[{"data":1,"prerenderedAt":559},["ShallowReactive",2],{"uc-bnpl-underwriting-and-credit-risk":3,"uc-regulations":335},{"useCase":4,"evidence":175,"blitsAiDeployments":231,"benchmarks":232,"indicative":239,"related":242,"indexability":333,"includeUnpublished":181},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":40,"indicativeValue":45,"macroEstimates":86,"feasibility":87,"implementation":99,"risk":134,"blitsAi":150,"faq":152,"related":165,"datePublished":170,"dateModified":170,"lastVerified":170,"changelog":171,"slug":174},"AI underwriting and credit risk decisioning for buy now pay later","BNPL underwriting and risk","AI underwriting for buy now pay later","AI decides buy now pay later approvals. Affirm's model produced 3.4% more completed purchases against a control group; Sezzle grades receivables by Prophet Score.","published","AI that underwrites a buy now pay later purchase at checkout, scoring the consumer and the specific order from bureau, transaction and behavioral data to approve, decline or size the credit line in real time, with no per transaction human review, and that keeps retraining as repayment outcomes come in.",[12,13,14,15],"BNPL credit decisioning","point of sale lending underwriting","real time checkout credit risk","pay in four risk scoring",[17,18],"payments","banking",[20,21],"lending-and-credit","risk-management",[23,24],"prediction-and-scoring","classification-and-routing",[26,27],"api","mobile-app","back-office","autonomous","mainstream","lending","Buy now pay later works only if the approval decision happens in the time it takes a shopper to\nreach a checkout button: too slow and the sale is lost, too permissive and losses erode the thin\nmargin on an unsecured, often fee free loan. Unlike a credit card, there is frequently no prior\nrelationship and no traditional credit history to lean on, including for thin file consumers with\nlimited or no credit history.\n\nReviewing a meaningful share of checkout decisions by hand is not feasible at buy now pay later's\nvolume, so the underwriting model is the business: every basis point of loss it misses is a basis\npoint off a margin that is already thin, and every good customer it declines is a sale the\nmerchant loses and a customer who may not come back.\n\nThe models keep having to improve, not just run: repayment outcomes on a high volume, short term\nloan book arrive quickly, which is both the risk (a bad model season shows up fast) and the\nopportunity (there is a fast, large feedback loop to retrain on).",[],"1. **Capture the order and applicant.** At checkout, the provider receives the order amount,\n   merchant and available consumer data (device, contact details, and, if returning, prior\n   repayment history on the platform).\n2. **Score in real time.** A machine learning model scores the consumer and the specific order\n   using bureau data where available, plus transaction and behavioral signals, without waiting\n   for a manual credit check.\n3. **Decide and size the line.** The model outputs an approve, decline or step down (a smaller\n   amount or a different payment schedule) decision and, for returning customers, an available\n   spending limit, in the time the checkout page takes to load.\n4. **Monitor the back book continuously.** Delinquency and charge off rates are tracked by\n   origination cohort, so a model or a segment that is underperforming shows up in weeks, not at\n   the next annual review.\n5. **Retrain on new repayment data.** As loans mature, their outcomes feed back into the next\n   model generation, so risk separation improves as more repayment history accumulates.",[36,37,38,39],"risk-reduction","revenue-growth","speed","inclusion-and-access",[41,42,43,44],"conversion-rate-uplift","delinquency-rate-reduction","approval-rate-uplift","automation-rate",{"referenceOrg":46,"inputs":47,"formula":82,"currency":73,"period":83,"resultLabel":84,"caveat":85},"A BNPL provider processing 1 million checkout decisions a month",[48,54,61,68,75],{"key":49,"label":50,"low":51,"high":51,"unit":52,"note":53},"decisionsPerMonth","Checkout credit decisions per month",1000000,"decisions per month","The reference provider.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"completionRate","Checkout decisions that already result in a completed purchase under the prior model",0.5,0.8,"fraction of decisions","Editorial assumption for the share of checkout decisions that already convert to a completed purchase under the prior underwriting system. Replace with your own approval or completion rate.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"additionalCompletedPurchaseUplift","Additional completed purchases versus the prior underwriting system",0.01,0.034,"relative uplift, not a share of decisions","Conservative against Affirm's reported 3.4% more completed purchases from its transformer based underwriting model, a relative uplift measured against a control group at checkout, not a share of all checkout decisions.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"averageOrderValue","Average order value",100,250,"USD","Editorial assumption for a general merchandise BNPL book. Replace with your own average order value.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"takeRate","Net revenue as a share of order value",0.02,0.05,"fraction of order value","Editorial assumption for merchant and consumer fee revenue net of funding and loss cost. Replace with your own take rate.","decisionsPerMonth * 12 * completionRate * additionalCompletedPurchaseUplift * averageOrderValue * takeRate","per year","Additional annual revenue from more completed purchases","Revenue only, from the additional completed purchases at an assumed take rate. It leaves out the credit loss on the additional volume, funding cost, and any change in merchant mix or repeat purchase behavior.",[],{"complexity":88,"complexityNote":89,"dataPrerequisites":90,"integrations":94},"high","The modeling itself is specialized (real time inference at checkout, thin file and no file consumers, explainability requirements for adverse action), and the business depends on continuously retraining as new repayment outcomes arrive, which needs a mature MLOps and model risk governance practice, not a one off build.",[91,92,93],"Bureau data where available, plus permissioned transaction and device data","A growing base of the provider's own repayment outcomes to train and validate against","Adverse action reason codes mapped to the model's decision factors",[95,96,97,98],"Merchant checkout and payment integration (web and app)","Credit bureau and identity verification services","Loan servicing and collections systems","Model monitoring and MLOps pipeline for retraining and champion challenger testing",{"steps":100,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":124},[101,104,107,110],{"title":102,"detail":103},"Separate the score from the policy","Keep the statistical model that scores risk separate from the business rules that turn a score into an approve, decline or step down decision, so policy can change without retraining the model.",{"title":105,"detail":106},"Build the adverse action explanation into the model, not after it","Choose a model architecture and reason code mapping that can explain a decline in plain terms from the start; retrofitting explainability onto an opaque model after launch is much harder.",{"title":108,"detail":109},"Champion challenger every model change","Run a new model version against a control group before full rollout, and measure the change in both approval rate and downstream loss rate, not approval rate alone.",{"title":111,"detail":112},"Monitor by origination cohort, not only in aggregate","Track delinquency and charge off by the month or week a loan was originated, so a deteriorating cohort is visible in weeks rather than showing up only in a lagging annual loss number.",[114,115,116,117],"Credit policy and decision thresholds are version controlled and changed only through a documented approval process","Every decline carries a specific, accurate adverse action reason a consumer can act on","New model versions are tested against a control group before they set decisions for the whole book","Approval rate and loss rate are reviewed together at every model or policy change, never one without the other","No individual checkout decision is reviewed by a person; the human role is model governance, not transaction review. A model risk function signs off on every new model version and policy change before rollout, reviews cohort level performance on a fixed schedule, and can force a rollback to a prior model version if a cohort deteriorates.",[120,121,122,123],"Approval rate and completed purchase rate, measured against a control group for any change","Delinquency and charge off rate by origination cohort","Adverse action accuracy, whether the stated decline reason matches the model's actual decision factors","Time from a detected cohort issue to a policy or model change",[125,128,131],{"title":126,"detail":127},"A model change is judged on approvals alone","A new model approves more applicants and looks like a win before enough of that cohort has had time to default. Hold judgment until the cohort has matured, or use an early performance proxy validated against past cohorts.",{"title":129,"detail":130},"Explainability bolted on after the fact","An accurate but opaque model gets a generic reason code added later that does not match what actually drove the decision, creating regulatory and consumer harm risk. Build the explanation into the model design.",{"title":132,"detail":133},"Thin file and no file segments drift unnoticed","Performance on consumers with limited credit history is not tracked separately from the whole book, so a problem specific to that segment is masked by strong performance elsewhere. Monitor it as its own cohort.",{"euAiAct":135,"regulations":137,"guidance":143,"controls":144,"incidents":149},{"tier":88,"basis":136},"Annex III point 5(b): AI systems intended to evaluate the creditworthiness of natural persons or establish their credit score are high risk, and a real time BNPL underwriting model is squarely this use, since it is the system making the credit decision rather than a supporting tool. Providers need risk management, data governance, logging and human oversight; deployers must run a fundamental rights impact assessment (Article 27), and consumers have a right to an explanation of an individual decision (Article 86).",[138,139,140,141,142],"eu-ai-act","gdpr","us-ecoa-reg-b","us-fcra","uk-consumer-duty",[],[145,146,147,148],"Adverse action reason codes that match the model's actual decision factors","Version controlled model and policy changes with a documented sign off before rollout","Champion challenger testing against a control group for every model change","Cohort level delinquency and charge off monitoring with a defined escalation path",[],{"howToBuild":151},"A real time, per transaction underwriting model like this sits outside what an LLM based\nplatform such as Blits.ai builds: the credit decision itself stays with the provider's own\nstatistical models and model risk function. Where Blits.ai fits is around that decision: an\n**AI agent** with **custom functions** can call the provider's existing decision API to explain\nan approval, a decline or a payment schedule to the consumer in plain language, including a\nspecific, accurate reason on a decline, grounded in a **knowledge base** of the provider's own\npolicy and terms so the explanation stays tied to the reason codes the decision API returns.\n\nThe same agent can run as a **conversational agent** on **web chat** or via the **REST or\nWebSocket API channel** inside the merchant's app, escalate a disputed decision to a **human\nhandover**, and log every explanation given for **analytics** and audit. **Guardrails** keep\nthe agent from speculating about why a decision was made beyond the reason codes it is given,\nand **test suites** verify its explanations stay consistent as the underlying decision policy\nchanges.",[153,156,159,162],{"question":154,"answer":155},"Does a human review each buy now pay later approval?","No. The decision is automated end to end at checkout speed. The human role is governing the model and policy: sign off on new model versions, monitor cohort performance and set the rules that turn a risk score into a decision, rather than reviewing individual transactions.",{"question":157,"answer":158},"What results have named BNPL providers disclosed?","Affirm announced a transformer based underwriting model on 17 September 2026 and reported that its initial deployment produced 3.4% more completed purchases measured against a control group, approving eligible applicants, including some with no FICO scores, that its prior system would have declined. Sezzle uses a proprietary machine learning score, the Prophet Score, grouped into A to C bands, as the credit quality indicator for its receivables portfolio, last updated in October 2023.",{"question":160,"answer":161},"Is buy now pay later underwriting high risk under the EU AI Act?","Yes, generally. Annex III point 5(b) makes AI that evaluates the creditworthiness of natural persons or establishes a credit score high risk, and a real time BNPL underwriting engine is the system making that decision, not a supporting tool, so the high risk obligations apply directly.",{"question":163,"answer":164},"How is this different from a bank's alternative data credit scoring?","The underlying modeling techniques overlap, but BNPL underwriting decides in real time at checkout on a small, often fee free loan with thin margins, usually without an existing customer relationship, which puts more weight on speed, explainability at scale and rapid retraining than a bank's periodic credit decisioning process.",[166,167,168,169],"alternative-data-credit-scoring","real-time-fraud-scoring","adverse-action-explanations","conversational-loan-application-intake","2026-09-29",[172],{"date":170,"note":173},"First published","bnpl-underwriting-and-credit-risk",[176,212],{"title":177,"useCases":178,"organization":179,"vendors":184,"summary":185,"stage":186,"year":187,"channels":188,"languages":189,"metrics":191,"outcomeDisclosed":200,"sources":201,"verification":207,"grade":209,"id":210,"organizationSlug":211},"Affirm: transformer based machine learning model for real time underwriting",[174],{"name":180,"anonymized":181,"country":182,"region":183,"industry":17},"Affirm Holdings",false,"US","north-america",[],"Affirm, a buy now pay later provider, has underwritten every purchase individually in real time using in house machine learning models for 14 years. In September 2026 it announced a transformer based model that reads the order and timing of events in a consumer's credit history, live at checkout in the US. In its initial deployment the model approved additional eligible applications, including consumers with limited credit histories and no FICO scores, that Affirm's prior models would have declined.","production",2026,[26],[190],"en",[192],{"kpi":41,"value":193,"unit":194,"qualifier":195,"baseline":196,"claimant":197,"quote":198,"sourceUrl":199},3.4,"percent","exact","A control group at checkout under Affirm's prior underwriting system. Separately, the additional loans approved also performed better than a comparable expansion under Affirm's previous machine learning models, with no figure given for that comparison.","organization","Measured against a control group, that produced 3.4% more completed purchases, and those additional loans performed better than a comparable expansion under Affirm's previous machine learning models.","https://investors.affirm.com/news-releases/news-release-details/affirm-launches-transformer-based-machine-learning-model-real",true,[202],{"url":199,"title":203,"publisher":204,"date":205,"archivedUrl":206},"Affirm launches transformer-based machine learning model for real-time underwriting","Affirm Holdings, Inc.","2026-09-17","https://web.archive.org/web/2026/https://investors.affirm.com/news-releases/news-release-details/affirm-launches-transformer-based-machine-learning-model-real",{"level":208,"checkedAt":170},"source-verified","B","affirm-transformer-underwriting-model",null,{"title":213,"useCases":214,"organization":215,"vendors":217,"summary":218,"stage":186,"year":219,"channels":220,"languages":221,"metrics":222,"outcomeDisclosed":181,"sources":223,"verification":229,"grade":209,"id":230,"organizationSlug":211},"Sezzle: Prophet Score machine learning credit model",[174],{"name":216,"anonymized":181,"country":182,"region":183,"industry":17},"Sezzle",[],"Sezzle, a buy now pay later provider, discloses in its fiscal year 2025 Form 10-K that its platform reviews the transaction and consumer profile in real time at checkout and that its underwriting platform, informed by its proprietary credit risk models, tailors the lending amount for each consumer. For the resulting receivables portfolio, Sezzle grades credit quality with an internal, proprietary machine learning score it calls the Prophet Score, built from internal risk indicators and consumer attributes predictive of a customer's ability and willingness to repay. Receivables are grouped into three Prophet Score bands, A to C, and Sezzle's risk and fraud team reviews the model's integrity at least annually; the model was last updated in October 2023.",2025,[26],[190],[],[224],{"url":225,"title":226,"publisher":227,"date":228},"https://www.sec.gov/Archives/edgar/data/1662991/000166299126000016/szl-20251231.htm","Sezzle, Inc. Form 10-K for the fiscal year ended December 31, 2025","U.S. Securities and Exchange Commission","2026-02-26",{"level":208,"checkedAt":170},"sezzle-prophet-score-credit-model",0,[233],{"kpi":41,"label":234,"unit":194,"aggregate":200,"higherIsBetter":200,"n":235,"nUpTo":231,"median":193,"min":193,"max":193,"byClaimant":236,"vendorOnly":181,"points":237},"Conversion uplift",1,{"organization":235,"vendor":231,"regulator":231,"independent":231},[238],{"evidenceId":210,"organization":180,"value":193,"qualifier":195,"claimant":197,"grade":209,"pooled":200},{"low":240,"high":241},120000,4080000,[243,269,296,314],{"slug":166,"title":244,"shortTitle":245,"definition":246,"status":9,"industries":247,"functions":248,"patterns":250,"audience":28,"autonomy":253,"adoptionStage":254,"segment":31,"evidenceCount":255,"publicEvidenceCount":255,"organizations":256,"bestGrade":209,"headline":263,"lastVerified":268,"indexable":200},"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.",[18,17],[20,249,21],"underwriting",[23,251,252],"document-processing","conversational-agent","supervised-agent","early-adopters",6,[257,258,259,260,261,262],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network","Upstart Holdings",{"kpi":44,"label":264,"unit":194,"n":235,"nUpTo":231,"kind":265,"value":266,"qualifier":267,"claimant":197,"organization":262,"vendorReported":181},"Automation rate","reported",90,"at-least","2026-09-26",{"slug":167,"title":270,"shortTitle":271,"definition":272,"status":9,"industries":273,"functions":274,"patterns":276,"audience":28,"autonomy":29,"adoptionStage":30,"segment":278,"evidenceCount":279,"publicEvidenceCount":279,"organizations":280,"bestGrade":209,"headline":289,"lastVerified":295,"indexable":200},"Real time fraud scoring for card and instant payments","Real time fraud scoring","Machine learning that decides in milliseconds, without any conversation, how likely each card authorization and account to account payment is to be fraudulent, combining behavioural, device and network signals, so the bank can approve, challenge or block a payment before the money leaves. Working the resulting alerts and talking to the customer about them are separate use cases.",[18,17],[275],"fraud-prevention",[23,277],"anomaly-detection","middle-office",9,[281,282,283,284,285,286,287,288],"ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Commonwealth Bank of Australia","Mastercard","NatWest Group","Pay.UK","Revolut","Stripe","Visa",{"kpi":290,"label":291,"unit":194,"n":292,"nUpTo":231,"kind":293,"value":294,"qualifier":195,"claimant":197,"organization":211,"vendorReported":181},"fraud-loss-reduction","Fraud loss reduction",3,"median",30,"2026-09-27",{"slug":168,"title":297,"shortTitle":298,"definition":299,"status":9,"industries":300,"functions":301,"patterns":304,"audience":307,"autonomy":308,"adoptionStage":309,"segment":31,"evidenceCount":310,"publicEvidenceCount":310,"organizations":311,"bestGrade":209,"headline":211,"lastVerified":295,"indexable":200},"AI drafted explanations for credit declines and adverse actions","Adverse action explanations","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.",[18,17],[20,302,303],"regulatory-compliance","customer-service",[305,306,252],"content-generation","rag-knowledge-assistant","employee-facing","copilot","emerging",2,[312,313],"Discover Financial Services","Wells Fargo",{"slug":169,"title":315,"shortTitle":316,"definition":317,"status":9,"industries":318,"functions":319,"patterns":321,"audience":323,"autonomy":253,"adoptionStage":254,"segment":324,"evidenceCount":255,"publicEvidenceCount":325,"organizations":326,"bestGrade":332,"headline":211,"lastVerified":295,"indexable":200},"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.",[18],[20,320,303],"sales",[252,251,306,322],"voice-agent","customer-facing","front-office",5,[327,328,329,330,331],"Absa Bank","Figure","Lloyds Banking Group","Oper Credits","Rocket Mortgage","C",{"indexable":200,"reasons":334},[],[336,343,348,356,363,370,376,382,390,397,404,411,417,423,430,437,443,450,456,462,468,474,479,486,491,496,501,506,512,517,524,531,537,543,548,553],{"id":138,"label":337,"issuer":338,"region":339,"url":340,"description":341,"useCases":342,"indexable":200},"EU AI Act","European Union","europe","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.",230,{"id":139,"label":344,"issuer":338,"region":339,"url":345,"description":346,"useCases":347,"indexable":200},"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.",207,{"id":349,"label":350,"issuer":351,"region":352,"url":353,"description":354,"useCases":355,"indexable":200},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":357,"label":358,"issuer":359,"region":183,"url":360,"description":361,"useCases":362,"indexable":200},"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.",92,{"id":364,"label":365,"issuer":366,"region":339,"url":367,"description":368,"useCases":369,"indexable":200},"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.",71,{"id":371,"label":372,"issuer":338,"region":339,"url":373,"description":374,"useCases":375,"indexable":200},"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":142,"label":377,"issuer":378,"region":339,"url":379,"description":380,"useCases":381,"indexable":200},"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.",50,{"id":383,"label":384,"issuer":385,"region":386,"url":387,"description":388,"useCases":389,"indexable":200},"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.",37,{"id":391,"label":392,"issuer":393,"region":386,"url":394,"description":395,"useCases":396,"indexable":200},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":398,"label":399,"issuer":400,"region":183,"url":401,"description":402,"useCases":403,"indexable":200},"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.",22,{"id":405,"label":406,"issuer":407,"region":352,"url":408,"description":409,"useCases":410,"indexable":200},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":412,"label":413,"issuer":338,"region":339,"url":414,"description":415,"useCases":416,"indexable":200},"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.",17,{"id":418,"label":419,"issuer":420,"region":339,"url":421,"description":422,"useCases":416,"indexable":200},"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.",{"id":424,"label":425,"issuer":426,"region":183,"url":427,"description":428,"useCases":429,"indexable":200},"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.",16,{"id":431,"label":432,"issuer":433,"region":352,"url":434,"description":435,"useCases":436,"indexable":200},"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":438,"label":439,"issuer":338,"region":339,"url":440,"description":441,"useCases":442,"indexable":200},"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":444,"label":445,"issuer":446,"region":183,"url":447,"description":448,"useCases":449,"indexable":200},"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":451,"label":452,"issuer":453,"region":183,"url":454,"description":455,"useCases":449,"indexable":200},"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":457,"label":458,"issuer":338,"region":339,"url":459,"description":460,"useCases":461,"indexable":200},"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":463,"label":464,"issuer":465,"region":352,"url":466,"description":467,"useCases":461,"indexable":200},"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":140,"label":469,"issuer":470,"region":183,"url":471,"description":472,"useCases":473,"indexable":200},"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.",11,{"id":475,"label":476,"issuer":338,"region":339,"url":477,"description":478,"useCases":473,"indexable":200},"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.",{"id":480,"label":481,"issuer":482,"region":339,"url":483,"description":484,"useCases":485,"indexable":200},"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.",10,{"id":487,"label":488,"issuer":385,"region":386,"url":489,"description":490,"useCases":485,"indexable":200},"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":492,"label":493,"issuer":338,"region":339,"url":494,"description":495,"useCases":485,"indexable":200},"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":497,"label":498,"issuer":338,"region":339,"url":499,"description":500,"useCases":485,"indexable":200},"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":502,"label":503,"issuer":338,"region":339,"url":504,"description":505,"useCases":279,"indexable":200},"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":141,"label":507,"issuer":508,"region":183,"url":509,"description":510,"useCases":511,"indexable":200},"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.",7,{"id":513,"label":514,"issuer":338,"region":339,"url":515,"description":516,"useCases":255,"indexable":200},"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":518,"label":519,"issuer":520,"region":521,"url":522,"description":523,"useCases":325,"indexable":200},"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":525,"label":526,"issuer":527,"region":339,"url":528,"description":529,"useCases":530,"indexable":200},"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.",4,{"id":532,"label":533,"issuer":534,"region":339,"url":535,"description":536,"useCases":530,"indexable":200},"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":538,"label":539,"issuer":540,"region":386,"url":541,"description":542,"useCases":292,"indexable":200},"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":544,"label":545,"issuer":338,"region":339,"url":546,"description":547,"useCases":292,"indexable":200},"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":549,"label":550,"issuer":338,"region":339,"url":551,"description":552,"useCases":292,"indexable":200},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":554,"label":555,"issuer":556,"region":183,"url":557,"description":558,"useCases":292,"indexable":200},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683494640]