":492,"useCases":261,"indexable":205},"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":494,"label":495,"issuer":297,"region":156,"url":496,"description":497,"useCases":261,"indexable":205},"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":499,"label":500,"issuer":265,"region":318,"url":501,"description":502,"useCases":261,"indexable":205},"us-fcra","Fair Credit Reporting Act","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.",1790598299925]