[{"data":1,"prerenderedAt":640},["ShallowReactive",2],{"uc-mortgage-underwriting-decision-engine":3,"uc-regulations":423},{"useCase":4,"evidence":213,"blitsAiDeployments":292,"benchmarks":293,"indicative":305,"related":308,"indexability":421,"includeUnpublished":219},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":18,"patterns":22,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":52,"macroEstimates":88,"feasibility":89,"implementation":103,"risk":149,"blitsAi":187,"faq":189,"related":202,"datePublished":208,"dateModified":208,"lastVerified":208,"changelog":209,"slug":212},"AI mortgage underwriting decision engine","Mortgage underwriting decision engine","AI reads a mortgage file, applies investor guidelines and returns a decision. Better says its Tinman engine does it in as little as 47 seconds.","published","A mortgage underwriting decision engine is AI that reads a loan file, applies the lender's and investors' guidelines, and returns an approve, refer or open conditions status for each loan. It sits above document and income checks, and underwriters handle the files it refers.",[11,12,13,14],"AI mortgage underwriter","automated mortgage underwriting","AI credit decision engine for mortgages","underwriting orchestrator",[16,17],"banking","real-estate",[19,20,21],"lending-and-credit","underwriting","operations",[23,24,25],"agentic-workflow","document-processing","prediction-and-scoring",[27,28],"api","internal-tools","back-office","supervised-agent","emerging","mortgage-underwriting","A mortgage file passes through several people before a lender can say yes. A processor collects\ndocuments, an underwriter checks income, assets, credit and the property against the guidelines of\nthe investor who will buy the loan, and conditions go back to the borrower until the file is clean.\nEach investor has its own overlays, so the same borrower can be eligible for one buyer and not for\nanother. The process is slow and sequential, and the underwriter is the scarce person in it.\n\nBetter, a US digital lender, puts the average for the traditional mortgage industry at 21 days. The\ndocument and income steps have specialist tools (see the related page on income and document\nverification). The step that remains is the decision: reading the whole file against the\nguidelines, listing what is missing and saying whether the loan can be approved. That is the part\na decision engine takes on.",[35],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"Better says the average for the traditional mortgage industry is 21 days, against a median of 2 minutes and 24 seconds for its own Tinman engine.","Better Announces First Conversational Credit Decision Engine in ChatGPT with OpenAI","https:\u002F\u002Finvestors.better.com\u002F2026-03-05-Better-Announces-First-Conversational-Credit-Decision-Engine-in-ChatGPT-with-OpenAI",2026,"1. **Keep one live record of the loan.** The system holds a snapshot of the file, with its facts,\n   documents, actions and outstanding conditions, and updates it as documents arrive.\n2. **Read the documents.** Specialized agents read the credit, income, collateral and asset\n   documents in the file and write the facts they find into the record.\n3. **Apply the guidelines.** An orchestrator routes the file to agents that check it against the\n   guidelines of the investors and programs the lender sells to, including the lender's own\n   overlays. Better says its engine covers the guidelines of more than 45 institutional investors.\n4. **Return a decision ready status.** The result is an approval, a referral or a list of open\n   conditions, supported by a decision tree.\n5. **Refer what needs judgment.** Files with exceptions, conflicting documents or unusual income go\n   to an underwriter with the evidence already assembled.\n6. **Feed back.** Underwriter overrides and investor repurchase or defect findings go back to improve\n   the rules and the readers.",[42,43,44,45],"speed","cost-to-serve","employee-productivity","customer-experience",[47,48,49,50,51],"automation-rate","cycle-time-days","productivity-gain","cost-savings","accuracy",{"referenceOrg":53,"inputs":54,"formula":83,"currency":84,"period":85,"resultLabel":86,"caveat":87},"A mortgage lender funding 12,000 loans a year",[55,61,69,76],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"loans","Funded loans per year",12000,"loans per year","The reference lender.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67,"sourceUrl":68},"automatedShare","Share of loan files whose underwriting is automated",0.2,0.4,"fraction of loan files","The high value follows the line on NEO Home Loans' site that Better's Tinman automates underwriting for nearly 40% of loan files, which is a product claim and not a measured NEO result. The low value is an editorial assumption for a first year.","https:\u002F\u002Fneohomeloans.com\u002F08\u002F14\u002F2025\u002Fhow-neo-home-loans-powered-by-better-is-empowering-loan-officers-not-replacing-them\u002F",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"hoursPerLoan","Underwriter hours per file today",2,4,"hours per loan","Editorial assumption. Replace with a time study of your own underwriting desk.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"costPerHour","Fully loaded cost of an underwriting hour",45,80,"USD per hour","Editorial assumption. Replace with your own fully loaded cost.","loans * automatedShare * hoursPerLoan * costPerHour","USD","per year","Underwriting capacity released","Capacity released, not cash saved, unless headcount changes. It leaves out the effects of faster decisions on pull through and on borrower experience, the cost of the platform, integration and model governance, and repurchase risk if automated decisions are wrong.",[],{"complexity":90,"complexityNote":91,"dataPrerequisites":92,"integrations":97},"high","The engine has to read every document type in a mortgage file and encode the guidelines of each investor and program the lender sells to, and those guidelines change often. It needs a loan origination system that exposes the file and its conditions by API, a rules and overlay store the credit team owns, pricing and credit data feeds, and a model risk process. Better says Tinman is trained on more than a decade of its own mortgage data.",[93,94,95,96],"Current guidelines and overlays for each investor and product, in a form that can be versioned","A set of past loan files with the underwriter's final decision and conditions, to test the engine against","Document images and extracted data for credit, income, assets and property","Records of repurchase demands, defects and quality control findings on past loans",[98,99,100,101,102],"Loan origination system and borrower portal","Credit bureau, income and asset verification and appraisal providers","Pricing engine and investor eligibility data","Document storage and condition tracking","Quality control and secondary marketing systems",{"steps":104,"guardrails":123,"humanInTheLoop":129,"kpisToInstrument":130,"failureModes":136},[105,108,111,114,117,120],{"title":106,"detail":107},"Run the engine in shadow beside underwriters","For a few hundred files, let the engine decide without acting on it, and compare its decision, conditions and cited rules with the underwriter's. Sort the disagreements by cause.",{"title":109,"detail":110},"Automate the clean files first","Start with salaried borrowers, standard products and complete documents. Refer anything that is self employed, has unusual assets or has a document conflict.",{"title":112,"detail":113},"Make every decision traceable to a rule","Store the facts used, the guideline and version applied, and the document page each fact came from, so that a quality control reviewer or an investor can reconstruct the decision.",{"title":115,"detail":116},"Own the guideline layer","Put one named person or team in charge of investor guidelines and overlays, with a change log and a test set that runs before every update.",{"title":118,"detail":119},"Add adverse action reasons","For a decline or counteroffer, produce the specific principal reasons from the rules that failed, in language a borrower can read, and test them with compliance.",{"title":121,"detail":122},"Widen by program and investor","Add products and investors one at a time, with a defect rate target and a release gate for each, rather than turning on every program together.",[124,125,126,127,128],"No automatic decline; declines and counteroffers are confirmed by an underwriter","Every decision stores the guideline version, the facts used and their document source","Fair lending testing on decisions, referrals and conditions across protected groups before release and on a schedule","Borrower data masked before it reaches a model that does not need it","Investor and agency requirements for AI governance checked before use on loans sold to them","Licensed underwriters decide referrals, exceptions and declines, and review a weekly sample of automated approvals. The credit team approves guideline changes, and quality control compares automated and manual loans for defects and repurchase demands.",[131,132,133,134,135],"Share of files decided without an underwriter touch, and share referred","Time from complete file to decision, and conditions per file","Defect and repurchase rate of automated versus manual loans","Agreement between the engine and a second underwriter on a held out sample","Loans funded per underwriter, tracked with quality",[137,140,143,146],{"title":138,"detail":139},"Guideline drift","Investors can change overlays often. An engine running on last quarter's rules approves loans that no buyer will take. Tie each guideline to an owner and a review date.",{"title":141,"detail":142},"Speed reported without quality","Productivity per underwriter rises when more files are automated, even if more of them are defective. Report defects and repurchases alongside throughput.",{"title":144,"detail":145},"Unexplainable decline","A decision that cannot be turned into specific reasons for the borrower fails adverse action and fair lending review. Keep the rule trail for every decision.",{"title":147,"detail":148},"Chat front end hides the control point","When loan officers query the engine from a general purpose assistant, access, data retention and logging move outside the loan origination system. Keep decisions in the system of record.",{"euAiAct":150,"regulations":152,"guidance":161,"controls":179,"incidents":186},{"tier":90,"basis":151},"Annex III point 5(b) lists AI systems used to evaluate the creditworthiness of natural persons or establish their credit score. A system that approves, refers or conditions a mortgage for a consumer falls under it, with risk management, data governance, logging, human oversight and conformity assessment obligations.",[153,154,155,156,157,158,159,160],"eu-ai-act","gdpr","eu-mortgage-credit-directive","eba-loan-origination","us-ecoa-reg-b","us-fcra","us-sr-11-7","nist-ai-rmf",[162,168,173],{"title":163,"issuer":164,"region":165,"url":166,"note":167},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 5(b) names creditworthiness evaluation of natural persons as high risk.",{"title":169,"issuer":170,"region":165,"url":171,"note":172},"Guidelines on loan origination and monitoring (EBA\u002FGL\u002F2020\u002F06)","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Factivities\u002Fsingle-rulebook\u002Fregulatory-activities\u002Fcredit-risk\u002Fguidelines-loan-origination-and-monitoring","Sets expectations for credit decisions, automated models and creditworthiness assessment in EU lending.",{"title":174,"issuer":175,"region":176,"url":177,"note":178},"Consumer Financial Protection Circular 2022-03: adverse action notification requirements for credit decisions based on complex algorithms (issued May 2022, withdrawn May 2025)","Consumer Financial Protection Bureau","north-america","https:\u002F\u002Fwww.consumerfinance.gov\u002Fcompliance\u002Fcirculars\u002Fcircular-2022-03-adverse-action-notification-requirements-in-connection-with-credit-decisions-based-on-complex-algorithms\u002F","States that a creditor using a complex algorithm must still give specific and accurate reasons for an adverse action. The circular was withdrawn in 2025, and the underlying Equal Credit Opportunity Act and Regulation B requirement still applies.",[180,181,182,183,184,185],"AI inventory entry with an accountable credit and compliance owner","Model risk validation of the decision rules and readers, with periodic revalidation","Versioned guideline and overlay library with change log and test set","Decision record per loan: facts, document sources, guideline version and reasons","Fair lending monitoring of approvals, referrals, conditions and declines","Vendor and model provider due diligence, including data retention terms",[],{"howToBuild":188},"On Blits.ai this runs as an **agentic workflow** that the loan origination system triggers through\nan **API token** whenever a file changes. The file's documents go through **document ingestion**,\nagents with **structured output** write the facts they find into the file record, and the lender's investor guidelines and overlays sit in the\n**knowledge base** with **hybrid retrieval** so each decision cites the rule it used. **Custom\nfunctions** call the credit, income, asset and pricing services, and a **SQL knowledge base**\ngives the agents read access to the loan record and past decisions.\n\nReferrals, exceptions and any decline go through **human in the loop approval** to a licensed\nunderwriter. **PII masking** at the gateway masks identifiers before they reach a model, every\nrun has a full **audit trail and trace**, and **test suites** replay a labelled set of past\nloans before each guideline change goes live. **Run history analytics** show the referral share over time, and **monitors** run scheduled checks\nof the workflow against expected answers on a fixed test file. The platform is **model agnostic**, so reading and rule checking can use different models, and it\ncan run in **EU or UAE data residency** regions.",[190,193,196,199],{"question":191,"answer":192},"How fast can AI underwrite a mortgage?","Better says its Tinman engine can complete the process in as little as 47 seconds, with a median of 2 minutes and 24 seconds, against an average of 21 days that it gives for the traditional mortgage industry. These are the company's figures from its March 2026 launch and are not an independent measure. The 21 days is an industry wide figure from the company, not a like for like comparison with the engine's timing.",{"question":194,"answer":195},"Does it replace underwriters?","Not in the figures reported here. Better reports that NEO Home Loans increased loans funded per underwriter by nearly 50% within six months of rolling out Tinman, which means the underwriters are still there and handle more loans. NEO's website also repeats Better's description of Tinman as automating underwriting for nearly 40% of loan files, a product claim and not a figure measured at NEO.",{"question":197,"answer":198},"Is AI mortgage underwriting high risk under the EU AI Act?","Yes for consumer lending. Annex III point 5(b) lists AI used to evaluate the creditworthiness of natural persons. The lender needs risk management, data governance, logging, human oversight and a conformity assessment, and in the EU the EBA loan origination guidelines also apply.",{"question":200,"answer":201},"What happens when the engine declines a borrower?","The lender still owes the borrower specific reasons. In the United States the Equal Credit Opportunity Act and Regulation B require them even when a complex model made the decision, so the engine has to keep the rule trail that produces the reasons.",[203,204,205,206,207],"mortgage-income-and-document-verification","home-loan-assistant-and-prequalification","adverse-action-explanations","property-valuation-support","credit-memo-drafting-agent","2026-10-13",[210],{"date":208,"note":211},"First published","mortgage-underwriting-decision-engine",[214,260],{"title":215,"useCases":216,"organization":217,"vendors":221,"summary":224,"stage":225,"year":39,"channels":226,"languages":227,"metrics":229,"outcomeDisclosed":238,"sources":239,"verification":255,"grade":257,"id":258,"organizationSlug":259},"Better: Tinman AI underwriting engine for mortgage and home equity loans",[212],{"name":218,"anonymized":219,"country":220,"region":176,"industry":16},"Better Home & Finance Holding Company",false,"US",[222],{"name":218,"role":223},"in-house","Better built Tinman, an AI platform trained on its own mortgage data that keeps a live snapshot of each loan file and uses an underwriting orchestrator to route work to specialized agents that read documents, apply investor guidelines and return a decision ready status with a decision tree. In March 2026 it released the engine as an app inside ChatGPT Enterprise for loan officers at other lenders, who connect their own guidelines, pricing and CRM data. Better reports that loans from Tinman partners made up $646 million, 44% of its funded loan volume, in the fourth quarter of 2025.","scaled",[27,28],[228],"en",[230],{"kpi":48,"value":231,"unit":232,"qualifier":233,"period":234,"baseline":235,"claimant":236,"quote":237,"sourceUrl":38},2.4,"minutes","exact","median per loan, reported at the March 2026 launch","An average of 21 days for the traditional mortgage industry, according to the company","organization","in as little as 47 seconds and a median of 2 minutes and 24 seconds compared to the average of 21 days for the traditional mortgage industry.",true,[240,242,246,251],{"url":38,"title":37,"publisher":218,"date":241},"2026-03-05",{"url":243,"title":244,"publisher":245},"https:\u002F\u002Fwww.housingwire.com\u002Farticles\u002Fbetter-chatgpt-conversational-credit-decision-engine-tinman-ai\u002F","Better launches ChatGPT-based mortgage credit decision engine","HousingWire",{"url":247,"title":248,"publisher":249,"date":250},"https:\u002F\u002Fwww.sec.gov\u002FArchives\u002Fedgar\u002Fdata\u002F1835856\u002F000162828026017501\u002Fbetrearningsrelease_q42025.htm","Better Home & Finance Holding Company Announces Fourth Quarter 2025 Results (Form 8-K, Exhibit 99.1)","Better Home & Finance Holding Company via SEC EDGAR","2026-03-13",{"url":252,"title":253,"publisher":254},"https:\u002F\u002Fbetter.com\u002Fcontent\u002Fai-mortgage-lending","How AI mortgage lending is transforming the home loan process","Better",{"level":256,"checkedAt":208},"source-verified","B","better-tinman-ai-mortgage-underwriting",null,{"title":261,"useCases":262,"organization":263,"vendors":265,"summary":268,"stage":269,"year":270,"channels":271,"languages":272,"metrics":273,"outcomeDisclosed":238,"sources":281,"verification":290,"grade":257,"id":291,"organizationSlug":259},"NEO Home Loans: Tinman AI platform for underwriting, powered by Better",[212],{"name":264,"anonymized":219,"country":220,"region":176,"industry":16},"NEO Home Loans",[266],{"name":218,"role":267},"platform","NEO Home Loans, a US mortgage lender with a network of loan officers and branches, runs its origination on Better's Tinman platform. NEO's website describes Tinman as automating underwriting for nearly 40% of loan files and cutting fulfillment costs by up to $1,400 per funded loan, a product description from Better that NEO repeats and not a result measured at NEO. Better reports that within six months of fully rolling out, NEO increased loans funded per officer by 91%, per processor by 17% and per underwriter by nearly 50%. The partnership began in January 2025 and had grown from a $1.50 billion to a $2.97 billion run rate by March 2026, according to Better.","production",2025,[28],[228],[274],{"kpi":49,"value":275,"unit":276,"qualifier":277,"period":278,"claimant":279,"quote":280,"sourceUrl":247},50,"percent","approximately","loans funded per underwriter, within six months of full rollout","vendor","Within six months of fully rolling out, NEO increased loans funded per officer by 91%, per processor by 17%, and per underwriter by nearly 50%, powered by Tinman AI",[282,285,286],{"url":68,"title":283,"publisher":264,"date":284},"NEO Powered by Better: Technology That Supports LOs","2025-08-14",{"url":247,"title":248,"publisher":249,"date":250},{"url":287,"title":288,"publisher":218,"date":289},"https:\u002F\u002Finvestors.better.com\u002F2026-05-07-Better-Home-Finance-Holding-Company-Announces-First-Quarter-2026-Results","Better Home & Finance Holding Company Announces First Quarter 2026 Results","2026-05-07",{"level":256,"checkedAt":208},"neo-home-loans-tinman-ai-underwriting",0,[294,300],{"kpi":48,"label":295,"unit":232,"aggregate":219,"higherIsBetter":219,"n":296,"nUpTo":292,"median":231,"min":231,"max":231,"byClaimant":297,"vendorOnly":219,"points":298},"Cycle time",1,{"organization":296,"vendor":292,"regulator":292,"independent":292},[299],{"evidenceId":258,"organization":218,"value":231,"qualifier":233,"claimant":236,"grade":257,"pooled":238},{"kpi":49,"label":301,"unit":276,"aggregate":238,"higherIsBetter":238,"n":296,"nUpTo":292,"median":275,"min":275,"max":275,"byClaimant":302,"vendorOnly":238,"points":303},"Productivity gain",{"organization":292,"vendor":296,"regulator":292,"independent":292},[304],{"evidenceId":291,"organization":264,"value":275,"qualifier":277,"claimant":279,"grade":257,"pooled":238},{"low":306,"high":307},216000,1536000,[309,338,362,379,400],{"slug":203,"title":310,"shortTitle":311,"definition":312,"status":8,"industries":313,"functions":314,"patterns":316,"audience":29,"autonomy":30,"adoptionStage":318,"segment":319,"evidenceCount":320,"publicEvidenceCount":320,"organizations":321,"bestGrade":331,"headline":332,"lastVerified":337,"indexable":238},"AI income and document verification for mortgage underwriting","Mortgage income verification","AI income and document verification is software that classifies a mortgage application's documents and calculates the borrower's qualifying income for underwriters. It classifies the pay stubs, bank statements, tax forms and other documents, applies the investor's or agency's own income rules, checks documents for signs of alteration, and sends only low confidence or unusual files to an underwriter, with every calculated figure linked to its source page.",[16,17],[19,315,21],"onboarding-and-kyc",[24,317],"classification-and-routing","mainstream","lending",9,[322,323,324,325,326,327,328,329,330],"American Federal Mortgage Corporation","Deephaven Mortgage","Haventree Bank","HomeTrust Bank","Key Mortgage Services, Inc.","MCM Holdings, Inc.","Nations Direct Mortgage","Volo Loans","Westpac","C",{"kpi":333,"label":334,"unit":276,"n":73,"nUpTo":72,"kind":335,"value":336,"qualifier":233,"claimant":279,"organization":259,"vendorReported":238},"handling-time-reduction","Handling time reduction","median",78.5,"2026-09-29",{"slug":204,"title":339,"shortTitle":340,"definition":341,"status":8,"industries":342,"functions":343,"patterns":346,"audience":350,"autonomy":30,"adoptionStage":351,"segment":352,"evidenceCount":353,"publicEvidenceCount":353,"organizations":354,"bestGrade":257,"headline":259,"lastVerified":361,"indexable":238},"AI home loan assistant with pre qualification","Home loan assistant","An AI home loan assistant with pre qualification answers a customer's home loan questions and runs indicative borrowing estimates, then books the customer with a mortgage specialist. It covers rates, loan to value, fees and the documents needed, runs the affordability estimate from the bank's published rules, and stays grounded in the bank's current, versioned product and policy documents.",[16,17],[19,344,345],"sales","customer-service",[347,348,349],"rag-knowledge-assistant","conversational-agent","voice-agent","customer-facing","early-adopters","front-office",6,[355,356,357,358,359,360],"Better.com","Figure","Loft","NatWest Group","Newrez","Safe Rate","2026-09-27",{"slug":205,"title":363,"shortTitle":364,"definition":365,"status":8,"industries":366,"functions":368,"patterns":370,"audience":372,"autonomy":373,"adoptionStage":31,"segment":319,"evidenceCount":374,"publicEvidenceCount":374,"organizations":375,"bestGrade":257,"headline":259,"lastVerified":361,"indexable":238},"AI drafted explanations for credit declines and adverse actions","Adverse action explanations","AI drafted explanations for credit declines and adverse actions come from an assistant that turns a credit model's reason codes into an accurate, specific and readable explanation for the customer. It explains a decline, reduced limit or repricing, and produces a matching internal rationale for the file, without adding any reason the model did not actually produce.",[16,367],"payments",[19,369,345],"regulatory-compliance",[371,347,348],"content-generation","employee-facing","copilot",3,[376,377,378],"Block, Inc.","Discover Financial Services","Wells Fargo",{"slug":206,"title":380,"shortTitle":381,"definition":382,"status":8,"industries":383,"functions":385,"patterns":387,"audience":372,"autonomy":30,"adoptionStage":318,"segment":319,"evidenceCount":388,"publicEvidenceCount":388,"organizations":389,"bestGrade":398,"headline":259,"lastVerified":399,"indexable":238},"AI support for property valuation and appraisal","Property valuation support","AI property valuation support, most often an automated valuation model, estimates a property's market value from comparable sales, characteristics and location data. It 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 properties that need a person's judgment.",[17,16,384],"government",[19,386,21],"case-management",[25,317],8,[390,391,392,393,394,395,396,397],"Fannie Mae","Freddie Mac","Redfin","Riverside County Assessor-County Clerk-Recorder","Rocket Companies","Valuation Office Agency","Vurderingsstyrelsen","Zillow Group","A","2026-09-28",{"slug":207,"title":401,"shortTitle":402,"definition":403,"status":8,"industries":404,"functions":405,"patterns":407,"audience":372,"autonomy":373,"adoptionStage":351,"segment":408,"evidenceCount":353,"publicEvidenceCount":353,"organizations":409,"bestGrade":257,"headline":416,"lastVerified":361,"indexable":238},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","AI credit underwriting and memo drafting is an AI agent that drafts a committee ready credit memo for a corporate borrower, for the relationship and credit teams to review. It gathers the borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and links every figure to its source for the team to challenge, complete and sign.",[16],[19,20,406],"risk-management",[24,23,347,371],"specialized-businesses",[410,411,412,413,414,415],"Banesco USA","Banestes","Citi","DBS Bank","Kuwait Finance House","Shinhan Bank",{"kpi":417,"label":418,"unit":276,"n":296,"nUpTo":292,"kind":419,"value":420,"qualifier":233,"claimant":279,"organization":414,"vendorReported":238},"processing-time-reduction","Cycle time reduction","reported",96,{"indexable":238,"reasons":422},[],[424,429,434,442,448,455,461,468,476,483,489,495,501,508,515,522,528,534,541,546,551,556,562,567,572,577,583,588,593,599,607,611,617,623,629,634],{"id":153,"label":425,"issuer":164,"region":165,"url":426,"description":427,"useCases":428,"indexable":238},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":154,"label":430,"issuer":164,"region":165,"url":431,"description":432,"useCases":433,"indexable":238},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":435,"label":436,"issuer":437,"region":438,"url":439,"description":440,"useCases":441,"indexable":238},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":160,"label":443,"issuer":444,"region":176,"url":445,"description":446,"useCases":447,"indexable":238},"NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":449,"label":450,"issuer":451,"region":165,"url":452,"description":453,"useCases":454,"indexable":238},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":456,"label":457,"issuer":164,"region":165,"url":458,"description":459,"useCases":460,"indexable":238},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":462,"label":463,"issuer":464,"region":165,"url":465,"description":466,"useCases":467,"indexable":238},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":469,"label":470,"issuer":471,"region":472,"url":473,"description":474,"useCases":475,"indexable":238},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":477,"label":478,"issuer":479,"region":472,"url":480,"description":481,"useCases":482,"indexable":238},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":159,"label":484,"issuer":485,"region":176,"url":486,"description":487,"useCases":488,"indexable":238},"SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":490,"label":491,"issuer":164,"region":165,"url":492,"description":493,"useCases":494,"indexable":238},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":496,"label":497,"issuer":498,"region":438,"url":499,"description":500,"useCases":494,"indexable":238},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":502,"label":503,"issuer":504,"region":165,"url":505,"description":506,"useCases":507,"indexable":238},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":509,"label":510,"issuer":511,"region":176,"url":512,"description":513,"useCases":514,"indexable":238},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":516,"label":517,"issuer":518,"region":438,"url":519,"description":520,"useCases":521,"indexable":238},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":523,"label":524,"issuer":164,"region":165,"url":525,"description":526,"useCases":527,"indexable":238},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":529,"label":530,"issuer":531,"region":176,"url":532,"description":533,"useCases":527,"indexable":238},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":535,"label":536,"issuer":537,"region":176,"url":538,"description":539,"useCases":540,"indexable":238},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":542,"label":543,"issuer":164,"region":165,"url":544,"description":545,"useCases":540,"indexable":238},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":547,"label":548,"issuer":164,"region":165,"url":549,"description":550,"useCases":540,"indexable":238},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":157,"label":552,"issuer":175,"region":176,"url":553,"description":554,"useCases":555,"indexable":238},"ECOA and Regulation B","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":557,"label":558,"issuer":559,"region":438,"url":560,"description":561,"useCases":555,"indexable":238},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":156,"label":563,"issuer":170,"region":165,"url":564,"description":565,"useCases":566,"indexable":238},"EBA Guidelines on loan origination and monitoring","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":568,"label":569,"issuer":164,"region":165,"url":570,"description":571,"useCases":566,"indexable":238},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":573,"label":574,"issuer":164,"region":165,"url":575,"description":576,"useCases":566,"indexable":238},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":578,"label":579,"issuer":471,"region":472,"url":580,"description":581,"useCases":582,"indexable":238},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":584,"label":585,"issuer":164,"region":165,"url":586,"description":587,"useCases":582,"indexable":238},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":158,"label":589,"issuer":590,"region":176,"url":591,"description":592,"useCases":388,"indexable":238},"Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":594,"label":595,"issuer":164,"region":165,"url":596,"description":597,"useCases":598,"indexable":238},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":600,"label":601,"issuer":602,"region":603,"url":604,"description":605,"useCases":606,"indexable":238},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":155,"label":608,"issuer":164,"region":165,"url":609,"description":610,"useCases":73,"indexable":238},"EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":612,"label":613,"issuer":614,"region":165,"url":615,"description":616,"useCases":73,"indexable":238},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":618,"label":619,"issuer":620,"region":165,"url":621,"description":622,"useCases":73,"indexable":238},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":624,"label":625,"issuer":626,"region":472,"url":627,"description":628,"useCases":374,"indexable":238},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":630,"label":631,"issuer":164,"region":165,"url":632,"description":633,"useCases":374,"indexable":238},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":635,"label":636,"issuer":637,"region":176,"url":638,"description":639,"useCases":374,"indexable":238},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904360152]