[{"data":1,"prerenderedAt":585},["ShallowReactive",2],{"uc-home-loan-assistant-and-prequalification":3,"uc-regulations":381},{"useCase":4,"evidence":202,"blitsAiDeployments":280,"benchmarks":281,"indicative":287,"related":290,"indexability":379,"includeUnpublished":208},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":27,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":85,"feasibility":86,"implementation":100,"risk":143,"blitsAi":179,"faq":181,"related":191,"datePublished":197,"dateModified":197,"lastVerified":197,"changelog":198,"slug":201},"AI home loan assistant with pre qualification","Home loan assistant","AI mortgage assistant for pre qualification","An AI home loan assistant answers rate and document questions and gives indicative borrowing estimates, handing qualified customers to a mortgage specialist.","published","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.",[12,13,14,15],"mortgage chatbot","home loan pre qualification assistant","mortgage enquiry agent","borrowing power assistant",[17,18],"banking","real-estate",[20,21,22],"lending-and-credit","sales","customer-service",[24,25,26],"rag-knowledge-assistant","conversational-agent","voice-agent",[28,29,30,31],"web-chat","mobile-app","whatsapp","voice","customer-facing","supervised-agent","early-adopters","front-office","A home loan is a large, long commitment, and the questions start well before an application: how\nmuch can I borrow, what deposit do I need, fixed or variable, what happens when my fixed rate\nends, which documents will you want. Customers research when it suits them, often outside the\nhours when specialists are available, and a slow or vague answer can send them to a broker or a\ncompetitor.\n\nEvery hour a specialist spends on early questions, or on applicants who were unlikely to qualify,\nis an hour not spent on applications. And because rates and credit policy change, a generic\nassistant, or a web page that is out of date, can give answers that are wrong in a way that\nmatters: a borrowing estimate that is too high, or a rate that no longer exists.",[],"1. **Answer from current, approved content.** Questions about rates, fees, loan to value limits,\n   product features and documents are answered by retrieval over versioned product and policy\n   documents, with the source and date shown, and a refusal when the content does not cover it.\n2. **Estimate, clearly labelled.** Borrowing power and repayment estimates come from the bank's\n   published calculator logic, called as a tool, never from the model's own arithmetic, and are\n   shown as indicative, not an offer.\n3. **Pre qualify against published rules.** Basic checks (deposit, income bands, residency,\n   property type) come from a rules service and tell the customer what is likely to be needed,\n   without a credit decision.\n4. **Prepare the handover.** The assistant captures the customer's situation and questions,\n   lists the documents they will need and books a call or meeting with a specialist.\n5. **Serve existing borrowers too.** Fixed rate expiry, switching products, extra repayments and\n   offset questions come from the same content, with account specific actions behind\n   authentication.",[40,41,42,43],"revenue-growth","customer-experience","employee-productivity","speed",[45,46,47,48,49],"conversion-rate-uplift","response-time-reduction","interactions-handled","customer-satisfaction","accuracy",{"referenceOrg":51,"inputs":52,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A lender handling 100,000 home loan enquiries a year",[53,59,66,73],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"enquiries","Home loan enquiries per year",100000,"enquiries per year","The reference lender.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"handledShare","Share of enquiries answered without a specialist",0.3,0.5,"fraction of enquiries","Editorial assumption; early questions about rates, fees and documents are the ones the assistant can answer.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"minutesPerEnquiry","Specialist minutes per early enquiry",10,20,"minutes per enquiry","Editorial assumption, replace with your own time data.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerMinute","Fully loaded cost of a specialist minute",1,1.5,"USD per minute","Editorial assumption, replace with your own fully loaded cost.","enquiries * handledShare * minutesPerEnquiry * costPerMinute","USD","per year","Specialist time freed from early enquiries","Counts specialist time only, which is capacity freed rather than cash saved unless staffing changes. It leaves out extra settled loans from faster, out of hours answers, retention of borrowers at fixed rate expiry and the cost of the AI and content upkeep.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"medium","Answering from documents is straightforward. The hard parts are keeping rate and policy content current to the day, reusing the bank's own calculators instead of letting the model compute, and a clean booking handover to specialists.",[90,91,92,93],"Current rate sheets, product terms and credit policy summaries with owners and effective dates","The bank's borrowing power and repayment calculator logic as a callable service","Published eligibility criteria for pre qualification","Specialist calendars and booking rules",[95,96,97,98,99],"Rates and product content management","Calculator and rules services","Scheduling system for specialists and brokers","CRM for leads and handover summaries","Loan origination for existing application status, behind authentication",{"steps":101,"guardrails":117,"humanInTheLoop":123,"kpisToInstrument":124,"failureModes":130},[102,105,108,111,114],{"title":103,"detail":104},"Build the content spine first","Collect every rate sheet, product term and policy summary, assign an owner and an effective date to each, and remove anything unofficial. The assistant is only as good as this set.",{"title":106,"detail":107},"Wrap the calculators as tools","Expose the bank's existing borrowing power and repayment calculators to the assistant, so every number matches what a specialist would show.",{"title":109,"detail":110},"Write the boundaries","Decide what the assistant may say about eligibility and what is reserved for a specialist, and phrase estimates as indicative every time.",{"title":112,"detail":113},"Make booking the default next step","End qualified conversations with a specialist booking and a summary, so the conversation turns into an application rather than a dead end.",{"title":115,"detail":116},"Test with rate changes","Include rate change days in the test plan: update the content, rerun the test suite and check that no old rate survives anywhere.",[118,119,120,121,122],"Rates, fees and limits only from dated, approved content, with a refusal when not covered","All numbers from the bank's calculators, labelled as indicative estimates","No credit decision or approval language","Personal financial details masked in prompts and logs","Handover to a specialist for complex situations and vulnerability signals","Mortgage specialists own advice, applications and every credit decision. Product owners approve the content set and the calculators, and a sample of conversations is reviewed weekly for accuracy and for any wording that sounds like advice or approval.",[125,126,127,128,129],"Share of enquiries answered without a specialist, with repeat contacts counted","Specialist bookings and applications started per conversation","Accuracy of answers on a weekly checked sample, including rates quoted","Out of hours share of conversations","Complaints mentioning the assistant",[131,134,137,140],{"title":132,"detail":133},"Yesterday's rate","A superseded rate stays in the index. Version content with effective dates and retire old versions on the day of change.",{"title":135,"detail":136},"The model does the maths","The assistant calculates a repayment itself and gets it wrong. Route every number through the calculator tool.",{"title":138,"detail":139},"Advice by accident","The assistant recommends a product for a person's circumstances in a market where that is regulated advice. Keep to general information and hand over for recommendations.",{"title":141,"detail":142},"Booking dead ends","The customer is told a specialist will call and nobody does. Book into real calendars and track kept appointments.",{"euAiAct":144,"regulations":147,"guidance":155,"controls":172,"incidents":178},{"tier":145,"basis":146},"context-dependent","Answering questions and giving indicative estimates from published rules is limited risk with an Article 50(1) disclosure that the customer is talking to an AI system. If the assistant evaluates an individual's creditworthiness to decide or filter access to a loan, it falls under Annex III point 5(b) and is high risk.",[148,149,150,151,152,153,154],"eu-ai-act","gdpr","eba-loan-origination","uk-consumer-duty","dora","eu-mortgage-credit-directive","us-ecoa-reg-b",[156,162,168],{"title":157,"issuer":158,"region":159,"url":160,"note":161},"Directive 2014/17/EU on credit agreements for consumers relating to residential immovable property","European Union","europe","https://eur-lex.europa.eu/eli/dir/2014/17/oj","The Mortgage Credit Directive sets rules on advertising, standard information and creditworthiness assessment that the assistant's answers must respect.",{"title":163,"issuer":164,"region":165,"url":166,"note":167},"Responsible lending","Australian Securities and Investments Commission","asia-pacific","https://asic.gov.au/regulatory-resources/credit/responsible-lending/","Example of national responsible lending obligations that shape what pre qualification may say.",{"title":169,"issuer":158,"region":159,"url":170,"note":171},"Annex III, high risk AI systems referred to in Article 6(2)","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Annex III of Regulation (EU) 2024/1689 (the AI Act). Point 5(b) covers creditworthiness evaluation of natural persons.",[173,174,175,176,177],"AI disclosure and a statement that estimates are indicative","Content inventory with owners, effective dates and retirement on change","Calculator and rules services under change control and tested","Log of every estimate shown, with the inputs used","Weekly accuracy sampling and complaint monitoring",[],{"howToBuild":180},"On Blits.ai this is an **AI agent** with a **knowledge base** of rate sheets, product terms and\npolicy summaries, retrieved with hybrid search, using **document version control** so superseded\ncontent is replaced on the day of a rate change. **Custom functions** call the bank's borrowing\npower and repayment calculators and the specialist booking system, so every number and every\nappointment comes from the bank's own systems. Structured output can hand a clean summary to CRM.\n\nThe assistant runs on **web chat**, **WhatsApp** and **voice** telephony, inside the bank's own\nmobile app through the **API channel**, and a **digital human** can present it on a website. **Guardrails** block approval\nlanguage and advice phrasing, **PII masking** protects income and debt details, and **human\nhandover** connects the customer to a specialist. **Test suites** with LLM grading and\nknowledge base evidence check answers after every content change, and **monitors** run scheduled\nchecks that the assistant quotes current rates.",[182,185,188],{"question":183,"answer":184},"Can a home loan chatbot tell me how much I can borrow?","It can give an indicative estimate from the bank's own calculator and published criteria, clearly labelled as such. A real borrowing amount needs a full application and a credit assessment by the bank.",{"question":186,"answer":187},"Who uses AI assistants for mortgages today?","Examples include Safe Rate in the US, whose AI assistant answers questions about rates and lenders, and Loft in Brazil, whose Gemini assistant lets real estate brokers run about 900 home financing simulations a week on WhatsApp, according to Google Cloud. Figure uses AI chatbots in home equity lending. None of these sources report conversion results.",{"question":189,"answer":190},"How do you keep answers current when rates change?","Treat rates and policy as versioned content with owners and effective dates, retire old versions on the day of change and rerun an automated test set that checks the quoted rates.",[192,193,194,195,196],"conversational-loan-application-intake","branch-and-appointment-booking-agent","digital-onboarding-assistant","inbound-lead-qualification-agent","proactive-outbound-engagement-agent","2026-09-27",[199],{"date":197,"note":200},"First published","home-loan-assistant-and-prequalification",[203,230,261],{"title":204,"useCases":205,"organization":206,"vendors":211,"summary":215,"stage":216,"year":217,"channels":218,"languages":219,"metrics":220,"outcomeDisclosed":208,"sources":221,"verification":225,"grade":227,"id":228,"organizationSlug":229},"Figure: AI chatbots for home equity lending",[192,201],{"name":207,"anonymized":208,"country":209,"region":210,"industry":17},"Figure",false,"US","north-america",[212],{"name":213,"role":214},"Google Cloud","model-provider","Figure, a US fintech that offers home equity lines of credit, uses Gemini models to run chatbots that simplify and speed up the lending experience for consumers and for its own staff. No outcome figures were published.","production",2025,[],[],[],[222],{"url":223,"title":224,"publisher":213},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","1,302 real-world gen AI use cases from the world's leading organizations",{"level":226,"checkedAt":197},"source-verified","C","figure-lending-chatbots",null,{"title":231,"useCases":232,"organization":233,"vendors":237,"summary":240,"stage":216,"year":217,"channels":241,"languages":242,"metrics":244,"outcomeDisclosed":253,"sources":254,"verification":258,"grade":227,"id":260,"organizationSlug":229},"Loft: Gemini assistant for mortgage simulations over WhatsApp",[201],{"name":234,"anonymized":208,"country":235,"region":236,"industry":18},"Loft","BR","latin-america",[238],{"name":213,"role":239},"platform","Loft, a real estate technology and financial services company active in Brazil and Mexico, moved its data to Google Cloud and built the Assistente Loft on Gemini. Brokers at the real estate agencies connected to Loft (about 9,000, according to Google Cloud) use it on WhatsApp, by text or voice, to compare home financing conditions from different banks in seconds, so a buyer knows their borrowing power before choosing a property. Google Cloud reports about 900 financing simulations a week on the company's WhatsApp channel.",[30],[243],"pt",[245],{"kpi":47,"value":246,"unit":247,"qualifier":248,"period":249,"claimant":250,"quote":251,"sourceUrl":252},900,"count","approximately","per week, home financing simulations on WhatsApp","vendor","Cerca de 900 simulações de financiamento por semana no WhatsApp e corretores de 9 mil imobiliárias conectados","https://cloud.google.com/customers/intl/pt-br/loft",true,[255,257],{"url":252,"title":256,"publisher":213},"Loft migra para a nuvem e adota IA para melhorar o dia a dia de corretores e clientes",{"url":223,"title":224,"publisher":213},{"level":226,"checkedAt":259},"2026-09-26","loft-whatsapp-mortgage-simulations",{"title":262,"useCases":263,"organization":264,"vendors":266,"summary":268,"stage":216,"year":217,"channels":269,"languages":270,"metrics":272,"outcomeDisclosed":208,"sources":273,"verification":278,"grade":227,"id":279,"organizationSlug":229},"Safe Rate: AI mortgage assistant for rate comparison and refinance quotes",[201],{"name":265,"anonymized":208,"country":209,"region":210,"industry":17},"Safe Rate",[267],{"name":213,"role":214},"Google Cloud describes Safe Rate as a digital mortgage lender that uses Gemini models to create an AI mortgage agent with chat features called \"Beat this Rate\" and \"Refinance Me\", which let borrowers compare rates and get a personalised quote in under 30 seconds. Safe Rate's own website now presents it as a US mortgage shopping service that ranks lenders, and says its AI assistant answers plain English questions about rates, lenders and local costs of ownership. Neither source reports outcome figures.",[28],[271],"en",[],[274,275],{"url":223,"title":224,"publisher":213},{"url":276,"title":277,"publisher":265},"https://www.saferate.com/","Shop for a Mortgage on Your Terms",{"level":226,"checkedAt":259},"safe-rate-ai-mortgage-agent",0,[282],{"kpi":47,"label":283,"unit":247,"aggregate":208,"higherIsBetter":253,"n":76,"nUpTo":280,"median":246,"min":246,"max":246,"byClaimant":284,"vendorOnly":253,"points":285},"Interactions handled",{"organization":280,"vendor":76,"regulator":280,"independent":280},[286],{"evidenceId":260,"organization":234,"value":246,"qualifier":248,"claimant":250,"grade":227,"pooled":253},{"low":288,"high":289},300000,1500000,[291,306,325,348,367],{"slug":192,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":296,"patterns":297,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":299,"publicEvidenceCount":300,"organizations":301,"bestGrade":227,"headline":229,"lastVerified":197,"indexable":253},"Conversational AI for loan application intake","Loan application intake","A conversational assistant on web, app, messaging or voice that explains loan products, captures the application through dialogue in the customer's language, checks documents and basic eligibility rules, and hands a complete, structured application to origination, without making the credit decision.",[17],[20,21,22],[25,298,24,26],"document-processing",6,5,[302,207,303,304,305],"Absa Bank","Lloyds Banking Group","Oper Credits","Rocket Mortgage",{"slug":193,"title":307,"shortTitle":308,"definition":309,"status":9,"industries":310,"functions":314,"patterns":315,"audience":32,"autonomy":317,"adoptionStage":34,"evidenceCount":318,"publicEvidenceCount":318,"organizations":319,"bestGrade":324,"headline":229,"lastVerified":197,"indexable":253},"AI agent for branch finding and appointment booking","Branch and appointment booking","A conversational agent that finds the nearest suitable location, checks opening hours and which services it offers, books an in person or video appointment with the right specialist, and records the reason for the visit so staff are prepared. In banking it answers \"where is my nearest branch\" and books the mortgage or business banker; the same job exists in retail, healthcare and property.",[311,17,312,313,18],"cross-industry","retail-and-ecommerce","healthcare",[22,21],[25,316,24,26],"agentic-workflow","autonomous",4,[320,321,322,323],"Bank of America","Best Buy","Hemominas","MOGUL.sg","B",{"slug":194,"title":326,"shortTitle":327,"definition":328,"status":9,"industries":329,"functions":332,"patterns":334,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":299,"publicEvidenceCount":336,"organizations":337,"bestGrade":227,"headline":341,"lastVerified":197,"indexable":253},"AI assistant for digital account onboarding and KYC","Digital onboarding","A customer facing AI assistant that guides a new applicant, a person or a small merchant, through a digital account, card or relationship application: it collects and checks identity and supporting documents, orchestrates the know your customer and anti money laundering checks, prefills what it can and sends only the unclear cases to a human reviewer with a summary. The ownership research for complex corporate clients is a separate back office job.",[17,330,331],"payments","wealth-and-asset-management",[333,21,22],"onboarding-and-kyc",[25,298,335,316],"computer-vision",3,[338,339,340],"Albo","Deutsche Bank","M-DAQ Global",{"kpi":342,"label":343,"unit":344,"n":76,"nUpTo":280,"kind":345,"value":346,"qualifier":347,"claimant":250,"organization":340,"vendorReported":253},"productivity-gain","Productivity gain","multiplier","reported",30,"exact",{"slug":195,"title":349,"shortTitle":350,"definition":351,"status":9,"industries":352,"functions":355,"patterns":357,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":318,"publicEvidenceCount":318,"organizations":359,"bestGrade":227,"headline":363,"lastVerified":197,"indexable":253},"AI agent for inbound lead qualification and meeting booking","Inbound lead qualification","An AI agent that engages inbound prospects the moment they arrive on the website, chat, messaging or the sales phone line, answers their first questions, qualifies them against the organization's criteria, and books a meeting or hands a ready conversation to the right salesperson, with the context written into the CRM.",[311,353,354,17],"technology","automotive",[21,356],"marketing",[25,26,358,316],"classification-and-routing",[360,361,305,362],"8x8","CarMax","SUSE",{"kpi":45,"label":364,"unit":365,"n":76,"nUpTo":280,"kind":345,"value":366,"qualifier":347,"claimant":250,"organization":360,"vendorReported":253},"Conversion uplift","percent",19,{"slug":196,"title":368,"shortTitle":369,"definition":370,"status":9,"industries":371,"functions":372,"patterns":373,"audience":32,"autonomy":33,"adoptionStage":375,"segment":35,"evidenceCount":336,"publicEvidenceCount":336,"organizations":376,"bestGrade":324,"headline":229,"lastVerified":197,"indexable":253},"AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[17,330],[356,21,22],[25,26,316,374],"recommendation-and-personalization","emerging",[320,377,378],"Capital One","Commonwealth Bank of Australia",{"indexable":253,"reasons":380},[],[382,386,391,399,406,411,418,424,431,438,444,450,457,464,470,475,482,488,494,500,506,512,517,522,527,533,539,544,549,556,562,568,574,579],{"id":148,"label":383,"issuer":158,"region":159,"url":170,"description":384,"useCases":385,"indexable":253},"EU AI Act","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":149,"label":387,"issuer":158,"region":159,"url":388,"description":389,"useCases":390,"indexable":253},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":392,"label":393,"issuer":394,"region":395,"url":396,"description":397,"useCases":398,"indexable":253},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":400,"label":401,"issuer":402,"region":210,"url":403,"description":404,"useCases":405,"indexable":253},"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.",83,{"id":152,"label":407,"issuer":158,"region":159,"url":408,"description":409,"useCases":410,"indexable":253},"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":412,"label":413,"issuer":414,"region":159,"url":415,"description":416,"useCases":417,"indexable":253},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":151,"label":419,"issuer":420,"region":159,"url":421,"description":422,"useCases":423,"indexable":253},"FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":425,"label":426,"issuer":427,"region":165,"url":428,"description":429,"useCases":430,"indexable":253},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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.",36,{"id":432,"label":433,"issuer":434,"region":165,"url":435,"description":436,"useCases":437,"indexable":253},"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":439,"label":440,"issuer":441,"region":395,"url":442,"description":443,"useCases":70,"indexable":253},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":445,"label":446,"issuer":447,"region":210,"url":448,"description":449,"useCases":70,"indexable":253},"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.",{"id":451,"label":452,"issuer":453,"region":159,"url":454,"description":455,"useCases":456,"indexable":253},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":458,"label":459,"issuer":460,"region":395,"url":461,"description":462,"useCases":463,"indexable":253},"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":465,"label":466,"issuer":158,"region":159,"url":467,"description":468,"useCases":469,"indexable":253},"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":471,"label":472,"issuer":158,"region":159,"url":473,"description":474,"useCases":469,"indexable":253},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":476,"label":477,"issuer":478,"region":210,"url":479,"description":480,"useCases":481,"indexable":253},"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":483,"label":484,"issuer":158,"region":159,"url":485,"description":486,"useCases":487,"indexable":253},"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":489,"label":490,"issuer":491,"region":210,"url":492,"description":493,"useCases":487,"indexable":253},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":495,"label":496,"issuer":497,"region":395,"url":498,"description":499,"useCases":487,"indexable":253},"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":501,"label":502,"issuer":158,"region":159,"url":503,"description":504,"useCases":505,"indexable":253},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":507,"label":508,"issuer":509,"region":210,"url":510,"description":511,"useCases":505,"indexable":253},"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":513,"label":514,"issuer":427,"region":165,"url":515,"description":516,"useCases":69,"indexable":253},"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":518,"label":519,"issuer":158,"region":159,"url":520,"description":521,"useCases":69,"indexable":253},"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":523,"label":524,"issuer":158,"region":159,"url":525,"description":526,"useCases":69,"indexable":253},"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":150,"label":528,"issuer":529,"region":159,"url":530,"description":531,"useCases":532,"indexable":253},"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.",9,{"id":154,"label":534,"issuer":535,"region":210,"url":536,"description":537,"useCases":538,"indexable":253},"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.",8,{"id":540,"label":541,"issuer":158,"region":159,"url":542,"description":543,"useCases":538,"indexable":253},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":545,"label":546,"issuer":158,"region":159,"url":547,"description":548,"useCases":299,"indexable":253},"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":550,"label":551,"issuer":552,"region":553,"url":554,"description":555,"useCases":300,"indexable":253},"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":557,"label":558,"issuer":559,"region":159,"url":560,"description":561,"useCases":318,"indexable":253},"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.",{"id":563,"label":564,"issuer":565,"region":159,"url":566,"description":567,"useCases":318,"indexable":253},"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":569,"label":570,"issuer":571,"region":165,"url":572,"description":573,"useCases":336,"indexable":253},"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":575,"label":576,"issuer":158,"region":159,"url":577,"description":578,"useCases":336,"indexable":253},"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":580,"label":581,"issuer":582,"region":210,"url":583,"description":584,"useCases":336,"indexable":253},"us-fcra","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.",1790598302342]