[{"data":1,"prerenderedAt":579},["ShallowReactive",2],{"uc-adverse-action-explanations":3,"uc-regulations":375},{"useCase":4,"evidence":214,"blitsAiDeployments":269,"benchmarks":270,"indicative":271,"related":274,"indexability":373,"includeUnpublished":220},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":18,"patterns":22,"channels":26,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":42,"indicativeValue":47,"macroEstimates":81,"feasibility":82,"implementation":95,"risk":134,"blitsAi":191,"faq":193,"related":203,"datePublished":209,"dateModified":209,"lastVerified":209,"changelog":210,"slug":213},"AI drafted explanations for credit declines and adverse actions","Adverse action explanations","AI adverse action notices for credit declines","AI can draft credit decline notices from the model's own reason codes. Regulation B requires specific reasons, and EU law an intelligible account of the scoring.","published","An assistant that turns the reason codes of a credit model into an accurate, specific and readable explanation of a decline, reduced limit or repricing for the customer, and a matching internal rationale for the file, without adding any reason the model did not produce.",[12,13,14],"adverse action notice drafting","credit decline explanation","decision explanation assistant",[16,17],"banking","payments",[19,20,21],"lending-and-credit","regulatory-compliance","customer-service",[23,24,25],"content-generation","rag-knowledge-assistant","conversational-agent",[27,28,29,30],"email","mobile-app","web-chat","agent-desktop","employee-facing","copilot","emerging","lending","When a lender declines an application, cuts a credit line or offers worse terms, it often has to\ntell the customer why: in the US, Regulation B requires the principal reasons for a decline or other\nadverse action, and EU law gives people a right to an explanation of automated credit assessments.\nIn practice a notice can be little more than a list of checked reasons from a sample form (\"limited\ncredit experience\", \"excessive obligations in relation to income\") that says little to the customer\nand may not reflect what really drove the decision. Complex\nmachine learning models make this harder: a model can weigh many features, and the reasons printed\nmust still be the principal ones and must be accurate.\n\nThe rules are explicit. In the US, Regulation B requires a statement of reasons that is specific\nand indicates the principal reasons, and says that checking the closest reason on a sample form is\nnot enough when it is not the factor actually used. The CFPB's 2022 circular stating that this\napplies equally to complex algorithms was withdrawn in May 2025 together with many other CFPB\nguidance documents, but the adverse action notice requirements in 12 CFR 1002.9 and their official\ninterpretation are unchanged. In the EU, the Court of Justice ruled in February 2025 (Dun &\nBradstreet Austria) that a person subject to an automated credit assessment is entitled to an\nexplanation of the procedure and principles actually applied, and the EU AI Act adds a right to an\nexplanation for decisions based on high risk systems such as credit scoring. Vague or inaccurate\nexplanations leave customers without a clear next step and the lender exposed on compliance.",[],"1. **Take the decision record.** The assistant receives the decision, the reason codes and their\n   ranking from the decision engine, and the relevant customer and product data.\n2. **Retrieve approved wording.** For each reason code it retrieves the approved plain language\n   description and, where allowed, what the customer could do about it.\n3. **Draft within strict limits.** A generative model composes the notice and a short internal\n   rationale using only those reasons, in the customer's language and at an agreed reading level.\n4. **Check automatically.** A second pass verifies that every reason in the draft maps to a code\n   from the decision, that no code is missing, and that no prohibited content appears.\n5. **Review and send.** A reviewer approves the draft, or approved templates go out automatically\n   once quality is proven. Follow up questions go to an assistant limited to the same reasons, with\n   a route to a person.",[39,40,41],"compliance","customer-experience","employee-productivity",[43,44,45,46],"accuracy","error-reduction","time-saved-per-task","handling-time-reduction",{"referenceOrg":48,"inputs":49,"formula":76,"currency":77,"period":78,"resultLabel":79,"caveat":80},"A consumer lender issuing 100,000 adverse action notices a year",[50,56,63,70],{"key":51,"label":52,"low":53,"high":53,"unit":54,"note":55},"notices","Adverse action notices per year",100000,"notices per year","The reference lender.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"followUpShare","Share of notices that lead to a question, complaint or reconsideration request",0.05,0.1,"fraction of notices","Editorial assumption. Replace with your own contact and complaint data.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"minutesSaved","Minutes saved per follow up case with a clear explanation and a prepared rationale",20,40,"minutes per case","Editorial assumption for looking up the decision and writing a response. Replace with your own time study.",{"key":71,"label":72,"low":67,"high":73,"unit":74,"note":75},"hourlyCost","Fully loaded cost of a lending operations hour",60,"USD per hour","Editorial assumption.","notices * followUpShare * minutesSaved / 60 * hourlyCost","USD","per year","Operations effort saved on decline follow ups","Counts handling effort only. The larger value, lower regulatory and fair lending risk and fewer repeat failed applications, is real but hard to price and is left out.",[],{"complexity":83,"complexityNote":84,"dataPrerequisites":85,"integrations":90},"medium","Drafting text is easy. The hard parts are reliable reason codes from the model, a reason library that compliance owns, and automated checks that make it impossible to state a reason the model did not produce.",[86,87,88,89],"Ranked reason codes for every adverse decision from the decision engine","An approved library of plain language descriptions per reason code, per language","Notice templates and regulatory content requirements per product and market","Samples of past notices and complaints for testing",[91,92,93,94],"Decision engine or loan origination system","Document generation and correspondence system","Complaint and case management","Customer channels for delivery and follow up questions",{"steps":96,"guardrails":112,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":124},[97,100,103,106,109],{"title":98,"detail":99},"Fix the reason codes first","Confirm with model risk that the model produces specific, ranked principal reasons that are accurate for each decision. The assistant cannot repair weak reasons.",{"title":101,"detail":102},"Build the reason library","For every code, compliance approves a customer description, an internal description and, where appropriate, a note on what the customer could change. Keep owners and review dates.",{"title":104,"detail":105},"Constrain the generation","Pass only the decision's codes and approved descriptions to the model and forbid any other reason. Use templates for the legally required parts of the notice.",{"title":107,"detail":108},"Verify every draft","Add an automated check that maps each stated reason back to a code and blocks the draft on any mismatch, omission or prohibited term.",{"title":110,"detail":111},"Review, measure, then automate","Start with human review of every draft, measure the error rate, and allow automatic sending per product only when the error rate is proven near zero.",[113,114,115,116,117],"The draft may only contain reasons present in the decision record","Every principal reason in the decision record must appear in the notice","No protected characteristic or proxy may appear as a reason","Legally required elements come from templates, not from generation","Every sent notice is stored with the decision record and the codes it was built from","Compliance owns the reason library and approves templates. Reviewers approve drafts until the measured error rate allows automatic sending, and a human answers any dispute or reconsideration request.",[120,121,122,123],"Share of drafts that pass the automated reason check first time","Error rate found in human review and in sampling after launch","Time to issue a notice after the decision","Complaints and reconsideration requests that mention unclear reasons",[125,128,131],{"title":126,"detail":127},"Invented or softened reasons","A fluent model adds a plausible reason or blurs the real one. Block any reason that does not map to a code.",{"title":129,"detail":130},"Reasons that are accurate but useless","Codes that describe model internals mean nothing to customers. Invest in the reason library, not only the model.",{"title":132,"detail":133},"Drift between model and library","A model update adds or renames features and the library falls behind. Tie library review to model change control.",{"euAiAct":135,"regulations":138,"guidance":146,"controls":185,"incidents":190},{"tier":136,"basis":137},"context-dependent","The drafting assistant does not assess creditworthiness, so on its own it is not the Annex III point 5(b) credit scoring system. It helps the lender meet the Article 86 right of affected people to a clear and meaningful explanation of decisions based on such a high risk system. If it is built into the scoring system it shares that system's high risk obligations; as a separate drafting tool its tier depends on its design and on how its output is reviewed. The follow up chat assistant must tell customers they are dealing with an AI system (Article 50).",[139,140,141,142,143,144,145],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","eba-loan-origination","us-ecoa-reg-b","us-fcra",[147,153,157,161,165,169,175,180],{"title":148,"issuer":149,"region":150,"url":151,"note":152},"Regulation B, 12 CFR 1002.9: notifications, with official interpretation","Consumer Financial Protection Bureau","north-america","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","The statement of reasons must be specific and indicate the principal reasons, must describe the factors actually considered or scored, and may not leave out a factor that was a principal reason.",{"title":154,"issuer":149,"region":150,"url":155,"note":156},"Regulation B, Appendix C: sample notification forms","https://www.consumerfinance.gov/rules-policy/regulations/1002/c/","Checking the closest reason on a sample form does not satisfy the notice requirement when it is not the factor actually used.",{"title":158,"issuer":149,"region":150,"url":159,"note":160},"Consumer Financial Protection Circular 2022-03: adverse action notification requirements for credit decisions based on complex algorithms","https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-in-connection-with-credit-decisions-based-on-complex-algorithms/","Stated that the specific reasons requirement applies equally to complex or opaque models. Withdrawn by the CFPB in May 2025 together with Circular 2023-03 on sample forms; the notice requirements in 12 CFR 1002.9 it interprets are unchanged.",{"title":162,"issuer":149,"region":150,"url":163,"note":164},"Interpretive rules, policy statements, and advisory opinions; withdrawal","https://www.federalregister.gov/documents/2025/05/12/2025-08286/interpretive-rules-policy-statements-and-advisory-opinions-withdrawal","The May 2025 Federal Register notice that withdrew many CFPB guidance documents, including Circulars 2022-03 and 2023-03 on adverse action notices.",{"title":166,"issuer":149,"region":150,"url":167,"note":168},"Innovation spotlight: providing adverse action notices when using AI/ML models","https://www.consumerfinance.gov/about-us/blog/innovation-spotlight-providing-adverse-action-notices-when-using-ai-ml-models/","An earlier CFPB blog on how the adverse action rules apply to machine learning models. The page now carries a notice that it describes the requirements incompletely.",{"title":170,"issuer":171,"region":172,"url":173,"note":174},"Case C-203/22, Dun & Bradstreet Austria: automated credit assessment and the right to an explanation","Court of Justice of the European Union","europe","https://curia.europa.eu/jcms/upload/docs/application/pdf/2025-02/cp250022en.pdf","Under the GDPR the controller must describe the procedure and principles actually applied so the person can understand which personal data were used and how; handing over the algorithm is not enough.",{"title":176,"issuer":177,"region":172,"url":178,"note":179},"Article 86, right to explanation of individual decision making","European Union","https://artificialintelligenceact.eu/article/86/","People affected by a deployer's decision based on an Annex III high risk system (except critical infrastructure, point 2) that has legal or similarly significant adverse effects on them can ask for a clear and meaningful explanation.",{"title":181,"issuer":182,"region":150,"url":183,"note":184},"SR 26-2: Revised Guidance on Model Risk Management","Board of Governors of the Federal Reserve System, OCC and FDIC","https://www.federalreserve.gov/supervisionreg/srletters/SR2602.htm","Interagency model risk management guidance of April 2026 that supersedes and replaces SR 11-7. It applies to the credit model whose reason codes the notice is built from, and is expected to be most relevant to banking organizations with over $30 billion in total assets.",[186,187,188,189],"Reason library under compliance ownership with version history","Automated reason to code reconciliation on every draft","Sampling of sent notices against decision records, reported to compliance","Change control that links model releases to reason library review",[],{"howToBuild":192},"On Blits.ai this is an **agentic workflow** triggered by the decision engine through an API token.\nA **custom function** fetches the decision record and reason codes, the **knowledge base** holds\nthe approved reason library and notice templates, and an **agent** with **structured output**\ndrafts the notice and the internal rationale. A deterministic **custom function** then checks that\nevery stated reason maps to a code, and **human in the loop approval** holds the draft until a\nreviewer releases it.\n\nCustomers who reply or ask in chat reach an **agent** that answers only from the same decision\nrecord and hands over to a person through **human handover** for disputes. **Guardrails** block\nprohibited terms, **PII masking** protects applicant data, and **test suites** with LLM based\ngrading check each release against a set of real decision records. The platform is model agnostic,\nso the drafting model can be changed without rebuilding the workflow.",[194,197,200],{"question":195,"answer":196},"Can a generative model write adverse action notices?","It can draft them, but only from the reason codes the credit model produced and with an automated check that nothing was added or left out. The legally required parts should come from templates.",{"question":198,"answer":199},"Does using AI change the duty to explain credit decisions?","No. In the US, Regulation B requires specific reasons that describe the factors actually considered or scored, whatever the technology. The CFPB withdrew its 2022 circular on complex algorithms in May 2025, but the notice requirements in 12 CFR 1002.9 and their official interpretation are unchanged. In the EU, the Court of Justice has ruled that a person subject to an automated credit assessment must be told the procedure and principles actually applied, in an intelligible way, and the EU AI Act adds a right to an explanation for decisions based on high risk systems such as credit scoring.",{"question":201,"answer":202},"Which named lenders have this documented?","Wells Fargo Bank holds a patent, granted in October 2022, for an adverse action methodology that ranks the characteristics of a machine learning credit risk model to identify the principal reasons behind a denial. Trade press reported in August 2021 that a Wells Fargo team had begun deploying an explainability technique for its deep learning credit models. Discover Financial Services holds a related patent, granted in July 2024, that turns Shapley based explanations of a credit model into adverse action reason codes. Both are the lender's own patent filings rather than a disclosed error rate or notice volume, so they show that the methodology is real and built, not that it runs at full production scale.",[204,205,206,207,208],"alternative-data-credit-scoring","sme-cash-flow-underwriting","outbound-notice-drafting","conversational-loan-application-intake","loan-restructuring-recommendations","2026-09-27",[211],{"date":209,"note":212},"First published","adverse-action-explanations",[215,244],{"title":216,"useCases":217,"organization":218,"vendors":222,"summary":225,"stage":226,"year":227,"channels":228,"languages":230,"metrics":232,"outcomeDisclosed":220,"sources":233,"verification":239,"grade":241,"id":242,"organizationSlug":243},"Discover Financial Services: patented SHAP based generation of adverse action reason codes",[213],{"name":219,"anonymized":220,"country":221,"region":150,"industry":16},"Discover Financial Services",false,"US",[223],{"name":219,"role":224},"in-house","Discover Financial Services patented a framework that generates the adverse action reason codes a lender must give a declined credit applicant directly from a machine learning model. The system groups correlated input variables, scores each group with partial dependence plots and Shapley Additive Explanations, ranks the groups, and turns the top ranked groups into the reason codes sent to the applicant. The United States Patent and Trademark Office granted the patent in July 2024 on an application Discover filed in May 2020. Discover Financial Services merged into Capital One Financial Corporation in May 2025 (the patent assignment was recorded in July 2025), which is why current patent databases list Capital One as the assignee. No source discloses an error rate, approval volume or other outcome for the system.","announced",2024,[229],"api",[231],"en",[],[234],{"url":235,"title":236,"publisher":237,"date":238},"https://patents.google.com/patent/US12050975B2/en","US12050975B2: System and method for utilizing grouped partial dependence plots and Shapley additive explanations in the generation of adverse action reason codes","United States Patent and Trademark Office","2024-07-30",{"level":240,"checkedAt":209},"source-verified","B","discover-financial-services-adverse-action-reason-codes",null,{"title":245,"useCases":246,"organization":247,"vendors":249,"summary":251,"stage":252,"year":253,"channels":254,"languages":255,"metrics":256,"outcomeDisclosed":220,"sources":257,"verification":267,"grade":241,"id":268,"organizationSlug":243},"Wells Fargo: patented adverse action methodology for machine learning credit risk models",[213],{"name":248,"anonymized":220,"country":221,"region":150,"industry":16},"Wells Fargo",[250],{"name":248,"role":224},"Wells Fargo Bank patented a computer based credit evaluation system that pairs a machine learning credit risk model with an adverse action methodology: when the model denies an applicant, the system compares the applicant's characteristic values against anchor values taken from a top scoring population, calculates a replacement score for each characteristic, and ranks the characteristics to identify the principal adverse action factors for the denial. The United States Patent and Trademark Office granted the patent in October 2022 on an application Wells Fargo filed in October 2019. Separately, the trade publication Risk.net reported in August 2021 that a team of Wells Fargo researchers had begun deploying an explainability technique for its deep learning credit models, and a paper by six Wells Fargo model risk researchers proposed a related Shapley decomposition method for explaining adverse credit decisions. No outcome metric or notice volume is disclosed by any source.","pilot",2022,[229],[231],[],[258,262],{"url":259,"title":260,"publisher":237,"date":261},"https://patents.google.com/patent/US11475515B1/en","US11475515B1: Adverse action methodology for credit risk models","2022-10-18",{"url":263,"title":264,"publisher":265,"date":266},"https://www.risk.net/risk-management/7865541/wells-touts-new-explainability-technique-for-ai-credit-models","Wells touts new explainability technique for AI credit models","Risk.net","2021-08-16",{"level":240,"checkedAt":209},"wells-fargo-adverse-action-methodology",0,[],{"low":272,"high":273},66666.66666666667,400000,[275,298,312,343,362],{"slug":204,"title":276,"shortTitle":277,"definition":278,"status":9,"industries":279,"functions":280,"patterns":283,"audience":286,"autonomy":287,"adoptionStage":288,"segment":34,"evidenceCount":289,"publicEvidenceCount":289,"organizations":290,"bestGrade":241,"headline":243,"lastVerified":296,"indexable":297},"AI credit scoring with alternative data for thin file applicants","Alternative data credit scoring","A machine learning credit model that adds consumer permissioned alternative data, such as bank account cash flow, rent, utility and telco payments or ecosystem data, to credit bureau data, so a lender can assess applicants with thin or no credit files and return a decision with specific reasons.",[16,17],[19,281,282],"underwriting","risk-management",[284,285,25],"prediction-and-scoring","document-processing","back-office","supervised-agent","early-adopters",5,[291,292,293,294,295],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network","2026-09-26",true,{"slug":205,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":303,"patterns":304,"audience":286,"autonomy":287,"adoptionStage":288,"segment":34,"evidenceCount":306,"publicEvidenceCount":306,"organizations":307,"bestGrade":241,"headline":243,"lastVerified":296,"indexable":297},"AI cash flow underwriting for small business loans","SME cash flow underwriting","An underwriting engine that assesses a small business's repayment capacity from live bank transactions, point of sale and payment flows, receivables and accounting data instead of audited accounts, and returns a decision recommendation with the evidence and reasons behind it.",[16],[19,281,282],[284,285,305,25],"agentic-workflow",4,[308,309,310,311],"MYbank","National Australia Bank","OakNorth Bank","Sumitomo Mitsui Banking Corporation",{"slug":206,"title":313,"shortTitle":314,"definition":315,"status":9,"industries":316,"functions":322,"patterns":326,"audience":31,"autonomy":32,"adoptionStage":288,"segment":286,"evidenceCount":289,"publicEvidenceCount":289,"organizations":328,"bestGrade":241,"headline":333,"lastVerified":296,"indexable":297},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[317,16,318,319,320,321],"cross-industry","insurance","government","healthcare","wealth-and-asset-management",[323,21,324,20,325],"operations","collections-and-recovery","claims",[23,24,327],"translation",[329,330,331,332],"Acentra Health","Hiscox","Health Resources and Services Administration","SS&C Technologies",{"kpi":334,"label":335,"unit":336,"n":337,"nUpTo":269,"kind":338,"value":339,"qualifier":340,"claimant":341,"organization":342,"vendorReported":297},"processing-time-reduction","Cycle time reduction","percent",1,"reported",25,"exact","vendor","SS&C GIDS and RS",{"slug":207,"title":344,"shortTitle":345,"definition":346,"status":9,"industries":347,"functions":348,"patterns":350,"audience":352,"autonomy":287,"adoptionStage":288,"segment":353,"evidenceCount":354,"publicEvidenceCount":289,"organizations":355,"bestGrade":361,"headline":243,"lastVerified":209,"indexable":297},"Conversational AI for loan application intake","Loan application intake","A conversational assistant on web, app, messaging or voice that explains loan products, captures the application through dialogue in the customer's language, checks documents and basic eligibility rules, and hands a complete, structured application to origination, without making the credit decision.",[16],[19,349,21],"sales",[25,285,24,351],"voice-agent","customer-facing","front-office",6,[356,357,358,359,360],"Absa Bank","Figure","Lloyds Banking Group","Oper Credits","Rocket Mortgage","C",{"slug":208,"title":363,"shortTitle":364,"definition":365,"status":9,"industries":366,"functions":367,"patterns":368,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":370,"publicEvidenceCount":337,"organizations":371,"bestGrade":241,"headline":243,"lastVerified":209,"indexable":297},"AI recommendations for loan restructuring and hardship arrangements","Restructuring recommendations","An assistant that assembles a stressed borrower's position, tests restructuring options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, and recommends the best fit with a written rationale for a person to approve.",[16],[324,19,282],[305,24,285,369],"recommendation-and-personalization",2,[372],"Commonwealth Bank of Australia",{"indexable":297,"reasons":374},[],[376,381,386,394,401,407,413,419,427,433,439,445,452,459,465,470,477,483,489,495,501,507,513,518,523,529,533,538,543,550,556,562,569,574],{"id":139,"label":377,"issuer":177,"region":172,"url":378,"description":379,"useCases":380,"indexable":297},"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":140,"label":382,"issuer":177,"region":172,"url":383,"description":384,"useCases":385,"indexable":297},"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":387,"label":388,"issuer":389,"region":390,"url":391,"description":392,"useCases":393,"indexable":297},"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":395,"label":396,"issuer":397,"region":150,"url":398,"description":399,"useCases":400,"indexable":297},"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":402,"label":403,"issuer":177,"region":172,"url":404,"description":405,"useCases":406,"indexable":297},"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":141,"label":408,"issuer":409,"region":172,"url":410,"description":411,"useCases":412,"indexable":297},"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":414,"issuer":415,"region":172,"url":416,"description":417,"useCases":418,"indexable":297},"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":420,"label":421,"issuer":422,"region":423,"url":424,"description":425,"useCases":426,"indexable":297},"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":428,"label":429,"issuer":430,"region":423,"url":431,"description":432,"useCases":339,"indexable":297},"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.",{"id":434,"label":435,"issuer":436,"region":390,"url":437,"description":438,"useCases":66,"indexable":297},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":440,"label":441,"issuer":442,"region":150,"url":443,"description":444,"useCases":66,"indexable":297},"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":446,"label":447,"issuer":448,"region":172,"url":449,"description":450,"useCases":451,"indexable":297},"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":453,"label":454,"issuer":455,"region":390,"url":456,"description":457,"useCases":458,"indexable":297},"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":460,"label":461,"issuer":177,"region":172,"url":462,"description":463,"useCases":464,"indexable":297},"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":466,"label":467,"issuer":177,"region":172,"url":468,"description":469,"useCases":464,"indexable":297},"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":471,"label":472,"issuer":473,"region":150,"url":474,"description":475,"useCases":476,"indexable":297},"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":478,"label":479,"issuer":177,"region":172,"url":480,"description":481,"useCases":482,"indexable":297},"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":484,"label":485,"issuer":486,"region":150,"url":487,"description":488,"useCases":482,"indexable":297},"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":490,"label":491,"issuer":492,"region":390,"url":493,"description":494,"useCases":482,"indexable":297},"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":496,"label":497,"issuer":177,"region":172,"url":498,"description":499,"useCases":500,"indexable":297},"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":502,"label":503,"issuer":504,"region":150,"url":505,"description":506,"useCases":500,"indexable":297},"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":508,"label":509,"issuer":422,"region":423,"url":510,"description":511,"useCases":512,"indexable":297},"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":514,"label":515,"issuer":177,"region":172,"url":516,"description":517,"useCases":512,"indexable":297},"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":519,"label":520,"issuer":177,"region":172,"url":521,"description":522,"useCases":512,"indexable":297},"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":143,"label":524,"issuer":525,"region":172,"url":526,"description":527,"useCases":528,"indexable":297},"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":144,"label":530,"issuer":149,"region":150,"url":151,"description":531,"useCases":532,"indexable":297},"ECOA and Regulation B","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":534,"label":535,"issuer":177,"region":172,"url":536,"description":537,"useCases":532,"indexable":297},"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":539,"label":540,"issuer":177,"region":172,"url":541,"description":542,"useCases":354,"indexable":297},"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":544,"label":545,"issuer":546,"region":547,"url":548,"description":549,"useCases":289,"indexable":297},"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":551,"label":552,"issuer":553,"region":172,"url":554,"description":555,"useCases":306,"indexable":297},"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":557,"label":558,"issuer":559,"region":172,"url":560,"description":561,"useCases":306,"indexable":297},"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":563,"label":564,"issuer":565,"region":423,"url":566,"description":567,"useCases":568,"indexable":297},"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.",3,{"id":570,"label":571,"issuer":177,"region":172,"url":572,"description":573,"useCases":568,"indexable":297},"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":145,"label":575,"issuer":576,"region":150,"url":577,"description":578,"useCases":568,"indexable":297},"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.",1790598298589]