[{"data":1,"prerenderedAt":563},["ShallowReactive",2],{"uc-auto-loan-underwriting-and-verification":3,"uc-regulations":336},{"useCase":4,"evidence":177,"blitsAiDeployments":236,"benchmarks":237,"indicative":238,"related":241,"indexability":334,"includeUnpublished":183},{"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":80,"feasibility":90,"implementation":102,"risk":137,"blitsAi":153,"faq":155,"related":168,"datePublished":172,"dateModified":172,"lastVerified":172,"changelog":173,"slug":176},"AI underwriting and verification for auto loans","Auto loan underwriting","AI auto loan verification automation","AI verifies income and identity on auto loan applications in real time, clearing stipulations automatically. Ally Financial reports fewer files need manual review.","published","AI that verifies the income, identity, employment and residence data on an auto loan application in real time against supporting documents and external databases, clears routine stipulations automatically, flags fraud patterns such as altered pay stubs, and gives the lender's credit policy engine a verified, structured application instead of a stack of documents a person has to check by hand.",[12,13,14,15],"auto loan document verification","indirect auto lending automation","stipulation clearing AI","auto finance income verification",[17,18],"banking","automotive",[20,21],"lending-and-credit","onboarding-and-kyc",[23,24],"document-processing","classification-and-routing",[26,27],"api","internal-tools","back-office","supervised-agent","early-adopters","lending","An auto loan is usually decided in minutes at the point of sale, whether online or at a dealer,\nand most applications are approved in automated fashion because the loan data is simple and\nstraightforward to verify. But some applications trigger what lenders call a \"stipulation\": a\ncondition, such as proof of income or proof of residence, that has to be cleared before funding,\nfor example because the applicant recently changed jobs or moved. Clearing it has traditionally\nmeant a person opening a pay stub or bank statement, checking it against the application by eye,\nand calling or emailing the dealer if something does not match, while the deal sits unfunded and\nthe buyer waits at the dealership.\n\nPay stub fraud is a known problem at the volumes auto lenders process: fabricated pay stub\ntemplates are easy to find online, and a human reviewer checking documents visually has no way to\ncompare a submission against the wider pattern of previously seen fraudulent templates. At the\nsame time, indirect lenders receive applications from thousands of dealers with wide swings in\nvolume, and adding review staff for every volume spike is not how the business wants to grow.\n\nThe fix is not a different credit decision, it is faster and more reliable verification of the\ndata the decision already runs on: real time checks against external databases and document\nanalysis in place of a person opening one PDF at a time.",[],"1. **Ingest the application and documents.** Pay stubs, bank statements and identity documents\n   submitted online or by a dealer are read and the relevant data points extracted automatically.\n2. **Verify against external sources.** Income, employment, identity and residence data are\n   checked against credit bureau, payroll and other third party databases in real time, in the\n   lender's own policy.\n3. **Screen for fraud patterns.** Documents are compared against known fraudulent templates and\n   checked for internal inconsistencies, such as pay math that does not add up or formatting that\n   does not match the stated employer.\n4. **Clear or route the stipulation.** Applications with no discrepancy clear automatically and\n   move straight to funding; anything uncertain goes to a credit analyst with the specific\n   mismatch highlighted, rather than sending the whole file back for review.\n5. **Feed the credit decision, not replace it.** The lender's own underwriting and credit policy\n   engine makes the approval decision; this layer only verifies that the data behind it is real.",[36,37,38,39],"cost-to-serve","speed","risk-reduction","employee-productivity",[41,42,43,44],"processing-time-reduction","automation-rate","error-reduction","fraud-loss-reduction",{"referenceOrg":46,"inputs":47,"formula":75,"currency":76,"period":77,"resultLabel":78,"caveat":79},"An indirect auto lender processing 20,000 contracts a month",[48,54,61,68],{"key":49,"label":50,"low":51,"high":51,"unit":52,"note":53},"contractsPerMonth","Contracts processed per month",20000,"contracts per month","The reference lender.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"manualReviewShare","Share of contracts that would need a manual stipulation review without automation",0.15,0.35,"fraction of contracts","Editorial assumption, replace with your own stipulation rate. Kept below half of contracts because American Banker reports that at Ally Financial \"most are approved in automated fashion because the loan data is simple and straightforward to verify,\" and only some applications trigger a stipulation.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"reviewsAutomated","Share of manual reviews the verification layer clears without a person",0.4,0.6,"fraction of manual reviews","Editorial assumption, replace with your own measured rate. Ally Financial's chief strategy and corporate development officer described its Informed.IQ deployment as resulting in far fewer loan documents needing manual intervention, without giving a percentage.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"costPerReview","Fully loaded cost of a manual stipulation review",8,20,"USD per review","Editorial assumption for US indirect auto lending operations staff. Replace with your own cost.","contractsPerMonth * 12 * manualReviewShare * reviewsAutomated * costPerReview","USD","per year","Manual stipulation review cost avoided","Gross review labor avoided only. It leaves out software cost, integration work, funding time savings from faster clearing, and any change in downstream fraud or repurchase risk.",[81,86],{"statement":82,"sourceTitle":83,"sourceUrl":84,"year":85},"Informed.IQ, an auto lending document verification vendor, states on its Auto Lending page that \"the company processes 12% of all American auto loans.\"","Auto Lending","https://informediq.com/solutions/auto-lending/",2026,{"statement":87,"sourceTitle":88,"sourceUrl":89,"year":85},"Informed.IQ states on its About Us page that it \"serves 8 of the nation's top 10 auto lenders, many US credit unions, and consumer lenders of all sizes.\"","About Us","https://informediq.com/about-us/",{"complexity":91,"complexityNote":92,"dataPrerequisites":93,"integrations":97},"medium","Document extraction and third party data checks are largely off the shelf; the work is integrating with the lender's loan origination and funding systems and dealer facing portals, and agreeing with credit policy which mismatches can clear automatically versus which must go to a person.",[94,95,96],"The lender's own stipulation rules by loan type and risk tier","Access to credit bureau, payroll and identity verification data sources","A labeled set of historical stipulation files, including known fraud cases, to test accuracy",[98,99,100,101],"Loan origination and funding system","Dealer facing origination portal","Credit bureau and employment or payroll verification services","Fraud and document authenticity data sources",{"steps":103,"guardrails":116,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":127},[104,107,110,113],{"title":105,"detail":106},"Start with one stipulation type","Start with proof of income, the stipulation type the evidence on this page describes; prove accuracy there before adding proof of residence, insurance and other document types.",{"title":108,"detail":109},"Define what clears automatically versus what needs a person","Agree with credit policy which combinations of match confidence and loan risk tier can clear without review, and route everything else to an analyst with the specific mismatch flagged.",{"title":111,"detail":112},"Keep the credit decision separate from verification","The verification layer confirms the data is real; it does not decide approval or terms. Keep that boundary explicit in the design and in what the system is allowed to write back.",{"title":114,"detail":115},"Measure fraud catch rate, not only speed","Track confirmed fraud caught against the historical rate found by manual review, so a faster process is not quietly also a leakier one.",[117,118,119,120],"The verification layer never sets the credit decision, only the data it runs on","Uncertain matches route to a credit analyst with the specific mismatch, not the full file","Document authenticity checks run on every file regardless of application volume","Dealers and applicants can see and correct a data mismatch before a stipulation is declined","A credit analyst reviews every application the system cannot confidently clear, and reviews a sample of automatically cleared files on a schedule to catch drift. Any change to what the system is allowed to clear automatically goes through the same sign off as a change to credit policy itself.",[123,124,125,126],"Share of stipulations cleared without manual review, by document type","Time from application submission to funding","Confirmed fraud caught versus the prior manual review baseline","Dealer and applicant complaints about incorrect declines or delays",[128,131,134],{"title":129,"detail":130},"Auto clearing creeps into risk territory","The threshold for automatic clearing gets loosened informally to hit speed targets, letting weaker matches through. Change the threshold only through the same governance as a credit policy change.",{"title":132,"detail":133},"External data sources go stale or unavailable","A verification data source has an outage or returns stale records, and the system either blocks funding across the book or silently falls back to a weaker check. Monitor source availability and fail safe to manual review, not to automatic clearing.",{"title":135,"detail":136},"Dealer facing errors with no path to fix them","A genuine applicant is flagged by a false mismatch and has no way to correct it before funding stalls. Give dealers and applicants a way to resubmit or explain a flagged document.",{"euAiAct":138,"regulations":141,"guidance":146,"controls":147,"incidents":152},{"tier":139,"basis":140},"context-dependent","Verifying documents and third party data for a person or system to use is a narrow procedural task under Article 6(3) when the credit decision itself is made separately. That derogation does not save the system if it profiles natural persons: Article 6(3)'s last subparagraph makes an Annex III system high risk regardless of the procedural task exception when it does. Calculating an applicant's income and checking it against what people in that job are normally paid, which is how Informed.IQ describes its Ally Financial deployment, evaluates a person's economic situation and is profiling under GDPR Article 4(4), so that step will usually be high risk unless it is strictly limited to checking document authenticity rather than calculating or assessing income. Annex III point 5(b) separately excludes AI used to detect financial fraud from the high risk credit scoring category, which covers this use case's fraud screening step on its own. The tier also changes if the verification result automatically decides or materially narrows a consumer's access to auto financing, since retail auto loan applicants are natural persons: that use falls under Annex III point 5(b) as evaluating creditworthiness directly.",[142,143,144,145],"eu-ai-act","gdpr","us-ecoa-reg-b","us-fcra",[],[148,149,150,151],"Verification kept separate from the credit approval decision in the system design","Human credit analyst review of every uncertain match before a stipulation is declined","Full audit trail from a cleared stipulation back to the data source that cleared it","Monitoring of automatic clearing rate and fraud catch rate for unexplained drift",[],{"howToBuild":154},"On Blits.ai this is an **agentic workflow** that ingests each application's documents and\ncalls a series of **custom functions**, as REST or SQL calls, to extract the data and check it\nagainst credit bureau, payroll and identity verification systems. Applications the workflow\ncannot confidently clear go to a credit analyst through **human in the loop confirmation** above\na configured threshold, with the specific mismatch shown rather than the whole file.\n\nDealer facing status updates can run through the dealer portal's **API** connection, and\n**guardrails** with **PII masking** at the gateway protect income, identity and account data\nbefore it reaches a model. **Run history** keeps a full audit trail of every verification and\nevery analyst decision, and **monitors** run scheduled health checks on the workflow itself so a\nfailure is caught quickly. The platform's **model agnostic** routing lets the extraction model be\nswapped without changing the verification rules or the write back logic.",[156,159,162,165],{"question":157,"answer":158},"Does this system decide whether to approve an auto loan?","No. It verifies that the income, identity and residence data behind the application is real and consistent, and clears routine stipulations. The lender's own credit policy engine makes the approval and pricing decision, on data this layer has checked.",{"question":160,"answer":161},"Which organizations have deployed this?","Ally Financial's chief strategy and corporate development officer described its Informed.IQ deployment as far fewer loan documents needing manual intervention, and Informed.IQ's founder said the platform calculates applicant income with 99% accuracy in the case of Ally Financial. Origence, a lending technology provider for more than 1,130 credit unions, integrated Informed.IQ into its indirect auto lending platform to automate document processing for a network of more than 15,000 dealers; Origence's deployment has no reported outcome.",{"question":163,"answer":164},"How does this differ from mortgage income verification?","The underlying document and data checking task is similar, but auto lending runs on a much faster decision cycle (often minutes, at a dealership or online checkout) and a different regulatory basis, so the stipulation types, fraud patterns and integrations are specific to auto finance origination systems. See mortgage income and document verification for the slower, agency rule driven version of the same job.",{"question":166,"answer":167},"How does this differ from application and identity fraud detection?","They overlap on document forensics: both check pay stubs and bank statements for signs of alteration. This page is about clearing a lender's own stipulations and getting a genuine application funded faster; application and identity fraud detection is about catching forged documents and synthetic identities across a whole application queue, in any lending or onboarding context, not only auto finance.",[169,170,171],"mortgage-income-and-document-verification","application-and-identity-fraud-detection","alternative-data-credit-scoring","2026-09-29",[174],{"date":172,"note":175},"First published","auto-loan-underwriting-and-verification",[178,208],{"title":179,"useCases":180,"organization":181,"vendors":186,"summary":190,"stage":191,"year":192,"channels":193,"languages":194,"metrics":196,"outcomeDisclosed":183,"sources":197,"verification":203,"grade":205,"id":206,"organizationSlug":207},"Origence: indirect auto lending document automation with Informed.IQ",[176],{"name":182,"anonymized":183,"country":184,"region":185,"industry":17},"Origence",false,"US","north-america",[187],{"name":188,"role":189},"Informed.IQ","platform","Origence, a lending technology and services provider for more than 1,130 credit unions serving over 64 million members, partnered with Informed.IQ to power document process automation inside its indirect auto lending platform. The system automatically identifies and classifies documents such as driver's licenses, pay stubs, W2 forms and bank statements, and validates the data against each credit union's financing policies, for a network of more than 15,000 dealers. Origence's chief product officer confirmed the partnership; the post does not report a deployment specific outcome number for Origence, so no metric is recorded.","production",2022,[27],[195],"en",[],[198],{"url":199,"title":200,"publisher":201,"date":202},"https://aws.amazon.com/blogs/machine-learning/informediq-automates-verifications-for-origences-auto-lending-using-machine-learning/","InformedIQ automates verifications for Origence's auto lending using machine learning","Amazon Web Services","2022-10-06",{"level":204,"checkedAt":172},"source-verified","C","origence-informed-iq-auto-lending-document-automation",null,{"title":209,"useCases":210,"organization":211,"vendors":213,"summary":215,"stage":191,"year":216,"channels":217,"languages":218,"metrics":219,"outcomeDisclosed":228,"sources":229,"verification":234,"grade":205,"id":235,"organizationSlug":207},"Ally Financial: real time income verification for auto loans with Informed.IQ",[176],{"name":212,"anonymized":183,"country":184,"region":185,"industry":17},"Ally Financial",[214],{"name":188,"role":189},"Ally Financial, one of the largest US auto lenders, put Informed.IQ's document and data verification software into production in its contract processing centers after a year long proof of concept. The software extracts data from auto loan documents such as pay stubs and compares it against credit bureau and other databases in real time, clearing routine \"stipulations\" so far fewer files need a person to intervene, according to Ally's chief strategy and corporate development officer.",2021,[27],[195],[220],{"kpi":221,"value":222,"unit":223,"qualifier":224,"claimant":225,"quote":226,"sourceUrl":227},"accuracy",99,"percent","exact","vendor","What we do with 99% accuracy in the case of, say, Ally Financial, is calculate applicant income on behalf of Ally Financial in accordance with Ally's policies,","https://www.americanbanker.com/news/how-ally-uses-ai-to-approve-auto-loans",true,[230],{"url":227,"title":231,"publisher":232,"date":233},"How Ally uses AI to approve auto loans","American Banker","2021-05-05",{"level":204,"checkedAt":172},"ally-financial-informed-iq-income-verification",0,[],{"low":239,"high":240},115200,1008000,[242,262,294,315],{"slug":169,"title":243,"shortTitle":244,"definition":245,"status":9,"industries":246,"functions":248,"patterns":250,"audience":28,"autonomy":29,"adoptionStage":251,"segment":31,"evidenceCount":252,"publicEvidenceCount":252,"organizations":253,"bestGrade":205,"headline":256,"lastVerified":172,"indexable":228},"AI income and document verification for mortgage underwriting","Mortgage income verification","AI that classifies the pay stubs, bank statements, tax forms and other documents in a mortgage application, calculates qualifying income under the investor's or agency's own rules, checks the documents for signs of alteration, and hands only the low confidence or unusual files to an underwriter, with every calculated figure linked back to the source page it came from.",[17,247],"real-estate",[20,21,249],"operations",[23,24],"mainstream",2,[254,255],"Haventree Bank","HomeTrust Bank",{"kpi":257,"label":258,"unit":223,"n":259,"nUpTo":236,"kind":260,"value":261,"qualifier":224,"claimant":225,"organization":254,"vendorReported":228},"handling-time-reduction","Handling time reduction",1,"reported",67,{"slug":170,"title":263,"shortTitle":264,"definition":265,"status":9,"industries":266,"functions":271,"patterns":273,"audience":28,"autonomy":29,"adoptionStage":30,"segment":277,"evidenceCount":278,"publicEvidenceCount":278,"organizations":279,"bestGrade":286,"headline":287,"lastVerified":293,"indexable":228},"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.",[17,267,268,269,270],"payments","cross-industry","government","telecommunications",[272,21,20],"fraud-prevention",[23,274,275,276],"anomaly-detection","computer-vision","prediction-and-scoring","front-office",6,[280,281,282,283,284,285],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra","B",{"kpi":288,"label":289,"unit":290,"n":259,"nUpTo":236,"kind":260,"value":291,"qualifier":224,"claimant":292,"organization":283,"vendorReported":183},"detection-rate-improvement","Detection improvement","multiplier",2.5,"organization","2026-09-26",{"slug":171,"title":295,"shortTitle":296,"definition":297,"status":9,"industries":298,"functions":299,"patterns":302,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"evidenceCount":278,"publicEvidenceCount":278,"organizations":304,"bestGrade":286,"headline":311,"lastVerified":293,"indexable":228},"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.",[17,267],[20,300,301],"underwriting","risk-management",[276,23,303],"conversational-agent",[305,306,307,308,309,310],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network","Upstart Holdings",{"kpi":42,"label":312,"unit":223,"n":259,"nUpTo":236,"kind":260,"value":313,"qualifier":314,"claimant":292,"organization":310,"vendorReported":183},"Automation rate",90,"at-least",{"slug":316,"title":317,"shortTitle":318,"definition":319,"status":9,"industries":320,"functions":323,"patterns":324,"audience":28,"autonomy":29,"adoptionStage":30,"segment":28,"evidenceCount":326,"publicEvidenceCount":252,"organizations":327,"bestGrade":205,"headline":330,"lastVerified":333,"indexable":228},"account-servicing-execution","AI for back office account servicing execution","Account servicing execution","AI that executes the servicing requests that land in operations queues, such as address and mandate changes, standing instructions, beneficiary updates, reissues, payoff and reference letters and loan maintenance, by reading the request, checking it against policy and entitlements, and preparing or making the change in core systems under dual control.",[17,321,322],"insurance","wealth-and-asset-management",[249,20],[325,23,24],"agentic-workflow",3,[328,329],"Banco Supervielle","SS&C Technologies",{"kpi":41,"label":331,"unit":223,"n":252,"nUpTo":236,"kind":260,"value":332,"qualifier":224,"claimant":225,"organization":329,"vendorReported":228},"Cycle time reduction",95,"2026-09-27",{"indexable":228,"reasons":335},[],[337,344,349,357,364,371,377,384,392,399,406,413,419,425,432,439,445,452,458,464,470,476,481,488,493,498,503,509,515,520,528,535,541,547,552,557],{"id":142,"label":338,"issuer":339,"region":340,"url":341,"description":342,"useCases":343,"indexable":228},"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":143,"label":345,"issuer":339,"region":340,"url":346,"description":347,"useCases":348,"indexable":228},"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":350,"label":351,"issuer":352,"region":353,"url":354,"description":355,"useCases":356,"indexable":228},"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":358,"label":359,"issuer":360,"region":185,"url":361,"description":362,"useCases":363,"indexable":228},"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":365,"label":366,"issuer":367,"region":340,"url":368,"description":369,"useCases":370,"indexable":228},"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":372,"label":373,"issuer":339,"region":340,"url":374,"description":375,"useCases":376,"indexable":228},"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":378,"label":379,"issuer":380,"region":340,"url":381,"description":382,"useCases":383,"indexable":228},"uk-consumer-duty","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":385,"label":386,"issuer":387,"region":388,"url":389,"description":390,"useCases":391,"indexable":228},"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":393,"label":394,"issuer":395,"region":388,"url":396,"description":397,"useCases":398,"indexable":228},"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":400,"label":401,"issuer":402,"region":185,"url":403,"description":404,"useCases":405,"indexable":228},"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":407,"label":408,"issuer":409,"region":353,"url":410,"description":411,"useCases":412,"indexable":228},"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":414,"label":415,"issuer":339,"region":340,"url":416,"description":417,"useCases":418,"indexable":228},"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":420,"label":421,"issuer":422,"region":340,"url":423,"description":424,"useCases":418,"indexable":228},"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":426,"label":427,"issuer":428,"region":185,"url":429,"description":430,"useCases":431,"indexable":228},"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":433,"label":434,"issuer":435,"region":353,"url":436,"description":437,"useCases":438,"indexable":228},"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":440,"label":441,"issuer":339,"region":340,"url":442,"description":443,"useCases":444,"indexable":228},"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":446,"label":447,"issuer":448,"region":185,"url":449,"description":450,"useCases":451,"indexable":228},"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":453,"label":454,"issuer":455,"region":185,"url":456,"description":457,"useCases":451,"indexable":228},"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":459,"label":460,"issuer":339,"region":340,"url":461,"description":462,"useCases":463,"indexable":228},"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":465,"label":466,"issuer":467,"region":353,"url":468,"description":469,"useCases":463,"indexable":228},"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":144,"label":471,"issuer":472,"region":185,"url":473,"description":474,"useCases":475,"indexable":228},"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":477,"label":478,"issuer":339,"region":340,"url":479,"description":480,"useCases":475,"indexable":228},"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":482,"label":483,"issuer":484,"region":340,"url":485,"description":486,"useCases":487,"indexable":228},"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":489,"label":490,"issuer":387,"region":388,"url":491,"description":492,"useCases":487,"indexable":228},"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":494,"label":495,"issuer":339,"region":340,"url":496,"description":497,"useCases":487,"indexable":228},"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":499,"label":500,"issuer":339,"region":340,"url":501,"description":502,"useCases":487,"indexable":228},"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":504,"label":505,"issuer":339,"region":340,"url":506,"description":507,"useCases":508,"indexable":228},"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.",9,{"id":145,"label":510,"issuer":511,"region":185,"url":512,"description":513,"useCases":514,"indexable":228},"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":516,"label":517,"issuer":339,"region":340,"url":518,"description":519,"useCases":278,"indexable":228},"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":521,"label":522,"issuer":523,"region":524,"url":525,"description":526,"useCases":527,"indexable":228},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":529,"label":530,"issuer":531,"region":340,"url":532,"description":533,"useCases":534,"indexable":228},"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":536,"label":537,"issuer":538,"region":340,"url":539,"description":540,"useCases":534,"indexable":228},"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":542,"label":543,"issuer":544,"region":388,"url":545,"description":546,"useCases":326,"indexable":228},"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":548,"label":549,"issuer":339,"region":340,"url":550,"description":551,"useCases":326,"indexable":228},"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":553,"label":554,"issuer":339,"region":340,"url":555,"description":556,"useCases":326,"indexable":228},"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":558,"label":559,"issuer":560,"region":185,"url":561,"description":562,"useCases":326,"indexable":228},"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.",1790683494655]