[{"data":1,"prerenderedAt":567},["ShallowReactive",2],{"uc-mortgage-income-and-document-verification":3,"uc-regulations":346},{"useCase":4,"evidence":182,"blitsAiDeployments":247,"benchmarks":248,"indicative":260,"related":263,"indexability":344,"includeUnpublished":188},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":24,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":41,"valueDrivers":42,"kpis":47,"indicativeValue":53,"macroEstimates":81,"feasibility":82,"implementation":94,"risk":132,"blitsAi":157,"faq":159,"related":172,"datePublished":177,"dateModified":177,"lastVerified":177,"changelog":178,"slug":181},"AI income and document verification for mortgage underwriting","Mortgage income verification","AI mortgage income verification automation","AI reads mortgage documents to calculate qualifying income. HomeTrust estimates 8,500 hours saved yearly; Haventree cut bank statement review 67%, Ocrolus reports.","published","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.",[12,13,14,15,16],"mortgage document automation","AI income calculation for mortgages","bank statement analysis for underwriting","automated income verification","mortgage document classification",[18,19],"banking","real-estate",[21,22,23],"lending-and-credit","onboarding-and-kyc","operations",[25,26],"document-processing","classification-and-routing",[28,29],"internal-tools","api","back-office","supervised-agent","mainstream","lending","A mortgage application arrives as a stack of documents built for a person to read: pay stubs,\nW2s, tax returns, bank statements, profit and loss statements for the self employed, and letters\nexplaining anything unusual. An underwriting team has to relabel and index every page, work out\nwhich income sources count under the applicable agency or investor guide (Fannie Mae, Freddie Mac,\nFHA, VA and USDA each have their own rules for calculating qualifying income), add them up by\nhand and run the whole calculation again every time a new document arrives to clear a condition.\n\nThe Mortgage Bankers Association put per loan production costs at USD 11,102 in the fourth\nquarter of 2025, across independent mortgage banks and mortgage subsidiaries of chartered banks.\nIncome verification and document handling add to that cost and to how long a file takes: a loan\nsits waiting on a person to open a PDF, find the right page and type numbers into a spreadsheet,\nand it sits there again every time a condition brings in one more document.\n\nThe task is well suited to AI because the rules are explicit and public (published agency income\nguides) and the documents are structured enough to extract reliably, but it stays a credit\ndecision: the qualifying income figure that comes out of this process can decide whether a\nborrower gets the loan they applied for, at the rate and term they were quoted.",[36],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"Per loan production costs decreased to USD 11,102 per loan in the fourth quarter of 2025, down from USD 11,109 per loan in the third quarter, according to the Mortgage Bankers Association's Quarterly Mortgage Bankers Performance Report.","IMBs Report Production Profits in Fourth Quarter of 2025","https://www.mba.org/news-and-research/newsroom/news/2026/03/18/imbs-report-production-profits-in-fourth-quarter-of-2025",2026,"1. **Classify and index.** Documents arriving by upload portal, email or fax are split into\n   pages, classified by type (pay stub, W2, bank statement, tax return) and indexed against the\n   loan file automatically.\n2. **Extract and calculate.** Wage, self employed, rental and other income types are extracted\n   from the source documents and calculated against the applicable agency or investor income\n   guide, with a confidence score on every figure.\n3. **Check document authenticity.** Bank statement deposits and document metadata are checked for\n   internal consistency and compared against known alteration patterns, flagging suspicious files\n   for a fraud specialist before they reach underwriting.\n4. **Route by confidence.** High confidence calculations flow into the loan origination system\n   with the source page attached for every figure; anything below the threshold goes to an\n   underwriter with the extracted data prefilled, not a blank form.\n5. **Clear conditions without retyping.** When a new document arrives to satisfy a stipulation,\n   the same pipeline reruns, updates the calculation and shows the underwriter exactly what\n   changed.",[43,44,45,46],"cost-to-serve","speed","employee-productivity","risk-reduction",[48,49,50,51,52],"processing-time-reduction","handling-time-reduction","cost-reduction","accuracy","hours-saved",{"referenceOrg":54,"inputs":55,"formula":76,"currency":77,"period":78,"resultLabel":79,"caveat":80},"A community bank originating 5,000 mortgage and home equity loans a year",[56,62,69],{"key":57,"label":58,"low":59,"high":59,"unit":60,"note":61},"loansPerYear","Loans processed per year",5000,"loans per year","The reference lender.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"hoursSavedPerLoan","Underwriting and income verification hours saved per loan",0.5,1.2,"hours per loan","Editorial assumption, replace with your own. Haventree Bank's up to three hours to under one hour reduction is an upper bound for its largest files and covers bank statement review only, not the full income verification workflow. HomeTrust Bank's reported saving (8,500 hours and USD 90,000 a year across its loan processing teams) cannot be converted into a per loan figure because its loan volume is not disclosed, so it does not set this range.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"costPerHour","Fully loaded cost of underwriting and processing staff time",35,55,"USD per hour","Editorial assumption for US retail mortgage operations staff. Replace with your own fully loaded cost.","loansPerYear * hoursSavedPerLoan * costPerHour","USD","per year","Underwriting and processing hours cost avoided","Gross labor time avoided only. It leaves out software licensing, integration work, and any revenue effect of a faster, more predictable closing timeline. The hours saved per loan input is an editorial assumption for this reference lender, not derived from the evidence on this page: HomeTrust Bank's own reported saving is USD 90,000 a year, at a loan volume Ocrolus does not disclose.",[],{"complexity":83,"complexityNote":84,"dataPrerequisites":85,"integrations":89},"medium","Document classification and extraction are largely off the shelf; the real work is mapping each investor's and agency's own income calculation rules, integrating with the loan origination system, and building an escalation path that gives the underwriter prefilled data rather than a blank recalculation.",[86,87,88],"Current agency and investor income calculation rules (Fannie Mae, Freddie Mac, FHA, VA, USDA)","A labeled set of historical loan files to test classification and income calculation accuracy","A document authenticity or fraud watchlist feed for altered statement detection",[90,91,92,93],"Loan origination system (for example Encompass by ICE Mortgage Technology)","Document upload portal and email or fax intake","Income and employment verification services","Underwriter task queue for flagged and low confidence files",{"steps":95,"guardrails":111,"humanInTheLoop":116,"kpisToInstrument":117,"failureModes":122},[96,99,102,105,108],{"title":97,"detail":98},"Start with one income type and one loan program","Prove accuracy on wage earner W2 income for conventional loans before expanding to self employed, rental and government loan income, where the rules are more complex.",{"title":100,"detail":101},"Set a confidence threshold and route below it to a person","Every calculation carries a confidence score. Anything below the threshold goes to an underwriter with the extracted figures and source pages attached, not a blank recalculation.",{"title":103,"detail":104},"Keep every figure traceable to its source page","Underwriters must be able to click from a calculated income figure back to the exact page and line of the document it came from, so a challenge takes seconds, not a document search.",{"title":106,"detail":107},"Route flagged files into the underwriter's existing queue","Low confidence and suspected fraud cases land in the loan origination system's own task list, so underwriters do not have to work a separate tool for exceptions.",{"title":109,"detail":110},"Reverify accuracy against agency guide updates","Agency and investor income guides change; schedule a recurring check of the calculation rules against the current published guides, not only against last year's test set.",[112,113,114,115],"A person, not the model, makes and signs the final income and eligibility determination","Every calculated figure links to the exact source document page it came from","Confidence thresholds route uncertain extractions to a person instead of a best guess","Document authenticity checks stay on and are monitored even under application volume pressure","An underwriter reviews and signs off on every loan file; the AI reduces retyping and first pass assembly, it does not remove the underwriter's judgment. Low confidence calculations, unusual income types and any authenticity flag go to a person before the file moves forward, and a sample of high confidence calculations is checked periodically against a manual recalculation.",[118,119,120,121],"Income calculation accuracy against a manually reviewed sample, by income type","Time from document receipt to a cleared condition","Share of files requiring manual income recalculation","Document authenticity flags raised versus confirmed fraud",[123,126,129],{"title":124,"detail":125},"Income rules go stale","Agency and investor income guidelines change periodically. An unmaintained rules engine keeps calculating against an old version and understates or overstates qualifying income. Review the rules against the agencies' published guides on a fixed schedule.",{"title":127,"detail":128},"Confidence miscalibrated on unusual income","A model that is confidently wrong on seasonal, gig or multiple job income routes bad numbers straight through. Measure accuracy per income type, not only in aggregate.",{"title":130,"detail":131},"Fraud checks disabled under volume pressure","Document authenticity checks get treated as an optional step when application volume spikes, letting altered bank statements and pay stubs through. Keep the check mandatory and monitor its trigger rate for unexplained drops.",{"euAiAct":133,"regulations":136,"guidance":144,"controls":151,"incidents":156},{"tier":134,"basis":135},"context-dependent","Classification and indexing of documents for a person to review may fall under the Article 6(3) derogation for narrow procedural tasks, if the provider documents that assessment. That derogation does not apply once the system profiles a natural person: calculating a named borrower's qualifying income from their pay stubs and bank statements evaluates that person's economic situation, which is profiling under GDPR Article 4(4). A system intended to calculate qualifying income for the credit decision is high risk under Annex III point 5(b), evaluating the creditworthiness of natural persons, whether or not a person reviews its output. Human oversight of that output is a separate obligation under Article 14, not a way to take the system out of the high risk category.",[137,138,139,140,141,142,143],"eu-ai-act","gdpr","eu-mortgage-credit-directive","us-ecoa-reg-b","us-fcra","eba-loan-origination","uk-consumer-duty",[145],{"title":146,"issuer":147,"region":148,"url":149,"note":150},"Guidelines on loan origination and monitoring (EBA/GL/2020/06)","European Banking Authority","europe","https://www.eba.europa.eu/activities/single-rulebook/regulatory-activities/credit-risk/guidelines-loan-origination-and-monitoring","Sets governance and creditworthiness assessment standards for loan origination, including automated elements of the process.",[152,153,154,155],"Human underwriter sign off on every loan file, with the AI's role limited to assembly and calculation","Full audit trail from every calculated figure back to its source document page","Periodic sampling of automated calculations against an independent manual recalculation","Mandatory document authenticity checks with monitored trigger rates",[],{"howToBuild":158},"On Blits.ai this runs as an **agentic workflow** built on **custom functions**: REST calls and\ncustom code, in an isolated sandbox with versioning, hold the investor's and agency's own\nincome calculation rules as versioned code, not a black box model, and write the result back to\nthe loan file through a REST call to the loan origination system. Calculations that need a\nsecond look go through **human in the loop approval** above a configurable threshold, so an\nunderwriter approves before a figure is marked complete, rather than reviewing a blank file.\n\n**PII masking** protects sensitive data in the messages and data the workflow processes, and\n**monitors** run scheduled checks that compare a sample of automated calculations against\nexpected outputs, alerting on failure so a rule change or a model update is caught quickly.\n**Test suites** run automated evaluations against the workflow before a change goes live. The\nplatform is model agnostic, so the model behind the workflow can be swapped without rebuilding\nthe income rule logic.",[160,163,166,169],{"question":161,"answer":162},"Does AI income verification replace the underwriter?","No. It removes the manual relabeling, indexing and retyping, and gives the underwriter a calculation with every figure linked to its source page. The underwriter still reviews and signs the file, and any low confidence or unusual case is routed to a person before the file moves forward.",{"question":164,"answer":165},"How much time can this save on a mortgage file?","Ocrolus reports that Haventree Bank cut bank statement review time by 67%, taking a review that took up to three hours down to under one hour, and that HomeTrust Bank estimated annual savings of 8,500 hours across its loan processing teams after automating income calculation and document handling.",{"question":167,"answer":168},"Which income types are hardest to automate?","Self employed, rental and other variable income sources are harder than a single wage earner pay stub, because they depend on multiple documents and more agency specific rules. Start with the simplest income type and expand once accuracy is proven.",{"question":170,"answer":171},"Is this a high risk system under the EU AI Act?","It depends on what the system is intended to do. Pure document classification and extraction for a person to review can fall under the Article 6(3) derogation for narrow procedural tasks. A system intended to calculate a borrower's qualifying income for the credit decision profiles a natural person's economic situation and is high risk under Annex III point 5(b), evaluating creditworthiness, whether or not a person reviews the output; Article 14 then requires human oversight of that high risk system.",[173,174,175,176],"home-loan-assistant-and-prequalification","credit-memo-drafting-agent","intelligent-document-processing","property-valuation-support","2026-09-29",[179],{"date":177,"note":180},"First published","mortgage-income-and-document-verification",[183,218],{"title":184,"useCases":185,"organization":186,"vendors":191,"summary":195,"stage":196,"year":40,"channels":197,"languages":198,"metrics":200,"outcomeDisclosed":208,"sources":209,"verification":213,"grade":215,"id":216,"organizationSlug":217},"Haventree Bank: AI bank statement income analysis for mortgage underwriting",[181],{"name":187,"anonymized":188,"country":189,"region":190,"industry":18},"Haventree Bank",false,"CA","north-america",[192],{"name":193,"role":194},"Ocrolus","platform","Haventree Bank, a Canadian alternative residential mortgage lender, had its underwriting team manually transferring data from bank statements into an Excel income calculator, taking up to three hours per file. It adopted Ocrolus to automate bank statement income analysis and document authenticity checks. Ocrolus reports the bank cut bank statement review time by 67% and moved from an inconsistent 9 to 12 months of statements reviewed per file to a consistent full 12 months, without adding headcount as loan volumes grew.","production",[28],[199],"en",[201],{"kpi":49,"value":202,"unit":203,"qualifier":204,"claimant":205,"quote":206,"sourceUrl":207},67,"percent","exact","vendor","Since adopting Ocrolus, Haventree Bank has cut bank statement review time by 67%.","https://www.ocrolus.com/customer-stories/haventree-bank-cuts-bank-statement-review-time-ocrolus/",true,[210],{"url":207,"title":211,"publisher":193,"date":212},"Haventree Bank cuts bank statement review time 67% with Ocrolus","2026-08-14",{"level":214,"checkedAt":177},"source-verified","C","haventree-bank-ocrolus-bank-statement-automation",null,{"title":219,"useCases":220,"organization":221,"vendors":224,"summary":226,"stage":196,"year":227,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":208,"sources":241,"verification":245,"grade":215,"id":246,"organizationSlug":217},"HomeTrust Bank: AI income calculation and document automation for mortgage loans",[181],{"name":222,"anonymized":188,"country":223,"region":190,"industry":18},"HomeTrust Bank","US",[225],{"name":193,"role":194},"HomeTrust Bank, a North Carolina community bank, used to spend over four hours a week per team member manually relabeling mortgage documents and a similar amount of time verifying income by hand. It adopted Ocrolus, integrated with its Encompass loan origination system, to classify documents and calculate wage, self employed, rental and other income automatically across loan origination, processing and underwriting. Ocrolus reports the bank estimates annual savings of 8,500 hours and USD 90,000 through the resulting efficiencies.",2024,[28],[199],[231,237],{"kpi":52,"value":232,"unit":233,"qualifier":234,"period":78,"claimant":205,"quote":235,"sourceUrl":236},8500,"hours","approximately","HomeTrust Bank estimates annual savings of 8,500 hours across loan processing teams and $90,000 through efficiencies in document processing.","https://www.ocrolus.com/customer-stories/hometrust-ai-document-automation/",{"kpi":238,"value":239,"unit":240,"currency":77,"qualifier":234,"period":78,"claimant":205,"quote":235,"sourceUrl":236},"cost-savings",90000,"currency",[242],{"url":236,"title":243,"publisher":193,"date":244},"HomeTrust Bank transforms mortgage loan processing and underwriting with Ocrolus' AI-driven document automation","2024-07-03",{"level":214,"checkedAt":177},"hometrust-bank-ocrolus-income-automation",0,[249,255],{"kpi":49,"label":250,"unit":203,"aggregate":208,"higherIsBetter":208,"n":251,"nUpTo":247,"median":202,"min":202,"max":202,"byClaimant":252,"vendorOnly":208,"points":253},"Handling time reduction",1,{"organization":247,"vendor":251,"regulator":247,"independent":247},[254],{"evidenceId":216,"organization":187,"value":202,"qualifier":204,"claimant":205,"grade":215,"pooled":208},{"kpi":52,"label":256,"unit":233,"aggregate":188,"higherIsBetter":208,"n":251,"nUpTo":247,"median":232,"min":232,"max":232,"byClaimant":257,"vendorOnly":208,"points":258},"Hours saved",{"organization":247,"vendor":251,"regulator":247,"independent":247},[259],{"evidenceId":246,"organization":222,"value":232,"qualifier":234,"claimant":205,"grade":215,"pooled":208},{"low":261,"high":262},87500,330000,[264,285,304,331],{"slug":173,"title":265,"shortTitle":266,"definition":267,"status":9,"industries":268,"functions":269,"patterns":272,"audience":276,"autonomy":31,"adoptionStage":277,"segment":278,"evidenceCount":279,"publicEvidenceCount":279,"organizations":280,"bestGrade":215,"headline":217,"lastVerified":284,"indexable":208},"AI home loan assistant with pre qualification","Home loan assistant","A customer facing assistant that answers home loan questions (rates, loan to value, fees, the documents needed), runs indicative affordability and borrowing estimates from the bank's published rules, and books the customer with a mortgage specialist, grounded in the bank's current, versioned product and policy documents.",[18,19],[21,270,271],"sales","customer-service",[273,274,275],"rag-knowledge-assistant","conversational-agent","voice-agent","customer-facing","early-adopters","front-office",3,[281,282,283],"Figure","Loft","Safe Rate","2026-09-27",{"slug":174,"title":286,"shortTitle":287,"definition":288,"status":9,"industries":289,"functions":290,"patterns":293,"audience":296,"autonomy":297,"adoptionStage":277,"segment":298,"evidenceCount":299,"publicEvidenceCount":299,"organizations":300,"bestGrade":303,"headline":217,"lastVerified":284,"indexable":208},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.",[18],[21,291,292],"underwriting","risk-management",[25,294,273,295],"agentic-workflow","content-generation","employee-facing","copilot","specialized-businesses",2,[301,302],"Banestes","DBS Bank","B",{"slug":175,"title":305,"shortTitle":306,"definition":307,"status":9,"industries":308,"functions":313,"patterns":316,"audience":30,"autonomy":31,"adoptionStage":32,"evidenceCount":318,"publicEvidenceCount":319,"organizations":320,"bestGrade":303,"headline":326,"lastVerified":284,"indexable":208},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[309,310,311,312],"cross-industry","government","automotive","manufacturing",[23,314,315],"case-management","finance-and-accounting",[25,317,26],"computer-vision",7,5,[321,322,323,324,325],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":51,"label":327,"unit":203,"n":251,"nUpTo":247,"kind":328,"value":329,"qualifier":330,"claimant":205,"organization":321,"vendorReported":208},"Accuracy","reported",90,"at-least",{"slug":176,"title":332,"shortTitle":333,"definition":334,"status":9,"industries":335,"functions":336,"patterns":337,"audience":296,"autonomy":31,"adoptionStage":32,"segment":33,"evidenceCount":279,"publicEvidenceCount":279,"organizations":339,"bestGrade":303,"headline":217,"lastVerified":343,"indexable":208},"AI support for property valuation and appraisal","Property valuation support","AI, most often an automated valuation model, that estimates a property's market value from comparable sales, property characteristics and location data, and either offers to replace a full appraisal within set limits or gives a professional valuer a first pass estimate, the closest comparable sales and a reliability score, so the valuer's time goes to the properties that need a person's judgment.",[19,18,310],[21,314,23],[338,26],"prediction-and-scoring",[340,341,342],"Fannie Mae","Riverside County Assessor-County Clerk-Recorder","Valuation Office Agency","2026-09-28",{"indexable":208,"reasons":345},[],[347,353,358,366,373,380,386,392,400,407,414,421,427,433,440,447,453,460,466,472,478,484,489,494,499,504,509,515,520,526,533,540,546,552,557,561],{"id":137,"label":348,"issuer":349,"region":148,"url":350,"description":351,"useCases":352,"indexable":208},"EU AI Act","European Union","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":138,"label":354,"issuer":349,"region":148,"url":355,"description":356,"useCases":357,"indexable":208},"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":359,"label":360,"issuer":361,"region":362,"url":363,"description":364,"useCases":365,"indexable":208},"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":367,"label":368,"issuer":369,"region":190,"url":370,"description":371,"useCases":372,"indexable":208},"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":374,"label":375,"issuer":376,"region":148,"url":377,"description":378,"useCases":379,"indexable":208},"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":381,"label":382,"issuer":349,"region":148,"url":383,"description":384,"useCases":385,"indexable":208},"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":143,"label":387,"issuer":388,"region":148,"url":389,"description":390,"useCases":391,"indexable":208},"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":393,"label":394,"issuer":395,"region":396,"url":397,"description":398,"useCases":399,"indexable":208},"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":401,"label":402,"issuer":403,"region":396,"url":404,"description":405,"useCases":406,"indexable":208},"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":408,"label":409,"issuer":410,"region":190,"url":411,"description":412,"useCases":413,"indexable":208},"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":415,"label":416,"issuer":417,"region":362,"url":418,"description":419,"useCases":420,"indexable":208},"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":422,"label":423,"issuer":349,"region":148,"url":424,"description":425,"useCases":426,"indexable":208},"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":428,"label":429,"issuer":430,"region":148,"url":431,"description":432,"useCases":426,"indexable":208},"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":434,"label":435,"issuer":436,"region":190,"url":437,"description":438,"useCases":439,"indexable":208},"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":441,"label":442,"issuer":443,"region":362,"url":444,"description":445,"useCases":446,"indexable":208},"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":448,"label":449,"issuer":349,"region":148,"url":450,"description":451,"useCases":452,"indexable":208},"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":454,"label":455,"issuer":456,"region":190,"url":457,"description":458,"useCases":459,"indexable":208},"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":461,"label":462,"issuer":463,"region":190,"url":464,"description":465,"useCases":459,"indexable":208},"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":467,"label":468,"issuer":349,"region":148,"url":469,"description":470,"useCases":471,"indexable":208},"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":473,"label":474,"issuer":475,"region":362,"url":476,"description":477,"useCases":471,"indexable":208},"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":479,"issuer":480,"region":190,"url":481,"description":482,"useCases":483,"indexable":208},"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":485,"label":486,"issuer":349,"region":148,"url":487,"description":488,"useCases":483,"indexable":208},"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":142,"label":490,"issuer":147,"region":148,"url":491,"description":492,"useCases":493,"indexable":208},"EBA Guidelines on loan origination and monitoring","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":495,"label":496,"issuer":395,"region":396,"url":497,"description":498,"useCases":493,"indexable":208},"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":500,"label":501,"issuer":349,"region":148,"url":502,"description":503,"useCases":493,"indexable":208},"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":505,"label":506,"issuer":349,"region":148,"url":507,"description":508,"useCases":493,"indexable":208},"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":510,"label":511,"issuer":349,"region":148,"url":512,"description":513,"useCases":514,"indexable":208},"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":141,"label":516,"issuer":517,"region":190,"url":518,"description":519,"useCases":318,"indexable":208},"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.",{"id":521,"label":522,"issuer":349,"region":148,"url":523,"description":524,"useCases":525,"indexable":208},"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.",6,{"id":527,"label":528,"issuer":529,"region":530,"url":531,"description":532,"useCases":319,"indexable":208},"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":534,"label":535,"issuer":536,"region":148,"url":537,"description":538,"useCases":539,"indexable":208},"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":541,"label":542,"issuer":543,"region":148,"url":544,"description":545,"useCases":539,"indexable":208},"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":547,"label":548,"issuer":549,"region":396,"url":550,"description":551,"useCases":279,"indexable":208},"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":553,"label":554,"issuer":349,"region":148,"url":555,"description":556,"useCases":279,"indexable":208},"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":139,"label":558,"issuer":349,"region":148,"url":559,"description":560,"useCases":279,"indexable":208},"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":562,"label":563,"issuer":564,"region":190,"url":565,"description":566,"useCases":279,"indexable":208},"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.",1790683492044]