[{"data":1,"prerenderedAt":649},["ShallowReactive",2],{"uc-alternative-data-credit-scoring":3,"uc-regulations":451},{"useCase":4,"evidence":225,"blitsAiDeployments":345,"benchmarks":346,"indicative":347,"related":350,"indexability":449,"includeUnpublished":231},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":27,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":42,"valueDrivers":43,"kpis":48,"indicativeValue":53,"macroEstimates":88,"feasibility":89,"implementation":103,"risk":149,"blitsAi":198,"faq":200,"related":213,"datePublished":219,"dateModified":219,"lastVerified":220,"changelog":221,"slug":224},"AI credit scoring with alternative data for thin file applicants","Alternative data credit scoring","Alternative data credit scoring with AI","AI credit models add permissioned data such as bank cash flow to bureau files. In simulations Upstart reported to the CFPB, its model approved 27% more applicants.","published","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.",[12,13,14,15],"cash flow underwriting for consumers","thin file credit scoring","open banking credit score","machine learning credit scoring",[17,18],"banking","payments",[20,21,22],"lending-and-credit","underwriting","risk-management",[24,25,26],"prediction-and-scoring","document-processing","conversational-agent",[28,29,30],"api","mobile-app","web-chat","back-office","supervised-agent","early-adopters","lending","A bureau score needs a credit history. Young adults, migrants, gig workers, the self employed and\npeople who simply never borrowed have little or none, so a traditional scorecard either declines\nthem or prices them as if they were high risk. In the United States alone, the CFPB estimates that\n26 million adults have no credit history at a nationwide credit bureau and another 19 million have\none too thin or stale to score.\n\nThe information that would show whether these people can repay usually exists: salary and\nexpenses in their bank account, years of rent, utility and phone payments, or activity on a super\napp. Lenders could not use it at scale because it was unstructured, scattered and not permissioned\nfor credit. Open banking, consent frameworks and machine learning now make it usable, but they also\nbring new questions about fairness, explainability and privacy that a bureau scorecard never raised.",[37],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"The CFPB estimates that 26 million Americans are credit invisible, with no credit history at a nationwide consumer reporting agency, and that another 19 million have a history that is stale or insufficient to produce a score under most scoring models.","An update on credit access and the Bureau's first No-Action Letter","https://www.consumerfinance.gov/about-us/blog/update-credit-access-and-no-action-letter/",2019,"1. **Ask for consent.** The applicant chooses to share additional data, such as a bank account\n   connection through open banking or data from an ecosystem partner, and is told what it is used\n   for.\n2. **Turn raw data into features.** Transactions are categorised into income, rent, essential\n   spend, debt payments and overdraft use; stability and trend features are computed over several\n   months.\n3. **Score alongside the bureau.** A machine learning model, or a cash flow score added to the\n   existing scorecard, estimates default risk using both bureau and alternative features.\n4. **Decide within policy.** A decision engine applies credit policy, affordability rules and\n   limits, approves, declines or refers the case, and sets line size and price.\n5. **Explain the outcome.** Each decline or unfavourable term carries the specific principal\n   reasons derived from the model, and the applicant can ask what would change the outcome.\n6. **Monitor.** Approval rates, default rates and outcomes by protected group are tracked per\n   segment, and the model is revalidated when data or populations drift.",[44,45,46,47],"inclusion-and-access","revenue-growth","risk-reduction","speed",[49,50,51,52],"conversion-rate-uplift","automation-rate","processing-time-reduction","users-served",{"referenceOrg":54,"inputs":55,"formula":83,"currency":84,"period":85,"resultLabel":86,"caveat":87},"A consumer lender receiving 200,000 personal loan applications a year",[56,62,69,76],{"key":57,"label":58,"low":59,"high":59,"unit":60,"note":61},"applications","Applications per year",200000,"applications per year","The reference lender.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"declineShare","Share of applications declined today",0.3,0.4,"fraction of applications","Editorial assumption for a mainstream personal loan book. Replace with your own decline rate.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"rescuedShare","Share of declines that alternative data turns into sound approvals",0.05,0.15,"fraction of declines","The high end is the Atlanticus figure, where the vendor reports that 15% of marginal declines (not all declines) could be approved profitably, so it is an upper bound; the low end allows for weaker data coverage and consent drop off. Editorial assumption, replace with your own.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"contributionPerLoan","Net contribution per additional approved loan over its life",150,400,"USD per loan","Editorial assumption after expected credit losses and funding cost. Replace with your own.","applications * declineShare * rescuedShare * contributionPerLoan","USD","per year","Net contribution from additional approvals","Leaves out the cost of data access, model development and validation, the consent drop off rate, and any change in losses on loans the bank would have approved anyway. The uplift must be proven on your own population with a holdout before it is counted.",[],{"complexity":90,"complexityNote":91,"dataPrerequisites":92,"integrations":97},"high","The model is the easy part. The work is in consent flows that people complete, data coverage, model validation, fair lending testing, reason codes that stay specific and accurate, and integration with the existing decision engine and bureau.",[93,94,95,96],"Historical applications with outcomes (performance over at least 12 months) for model training and validation","Access to permissioned alternative data with a clear legal basis, such as open banking or partner data","Protected attribute data or accepted proxies for fairness testing, where the law allows","Documented credit policy and affordability rules",[98,99,100,101,102],"Open banking or account aggregation provider","Credit bureau","Decision engine or loan origination system","Model monitoring and model inventory","Customer channels for consent and status updates",{"steps":104,"guardrails":123,"humanInTheLoop":129,"kpisToInstrument":130,"failureModes":136},[105,108,111,114,117,120],{"title":106,"detail":107},"Pick the segment and the question","Start with one product and one segment, such as thin file applicants for a personal loan, and one question: can alternative data safely approve some of today's declines?",{"title":109,"detail":110},"Backtest before you lend","Score past applicants with and without the new data and compare default rates at equal approval rates. The published VantageScore pilots at Patelco Credit Union and Michigan State University Federal Credit Union tested the score on existing portfolios before lending on it, and that is the right first step.",{"title":112,"detail":113},"Design consent as a product","The uplift only reaches customers who share their data. Explain the benefit in plain words, keep the connection step short and offer it at the point of decline or referral.",{"title":115,"detail":116},"Build reasons in from the start","Choose features and methods that let you state the specific principal reasons for every adverse action. Test the reasons with real applicants before launch.",{"title":118,"detail":119},"Test for fairness and validate independently","Run disparate impact analysis and search for less discriminatory alternatives, then put the model through the same independent validation as any credit model.",{"title":121,"detail":122},"Launch with a champion and challenger","Route a share of traffic to the new model, keep the old one as control, and widen only when loss rates on the new approvals are confirmed.",[124,125,126,127,128],"Alternative data only with explicit, recorded consent and a documented legal basis","No feature that acts as a proxy for a protected characteristic, checked by testing, not by assertion","Specific, accurate reasons for every decline or unfavourable change in terms","Credit policy and affordability rules stay deterministic and outside the model","A human owns referred cases and any appeal against a decision","Credit officers decide referred and borderline cases and handle appeals. Model risk and fair lending teams approve the model before launch and review performance by segment every quarter. The customer can ask for human review of an automated decision.",[131,132,133,134,135],"Approval rate on the target segment versus a control group","Default and loss rates of the incremental approvals over time","Consent completion rate at the data sharing step","Approval and pricing gaps across protected groups","Share of decisions returned automatically, and decision time",[137,140,143,146],{"title":138,"detail":139},"Uplift that disappears in production","A backtest on past applicants does not match who actually consents. Measure with a live control group, not only on history.",{"title":141,"detail":142},"Proxy discrimination","Behavioural or device data can stand in for race, sex or age. Test outcomes by group and remove features that drive unjustified gaps.",{"title":144,"detail":145},"Reasons nobody can act on","Generic reasons such as \"failed to achieve a qualifying score\" or \"based on internal standards or policies\" do not meet the requirement for specific reasons and frustrate applicants. Map features to plain, specific reasons.",{"title":147,"detail":148},"Data that goes stale or disappears","A partner or aggregator changes coverage and the model degrades silently. Monitor input distributions and fall back to the bureau model.",{"euAiAct":150,"regulations":152,"guidance":163,"controls":187,"incidents":193},{"tier":90,"basis":151},"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).",[153,154,155,156,157,158,159,160,161,162],"eu-ai-act","gdpr","eba-loan-origination","us-sr-11-7","uk-consumer-duty","mas-ai-risk-management","nist-ai-rmf","us-ecoa-reg-b","us-fcra","pra-ss1-23",[164,170,176,181],{"title":165,"issuer":166,"region":167,"url":168,"note":169},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(b) lists creditworthiness evaluation and credit scoring of natural persons as high risk.",{"title":171,"issuer":172,"region":173,"url":174,"note":175},"Consumer Financial Protection Circular 2022-03: adverse action notification requirements for credit decisions based on complex algorithms (issued June 2022, withdrawn May 2025)","Consumer Financial Protection Bureau","north-america","https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-in-connection-with-credit-decisions-based-on-complex-algorithms/","Withdrawn by the CFPB on 12 May 2025 (90 FR 20084, FR Doc 2025-08286). The underlying requirement to give specific reasons comes from Regulation B itself (12 CFR 1002.9(b)(2)), which still applies.",{"title":177,"issuer":178,"region":167,"url":179,"note":180},"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","Sets expectations for creditworthiness assessment, including the use of automated models, data quality and explainability.",{"title":182,"issuer":183,"region":184,"url":185,"note":186},"MAS consultation paper: Guidelines on Artificial Intelligence Risk Management","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Proposed guidelines, issued for consultation in November 2025, setting supervisory expectations for AI inventories, risk materiality assessment, fairness, explainability and human oversight at financial institutions.",[188,189,190,191,192],"Model inventory entry with an accountable owner, validation report and approved use","Consent records linked to every decision that used alternative data","Fair lending testing before launch and on a fixed schedule, with a documented search for less discriminatory alternatives","Reason code library reviewed by compliance and tested for accuracy against model output","Drift and performance monitoring with a documented fallback to the bureau model",[194],{"title":195,"url":196,"note":197},"Incident 92: Apple Card's credit assessment algorithm allegedly discriminated against women","https://incidentdatabase.ai/cite/92/","Customers alleged that men received much higher credit limits than women with similar credit qualifications, and the complaints led to a regulatory investigation of the issuer's credit card practices. It shows why specific reasons and fairness testing must be ready before complaints arrive.",{"howToBuild":199},"Blits.ai does not replace the credit model or the decision engine; it runs the conversations and\nthe workflow around them. An **AI agent** on the app, web chat or WhatsApp explains the product,\nasks for consent to share bank data in plain language, collects documents through **receive\nattachment** blocks and gives status updates. **Custom functions** call the lender's consent,\naggregation and decision APIs, and regulated steps such as consent and the declaration run as\ndeterministic **flows**.\n\nWhen a decision comes back, the agent explains it using only the reason codes the decision engine\nreturned, retrieved against approved wording in the **knowledge base**, and offers a route to a\nhuman through **human handover**. **Agentic workflows with human in the loop approval** handle\nreferred cases, preparing a summary for the credit officer. **PII masking** runs at the gateway,\n**test suites** check that explanations match the reason codes, and deployments can run in the\nEU or UAE region for data residency.",[201,204,207,210],{"question":202,"answer":203},"How much does alternative data increase approvals?","It depends on the population and the data. In simulations it reported to the CFPB, which the CFPB did not separately replicate, Upstart's model approved 27% more applicants than a hypothetical traditional model, with lower average APRs. In a 2025 open banking pilot, Patelco Credit Union saw 12% of subprime and 15% of near prime members move to a higher credit tier. Prove it on your own applicants with a control group.",{"question":205,"answer":206},"Is alternative data credit scoring high risk under the EU AI Act?","Yes, when it evaluates the creditworthiness of natural persons (Annex III point 5(b)). That brings obligations for risk management, data governance, human oversight and logging, deployers must assess the impact on fundamental rights, and affected people have a right to an explanation of the decision.",{"question":208,"answer":209},"Does using machine learning excuse a lender from giving specific decline reasons?","No. Regulation B requires a statement of reasons that is specific and indicates the principal reasons for the adverse action; saying the applicant failed to reach a qualifying score or cites the creditor's internal standards is not enough. Design the model and the reason codes together.",{"question":211,"answer":212},"Which alternative data is safest to start with?","Consumer permissioned bank transaction data is a common starting point, because it measures income and spending directly and the applicant chooses to share it; the Atlanticus and Patelco examples on this page both use it. Device, social and behavioural data carry higher privacy and proxy discrimination risk.",[214,215,216,217,218],"sme-cash-flow-underwriting","adverse-action-explanations","application-and-identity-fraud-detection","conversational-loan-application-intake","model-risk-validation-copilot","2026-09-27","2026-09-26",[222],{"date":219,"note":223},"First published","alternative-data-credit-scoring",[226,256,282,303,324],{"title":227,"useCases":228,"organization":229,"vendors":233,"summary":237,"stage":238,"year":41,"channels":239,"languages":240,"metrics":242,"outcomeDisclosed":243,"sources":244,"verification":251,"grade":253,"id":254,"organizationSlug":255},"Upstart: alternative data and machine learning credit model under a CFPB No Action Letter",[224],{"name":230,"anonymized":231,"country":232,"region":173,"industry":17},"Upstart Network",false,"US",[234],{"name":235,"role":236},"Upstart","in-house","Upstart underwrites and prices consumer loans with a machine learning model that adds alternative data, such as education and employment, to traditional credit data. In 2017 it received the CFPB's first No Action Letter for this model, and in 2019 the CFPB published the access to credit results Upstart reported under that letter: in simulations run against a hypothetical traditional model on the same applicant pool, Upstart's model approved 27% more applicants with 16% lower average APRs, across all tested race, ethnicity and sex segments. The CFPB states it did not separately replicate these simulations. An independent fair lending monitorship of the model published its initial report in April 2021.","scaled",[28],[241],"en",[],true,[245,246],{"url":40,"title":39,"publisher":172},{"url":247,"title":248,"publisher":249,"date":250},"https://www.relmanlaw.com/media/cases/1088_Upstart%20Initial%20Report%20-%20Final.pdf","Fair Lending Monitorship of Upstart Network's Lending Model, initial report","Relman Colfax PLLC","2021-04-14",{"level":252,"checkedAt":220},"source-verified","B","upstart-alternative-data-credit-model",null,{"title":257,"useCases":258,"organization":259,"vendors":261,"summary":268,"stage":269,"year":270,"channels":271,"languages":272,"metrics":273,"outcomeDisclosed":243,"sources":274,"verification":279,"grade":280,"id":281,"organizationSlug":255},"Atlanticus: cash flow underwriting for marginal credit card declines",[224],{"name":260,"anonymized":231,"country":232,"region":173,"industry":17},"Atlanticus",[262,265],{"name":263,"role":264},"Nova Credit","platform",{"name":266,"role":267},"Engine by Gen","integrator","Atlanticus, which runs credit card brands through bank partners for consumers overlooked by prime lenders, added consumer permissioned bank transaction data (Nova Credit Cash Atlas) to its decisions, with consent collected inside an embedded finance marketplace. The vendor reports that 15% of marginal declines, applicants that bureau data alone would have rejected, could be approved profitably with cash flow insights, with no deterioration in credit quality, and that the data is also used to set line sizes and pricing.","production",2025,[28],[241],[],[275],{"url":276,"title":277,"publisher":263,"archivedUrl":278},"https://www.novacredit.com/corporate-blog/case-study-how-atlanticus-unlocked-15-more-approvals-while-maintaining-risk","Case Study: How Atlanticus Unlocked 15% More Approvals While Maintaining Risk Standards","https://web.archive.org/web/20260124014124/https://www.novacredit.com/corporate-blog/case-study-how-atlanticus-unlocked-15-more-approvals-while-maintaining-risk",{"level":252,"checkedAt":220},"C","atlanticus-cash-flow-underwriting",{"title":283,"useCases":284,"organization":285,"vendors":287,"summary":290,"stage":291,"year":270,"channels":292,"languages":293,"metrics":294,"outcomeDisclosed":243,"sources":295,"verification":301,"grade":280,"id":302,"organizationSlug":255},"Patelco Credit Union: open banking credit score pilot",[224],{"name":286,"anonymized":231,"country":232,"region":173,"industry":17},"Patelco Credit Union",[288],{"name":289,"role":264},"VantageScore","Patelco Credit Union tested VantageScore 4plus, a score that combines credit file data with consumer permissioned open banking (bank account cash flow) data, on its own portfolio. In the pilot, 12% of subprime and 15% of near prime members moved to higher credit tiers, and predictive power in originations improved by 4.8% over VantageScore 3.0. It was a portfolio test, not a production rollout. The larger figures in the press release headline (33% and 41%) come from the second pilot at Michigan State University Federal Credit Union, not from Patelco.","pilot",[28],[241],[],[296],{"url":297,"title":298,"publisher":299,"date":300},"https://www.prnewswire.com/news-releases/vantagescore-4plus-pilots-find-33-of-subprime-and-41-of-near-prime-consumers-moved-to-higher-credit-tiers-by-adding-open-banking-data-302487924.html","VantageScore 4plus Pilots Find 33% of Subprime and 41% of Near Prime Consumers Moved to Higher Credit Tiers by Adding Open Banking Data","VantageScore via PR Newswire","2025-06-23",{"level":252,"checkedAt":220},"patelco-credit-union-open-banking-score-pilot",{"title":304,"useCases":305,"organization":306,"vendors":308,"summary":311,"stage":269,"year":312,"channels":313,"languages":314,"metrics":315,"outcomeDisclosed":243,"sources":316,"verification":322,"grade":280,"id":323,"organizationSlug":255},"Golden 1 Credit Union: custom machine learning credit scorecard",[224],{"name":307,"anonymized":231,"country":232,"region":173,"industry":17},"Golden 1 Credit Union",[309],{"name":310,"role":264},"Zest AI","Golden 1, a California credit union with about USD 21 billion in assets, built a custom machine learning credit scorecard with Zest AI, trained on its own members and on other Californians who resembled its membership. It launched on credit cards in December 2022 and extended to unsecured and auto loans. The CEO reported higher approvals overall and a 28% increase in approvals to protected classes of borrowers. The article does not say that the scorecard uses data from outside the credit file, so the record illustrates the machine learning half of this use case.",2024,[28],[241],[],[317],{"url":318,"title":319,"publisher":320,"date":321},"https://www.bankingdive.com/news/golden-1-credit-union-zest-ai-partnership-28-percent-increase-protected-classes-bias-algorithm/709445/","How Golden 1 used AI to find 'good risk'","Banking Dive","2024-03-12",{"level":252,"checkedAt":220},"golden-1-credit-union-ai-credit-scorecard",{"title":325,"useCases":326,"organization":327,"vendors":330,"summary":333,"stage":269,"year":312,"channels":334,"languages":335,"metrics":336,"outcomeDisclosed":243,"sources":337,"verification":343,"grade":280,"id":344,"organizationSlug":255},"GXS Bank: ecosystem data from Grab and Singtel in FlexiLoan credit decisions",[224],{"name":328,"anonymized":231,"country":329,"region":184,"industry":17},"GXS Bank","SG",[331],{"name":332,"role":264},"FICO","GXS Bank, a Singapore digital bank, decisions its FlexiLoan personal loan with user permissioned data from its ecosystem partners Grab and Singtel layered on top of credit bureau scores, to expand credit access to underserved users, such as people starting their careers and entrepreneurs with fluctuating incomes, who were previously overlooked by traditional banks. The FICO decision platform returns credit decisions in milliseconds, and the bank reports onboarding in under three minutes for the vast majority of approved applications. The platform was implemented in three months.",[29,28],[241],[],[338],{"url":339,"title":340,"publisher":341,"date":342},"https://www.theasianbanker.com/press-releases/gxs-bank-achieves-onboarding-efficiency-with-fico-platform","GXS Bank achieves onboarding efficiency with FICO platform","The Asian Banker (FICO press release)","2024-05-30",{"level":252,"checkedAt":220},"gxs-bank-alternative-data-flexiloan",0,[],{"low":348,"high":349},450000,4800000,[351,365,383,415,432],{"slug":214,"title":352,"shortTitle":353,"definition":354,"status":9,"industries":355,"functions":356,"patterns":357,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":359,"publicEvidenceCount":359,"organizations":360,"bestGrade":253,"headline":255,"lastVerified":220,"indexable":243},"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.",[17],[20,21,22],[24,25,358,26],"agentic-workflow",4,[361,362,363,364],"MYbank","National Australia Bank","OakNorth Bank","Sumitomo Mitsui Banking Corporation",{"slug":215,"title":366,"shortTitle":367,"definition":368,"status":9,"industries":369,"functions":370,"patterns":373,"audience":376,"autonomy":377,"adoptionStage":378,"segment":34,"evidenceCount":379,"publicEvidenceCount":379,"organizations":380,"bestGrade":253,"headline":255,"lastVerified":219,"indexable":243},"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.",[17,18],[20,371,372],"regulatory-compliance","customer-service",[374,375,26],"content-generation","rag-knowledge-assistant","employee-facing","copilot","emerging",2,[381,382],"Discover Financial Services","Wells Fargo",{"slug":216,"title":384,"shortTitle":385,"definition":386,"status":9,"industries":387,"functions":391,"patterns":394,"audience":31,"autonomy":32,"adoptionStage":33,"segment":397,"evidenceCount":398,"publicEvidenceCount":398,"organizations":399,"bestGrade":253,"headline":406,"lastVerified":220,"indexable":243},"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.",[17,18,388,389,390],"cross-industry","government","telecommunications",[392,393,20],"fraud-prevention","onboarding-and-kyc",[25,395,396,24],"anomaly-detection","computer-vision","front-office",6,[400,401,402,403,404,405],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":407,"label":408,"unit":409,"n":410,"nUpTo":345,"kind":411,"value":412,"qualifier":413,"claimant":414,"organization":403,"vendorReported":231},"detection-rate-improvement","Detection improvement","multiplier",1,"reported",2.5,"exact","organization",{"slug":217,"title":416,"shortTitle":417,"definition":418,"status":9,"industries":419,"functions":420,"patterns":422,"audience":424,"autonomy":32,"adoptionStage":33,"segment":397,"evidenceCount":398,"publicEvidenceCount":425,"organizations":426,"bestGrade":280,"headline":255,"lastVerified":219,"indexable":243},"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.",[17],[20,421,372],"sales",[26,25,375,423],"voice-agent","customer-facing",5,[427,428,429,430,431],"Absa Bank","Figure","Lloyds Banking Group","Oper Credits","Rocket Mortgage",{"slug":218,"title":433,"shortTitle":434,"definition":435,"status":9,"industries":436,"functions":440,"patterns":441,"audience":376,"autonomy":377,"adoptionStage":378,"segment":442,"evidenceCount":443,"publicEvidenceCount":443,"organizations":444,"bestGrade":253,"headline":255,"lastVerified":448,"indexable":243},"AI copilot for model risk validation and monitoring","Model risk validation","A copilot for independent model validation and review, whether run by a bank's validation function, an external tester or a supervisor, that checks model documentation against the model risk standard, generates and scores challenger tests (for generative AI, often with an LLM as a judge calibrated against human experts), watches production models for drift and drafts and consistency checks the validation report. An accountable validator owns every conclusion.",[17,437,438,439],"insurance","capital-markets","wealth-and-asset-management",[22,371],[358,25,374,395],"second-line",3,[445,446,447],"European Central Bank (ECB Banking Supervision)","Standard Chartered","United Overseas Bank (UOB)","2026-09-28",{"indexable":243,"reasons":450},[],[452,457,462,470,476,482,489,495,499,506,513,518,525,532,538,543,550,556,562,568,574,580,586,591,596,600,605,610,615,622,627,633,639,644],{"id":153,"label":453,"issuer":166,"region":167,"url":454,"description":455,"useCases":456,"indexable":243},"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":154,"label":458,"issuer":166,"region":167,"url":459,"description":460,"useCases":461,"indexable":243},"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":463,"label":464,"issuer":465,"region":466,"url":467,"description":468,"useCases":469,"indexable":243},"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":159,"label":471,"issuer":472,"region":173,"url":473,"description":474,"useCases":475,"indexable":243},"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":477,"label":478,"issuer":166,"region":167,"url":479,"description":480,"useCases":481,"indexable":243},"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":483,"label":484,"issuer":485,"region":167,"url":486,"description":487,"useCases":488,"indexable":243},"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":157,"label":490,"issuer":491,"region":167,"url":492,"description":493,"useCases":494,"indexable":243},"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":158,"label":496,"issuer":183,"region":184,"url":185,"description":497,"useCases":498,"indexable":243},"MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":500,"label":501,"issuer":502,"region":184,"url":503,"description":504,"useCases":505,"indexable":243},"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":507,"label":508,"issuer":509,"region":466,"url":510,"description":511,"useCases":512,"indexable":243},"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":156,"label":514,"issuer":515,"region":173,"url":516,"description":517,"useCases":512,"indexable":243},"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":519,"label":520,"issuer":521,"region":167,"url":522,"description":523,"useCases":524,"indexable":243},"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":526,"label":527,"issuer":528,"region":466,"url":529,"description":530,"useCases":531,"indexable":243},"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":533,"label":534,"issuer":166,"region":167,"url":535,"description":536,"useCases":537,"indexable":243},"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":539,"label":540,"issuer":166,"region":167,"url":541,"description":542,"useCases":537,"indexable":243},"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":544,"label":545,"issuer":546,"region":173,"url":547,"description":548,"useCases":549,"indexable":243},"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":551,"label":552,"issuer":166,"region":167,"url":553,"description":554,"useCases":555,"indexable":243},"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":557,"label":558,"issuer":559,"region":173,"url":560,"description":561,"useCases":555,"indexable":243},"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":563,"label":564,"issuer":565,"region":466,"url":566,"description":567,"useCases":555,"indexable":243},"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":569,"label":570,"issuer":166,"region":167,"url":571,"description":572,"useCases":573,"indexable":243},"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":575,"label":576,"issuer":577,"region":173,"url":578,"description":579,"useCases":573,"indexable":243},"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":581,"label":582,"issuer":183,"region":184,"url":583,"description":584,"useCases":585,"indexable":243},"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":587,"label":588,"issuer":166,"region":167,"url":589,"description":590,"useCases":585,"indexable":243},"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":592,"label":593,"issuer":166,"region":167,"url":594,"description":595,"useCases":585,"indexable":243},"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":155,"label":597,"issuer":178,"region":167,"url":179,"description":598,"useCases":599,"indexable":243},"EBA Guidelines on loan origination and monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":160,"label":601,"issuer":172,"region":173,"url":602,"description":603,"useCases":604,"indexable":243},"ECOA and Regulation B","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":606,"label":607,"issuer":166,"region":167,"url":608,"description":609,"useCases":604,"indexable":243},"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":611,"label":612,"issuer":166,"region":167,"url":613,"description":614,"useCases":398,"indexable":243},"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":616,"label":617,"issuer":618,"region":619,"url":620,"description":621,"useCases":425,"indexable":243},"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":162,"label":623,"issuer":624,"region":167,"url":625,"description":626,"useCases":359,"indexable":243},"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":628,"label":629,"issuer":630,"region":167,"url":631,"description":632,"useCases":359,"indexable":243},"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":634,"label":635,"issuer":636,"region":184,"url":637,"description":638,"useCases":443,"indexable":243},"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":640,"label":641,"issuer":166,"region":167,"url":642,"description":643,"useCases":443,"indexable":243},"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":161,"label":645,"issuer":646,"region":173,"url":647,"description":648,"useCases":443,"indexable":243},"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.",1790598298388]