[{"data":1,"prerenderedAt":104},["ShallowReactive",2],{"uc-reg-us-fcra":3},{"regulation":4,"includeUnpublished":11,"indexable":12,"useCases":13},{"id":5,"label":6,"issuer":7,"region":8,"url":9,"description":10},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","north-america","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.",false,true,[14,47,69],{"slug":15,"title":16,"shortTitle":17,"definition":18,"status":19,"industries":20,"functions":23,"patterns":27,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":35,"publicEvidenceCount":35,"organizations":36,"bestGrade":42,"headline":43,"lastVerified":44,"indexable":12,"euAiActTier":45,"euAiActBasis":46},"alternative-data-credit-scoring","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.","published",[21,22],"banking","payments",[24,25,26],"lending-and-credit","underwriting","risk-management",[28,29,30],"prediction-and-scoring","document-processing","conversational-agent","back-office","supervised-agent","early-adopters","lending",5,[37,38,39,40,41],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network","B",null,"2026-09-26","high","Annex III point 5(b): AI systems intended to evaluate the creditworthiness of natural persons or establish their credit score are high risk, except systems used to detect financial fraud. Providers need risk management, data governance, logging and human oversight. Deployers must carry out a fundamental rights impact assessment before use (Article 27), and affected persons have a right to an explanation of individual decisions from the deployer (Article 86).",{"slug":48,"title":49,"shortTitle":50,"definition":51,"status":19,"industries":52,"functions":53,"patterns":56,"audience":59,"autonomy":60,"adoptionStage":61,"segment":34,"evidenceCount":62,"publicEvidenceCount":62,"organizations":63,"bestGrade":42,"headline":43,"lastVerified":66,"indexable":12,"euAiActTier":67,"euAiActBasis":68},"adverse-action-explanations","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.",[21,22],[24,54,55],"regulatory-compliance","customer-service",[57,58,30],"content-generation","rag-knowledge-assistant","employee-facing","copilot","emerging",2,[64,65],"Discover Financial Services","Wells Fargo","2026-09-27","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).",{"slug":70,"title":71,"shortTitle":72,"definition":73,"status":19,"industries":74,"functions":78,"patterns":81,"audience":31,"autonomy":32,"adoptionStage":33,"segment":84,"evidenceCount":85,"publicEvidenceCount":85,"organizations":86,"bestGrade":42,"headline":93,"lastVerified":44,"indexable":12,"euAiActTier":67,"euAiActBasis":103},"application-and-identity-fraud-detection","AI for application and identity fraud detection","Application and identity fraud","AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.",[21,22,75,76,77],"cross-industry","government","telecommunications",[79,80,24],"fraud-prevention","onboarding-and-kyc",[29,82,83,28],"anomaly-detection","computer-vision","front-office",6,[87,88,89,90,91,92],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":94,"label":95,"unit":96,"n":97,"nUpTo":98,"kind":99,"value":100,"qualifier":101,"claimant":102,"organization":90,"vendorReported":11},"detection-rate-improvement","Detection improvement","multiplier",1,0,"reported",2.5,"exact","organization","Annex III point 5(b) excludes AI used to detect financial fraud from the high risk credit scoring category, but a system that in effect decides on creditworthiness is high risk, and remote biometric identification is high risk under point 1(a), which excludes one to one biometric verification. When a public authority uses the model on claims for public benefits, point 5(a) can apply, because it covers AI used to grant, reduce, revoke or reclaim benefits and has no fraud exception. Keep fraud detection separate from the credit or eligibility decision and use biometrics only for one to one verification.",1790598320289]