[{"data":1,"prerenderedAt":657},["ShallowReactive",2],{"uc-conversational-loan-application-intake":3,"uc-regulations":452},{"useCase":4,"evidence":218,"blitsAiDeployments":88,"benchmarks":329,"indicative":330,"related":333,"indexability":450,"includeUnpublished":224},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":27,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"problem":36,"problemStats":37,"howItWorks":43,"valueDrivers":44,"kpis":50,"indicativeValue":56,"macroEstimates":96,"feasibility":97,"implementation":111,"risk":157,"blitsAi":194,"faq":196,"related":206,"datePublished":213,"dateModified":213,"lastVerified":213,"changelog":214,"slug":217},"Conversational AI for loan application intake","Loan application intake","AI assistant for loan application intake","Chat and voice assistants can take loan applications and hand origination a complete file, while the credit decision stays with the bank's governed credit process.","published","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.",[12,13,14,15],"loan application chatbot","conversational loan origination","AI loan intake assistant","digital loan application assistant",[17],"banking",[19,20,21],"lending-and-credit","sales","customer-service",[23,24,25,26],"conversational-agent","document-processing","rag-knowledge-assistant","voice-agent",[28,29,30,31],"web-chat","mobile-app","whatsapp","voice","customer-facing","supervised-agent","early-adopters","front-office","Loan application forms can lose customers. They ask for terms the applicant does not understand,\nin a language that may not be their first, and they rarely explain which product fits. Some\napplicants give up, and many completed applications arrive with missing documents or wrong\nfigures. According to Google Cloud, Oper Credits reports that most loan applications in Belgium\nare returned for missing or incorrect information, and each return is another round between the\ncustomer and the credit team. Where branches are few, a phone or messaging channel can be the most\npractical way to apply.\n\nThe bank also has to be careful. Intake collects personal and financial data, touches product\nsuitability and responsible lending duties, and sits right next to the credit decision. An\nassistant that drifts from collecting information into telling people they will or will not be\napproved creates regulatory and fairness risk.",[38],{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":42},"Oper Credits, a mortgage digitisation company serving about 20 banks, says only 30 to 40% of loan applications in Belgium are complete and compliant on first submission, as reported by Google Cloud.","1,302 real-world gen AI use cases from the world's leading organizations","https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders",2025,"1. **Understand the need.** The assistant asks what the customer wants to finance and explains\n   the relevant products from approved, current product content, in the customer's language.\n2. **Check the basics first.** Published eligibility rules (age, residency, minimum income,\n   product limits) are checked by a rules service before the customer invests time, and the\n   assistant explains any rule that is not met.\n3. **Capture the application in conversation.** It asks one question at a time, fills the\n   application fields, and reads uploaded payslips, statements and identity documents with\n   document AI, asking again when something is unclear.\n4. **Validate completeness.** Before submission it checks that every required field and\n   document is present and consistent, so the application arrives complete.\n5. **Hand over a structured file.** The complete application goes into the loan origination\n   system with a summary; the credit decision is made there, by the bank's governed credit\n   process and people.\n6. **Keep the customer informed.** The assistant tells the customer what happens next and when,\n   and hands over to a lending specialist on request or when the case is complex.",[45,46,47,48,49],"revenue-growth","customer-experience","speed","inclusion-and-access","cost-to-serve",[51,52,53,54,55],"conversion-rate-uplift","processing-time-reduction","automation-rate","customer-satisfaction","handling-time-reduction",{"referenceOrg":57,"inputs":58,"formula":91,"currency":92,"period":93,"resultLabel":94,"caveat":95},"A lender receiving 50,000 personal loan applications a year",[59,65,72,78,85],{"key":60,"label":61,"low":62,"high":62,"unit":63,"note":64},"applications","Applications received per year",50000,"applications per year","The reference lender.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"incompleteShare","Share of applications that need follow up for missing or wrong information",0.3,0.5,"fraction of applications","Editorial assumption. According to Google Cloud, Oper Credits, a mortgage digitisation company, says only 30 to 40% of loan applications in Belgium are complete on first submission, which implies 60 to 70% need follow up; this range stays below that to be conservative for other markets and products.",{"key":73,"label":74,"low":68,"high":75,"unit":76,"note":77},"followUpAvoided","Share of that follow up the assistant prevents",0.6,"fraction of follow up","Editorial assumption, replace with your own pilot result.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"minutesPerFollowUp","Staff minutes per follow up",20,40,"minutes per application","Editorial assumption covering calls, emails and re checking documents.",{"key":86,"label":87,"low":75,"high":88,"unit":89,"note":90},"costPerMinute","Fully loaded cost of a lending staff minute",1,"USD per minute","Editorial assumption, replace with your own fully loaded cost.","applications * incompleteShare * followUpAvoided * minutesPerFollowUp * costPerMinute","USD","per year","Application follow up cost avoided","Counts avoided follow up work only. It leaves out additional completed applications (the larger prize, but hard to predict), faster time to decision, the cost of the AI and the integration with origination.",[],{"complexity":98,"complexityNote":99,"dataPrerequisites":100,"integrations":105},"medium","Capturing an application in conversation is well understood. The work is in the eligibility rules service, reliable document reading, writing clean records into loan origination and keeping the assistant on the right side of the line between information and a credit decision.",[101,102,103,104],"Approved, versioned product content and eligibility rules","The application data model of the origination system","Sample documents per type for testing extraction","Past applications with the reasons they were returned or declined",[106,107,108,109,110],"Loan origination system","Document storage and document AI","Identity verification and authentication","CRM for leads and follow up","Contact centre or lending specialists for handover",{"steps":112,"guardrails":131,"humanInTheLoop":137,"kpisToInstrument":138,"failureModes":144},[113,116,119,122,125,128],{"title":114,"detail":115},"Pick one product and one channel","Start with a simple product (a personal loan or a card) on the channel your applicants use most, often the app or WhatsApp, and measure completion and quality against the web form.",{"title":117,"detail":118},"Separate rules from conversation","Put eligibility checks and required fields in a rules service the assistant calls. The model explains; it does not decide whether someone qualifies.",{"title":120,"detail":121},"Map every field to a question and a check","For each application field write the plain language question, the validation and what the assistant says when the answer does not fit.",{"title":123,"detail":124},"Make documents conversational","Let the customer upload a photo, read it, show what was extracted and ask them to confirm, instead of retyping.",{"title":126,"detail":127},"Define the handover and the wording","Agree the phrases the assistant may use about outcomes (\"your application is complete and will be reviewed\") and ban the ones it may not (\"you are approved\").",{"title":129,"detail":130},"Monitor fairness from day one","Track drop off and completion by language, channel and customer segment, so the assistant does not become a filter that disadvantages some groups.",[132,133,134,135,136],"The assistant never states or implies a credit decision","Eligibility checks come only from the versioned rules service","Product explanations come only from approved, current content","Personal and financial data masked in prompts and logs, and kept in region","Handover to a specialist on request, for complex cases and on vulnerability signals","Credit officers or the bank's governed credit models decide every application. Lending specialists take over complex or vulnerable cases, and a sample of conversations is reviewed each week for accuracy, suitability and fair treatment.",[139,140,141,142,143],"Application completion rate versus the web form, by segment and language","Share of applications complete at first submission","Time from first message to complete application","Handover rate and reasons","Complaints mentioning the assistant",[145,148,151,154],{"title":146,"detail":147},"Implied approvals","The assistant says something that sounds like a decision. Restrict outcome wording and test for it.",{"title":149,"detail":150},"Stale rules or rates","The assistant quotes last month's criteria. Version the rules and content with owners and review dates.",{"title":152,"detail":153},"Silent filtering","Customers are discouraged before they apply, unevenly across groups; in the US, Regulation B bars discouraging applicants on a prohibited basis. Measure drop off by segment and review the eligibility wording.",{"title":155,"detail":156},"Bad data, faster","Extraction errors enter origination unnoticed. Show extracted values to the customer for confirmation and sample them.",{"euAiAct":158,"regulations":161,"guidance":168,"controls":187,"incidents":193},{"tier":159,"basis":160},"context-dependent","Explaining products and capturing an application is limited risk with an Article 50 disclosure. If the assistant evaluates creditworthiness or filters applicants on its own assessment, it falls under Annex III point 5(b) and becomes high risk, so keep the decision in the governed credit process.",[162,163,164,165,166,167],"eu-ai-act","gdpr","eba-loan-origination","uk-consumer-duty","dora","us-ecoa-reg-b",[169,175,181],{"title":170,"issuer":171,"region":172,"url":173,"note":174},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(b) lists AI systems used to evaluate the creditworthiness of natural persons or establish their credit score as high risk, except systems used to detect financial fraud; intake must stay outside that scope or meet the high risk duties.",{"title":176,"issuer":177,"region":178,"url":179,"note":180},"Regulation B, § 1002.4 General rules","Consumer Financial Protection Bureau","north-america","https://www.consumerfinance.gov/rules-policy/regulations/1002/4/","Section 1002.4(b) bars statements to applicants or prospective applicants that would discourage them on a prohibited basis, and the official commentary names interview scripts that do so; an intake assistant's eligibility wording falls under the same rule.",{"title":182,"issuer":183,"region":184,"url":185,"note":186},"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 an intake assistant's questions and wording must support.",[188,189,190,191,192],"AI disclosure at the start and a clear route to a person","Versioned eligibility rules and product content with change control","Log of every question, answer and document captured","Fairness monitoring of drop off and completion by segment","Data protection impact assessment for the personal and financial data collected",[],{"howToBuild":195},"On Blits.ai the application runs as a **flow** that holds the regulated structure: **ask\nquestion** blocks with the **sensitive data flag**, **multiple entity check** (slot filling),\n**validation**, **receive attachment** for documents and an **authentication** step. An **AI\nagent** inside the flow explains products from a **knowledge base** of approved content with\nhybrid retrieval, and **custom functions** call the eligibility rules service and write the\ncompleted application into the origination system.\n\nThe same flow serves **web chat**, **WhatsApp**, **voice** and, through the **API channel**, the\nbank's mobile app, in every bot language, with **language detection** and per language content. **PII masking** keeps personal\ndata out of prompts, **guardrails** block outcome wording the bank has banned, and **human\nhandover** passes complex cases to lending specialists. **Test suites** check the flow and the\nagent's wording on every change, and **flow statistics** show where applicants stop. EU and UAE\nregions support data residency.",[197,200,203],{"question":198,"answer":199},"Can a chatbot approve a loan?","It should not. The assistant collects and checks information; the credit decision stays with the bank's governed credit process. An assistant that assessed creditworthiness itself would be a high risk system under the EU AI Act.",{"question":201,"answer":202},"Are banks using AI assistants for loan applications today?","Yes, though outcome data is scarce. Absa's Agentforce based assistant Abby is reported to help customers apply for loans, and Figure uses Gemini powered chatbots in its home equity lending. Rocket Mortgage's assistant, built with Sierra, goes further than intake: it also pulls credit and takes clients to preapproval, and the vendor says it handles more than 400,000 chat conversations a month. Oper Credits, which serves about 20 banks, uses Vertex AI to automate document checks on mortgage applications.",{"question":204,"answer":205},"What should we measure first?","Completion rate against your web form and the share of applications that arrive complete, both split by language and customer segment. They show value and fairness at the same time.",[207,208,209,210,211,212],"home-loan-assistant-and-prequalification","digital-onboarding-assistant","alternative-data-credit-scoring","application-and-identity-fraud-detection","inbound-lead-qualification-agent","adverse-action-explanations","2026-09-27",[215],{"date":213,"note":216},"First published","conversational-loan-application-intake",[219,247,265,283,301],{"title":220,"useCases":221,"organization":222,"vendors":227,"summary":231,"stage":232,"year":42,"channels":233,"languages":234,"metrics":235,"outcomeDisclosed":224,"sources":236,"verification":242,"grade":244,"id":245,"organizationSlug":246},"Absa: Abby, an agentic assistant that guides customers to products and applications",[217],{"name":223,"anonymized":224,"country":225,"region":226,"industry":17},"Absa Bank",false,"ZA","africa",[228],{"name":229,"role":230},"Salesforce","platform","Absa runs Abby, a customer facing assistant built on Salesforce Agentforce, in production. At a Salesforce event in June 2025 Absa's Relationship Banking technology chief described it asking business customers about their needs, such as working capital, and recommending matching products. The report says that, according to Absa's website, the agent can help customers apply for loans, open investment accounts and make international payments. Absa staff set guidelines on what the agent may not do, and Salesforce says it urges its partners to route topics such as pricing to employees. Salesforce could not share the impact on Absa customers, and no outcome figures were disclosed.","production",[],[],[],[237],{"url":238,"title":239,"publisher":240,"date":241},"https://htxt.co.za/2025/06/south-african-banking-giant-reaches-global-first-with-ai-agent/","South African banking giant reaches global first with AI","Hypertext","2025-06-05",{"level":243,"checkedAt":213},"source-verified","C","absa-abby-agentic-assistant",null,{"title":248,"useCases":249,"organization":250,"vendors":253,"summary":257,"stage":232,"year":42,"channels":258,"languages":259,"metrics":260,"outcomeDisclosed":224,"sources":261,"verification":263,"grade":244,"id":264,"organizationSlug":246},"Figure: AI chatbots for home equity lending",[217,207],{"name":251,"anonymized":224,"country":252,"region":178,"industry":17},"Figure","US",[254],{"name":255,"role":256},"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.",[],[],[],[262],{"url":41,"title":40,"publisher":255},{"level":243,"checkedAt":213},"figure-lending-chatbots",{"title":266,"useCases":267,"organization":268,"vendors":271,"summary":273,"stage":232,"year":42,"channels":274,"languages":275,"metrics":277,"outcomeDisclosed":278,"sources":279,"verification":281,"grade":244,"id":282,"organizationSlug":246},"Lloyds Banking Group: faster income verification in mortgage applications",[217],{"name":269,"anonymized":224,"country":270,"region":172,"industry":17},"Lloyds Banking Group","GB",[272],{"name":255,"role":230},"Lloyds Banking Group uses Vertex AI to scale machine learning work across more than 300 data scientists and AI developers. In the same entry Google Cloud reports that the bank cut income verification in mortgage applications from days to seconds and has put 18 generative AI systems into production. This is back office work at the application stage, not a customer facing assistant.",[],[276],"en",[],true,[280],{"url":41,"title":40,"publisher":255},{"level":243,"checkedAt":213},"lloyds-mortgage-income-verification",{"title":284,"useCases":285,"organization":286,"vendors":290,"summary":292,"stage":232,"year":42,"channels":293,"languages":294,"metrics":295,"outcomeDisclosed":224,"sources":296,"verification":298,"grade":244,"id":300,"organizationSlug":246},"Oper Credits: AI document verification for mortgage applications",[217],{"name":287,"anonymized":224,"country":288,"region":172,"industry":289},"Oper Credits","BE","technology",[291],{"name":255,"role":230},"Oper Credits, a Belgian mortgage digitisation company that serves about 20 banks in six countries, uses Vertex AI to automate document verification that used to take several hours of manual work. According to Google Cloud, only 30 to 40% of loan applications in Belgium are complete and compliant on first submission, and most are returned for missing or incorrect information. The company aims to raise that to 90%; the aim is a target, not a reported result.",[],[],[],[297],{"url":41,"title":40,"publisher":255},{"level":243,"checkedAt":299},"2026-09-26","oper-credits-mortgage-document-verification",{"title":302,"useCases":303,"organization":304,"vendors":306,"summary":309,"stage":310,"year":42,"channels":311,"languages":312,"metrics":313,"outcomeDisclosed":278,"sources":323,"verification":327,"grade":244,"id":328,"organizationSlug":246},"Rocket Mortgage: AI Digital Assistant from first question to preapproval",[211,217],{"name":305,"anonymized":224,"country":252,"region":178,"industry":17},"Rocket Mortgage",[307],{"name":308,"role":230},"Sierra","Rocket Mortgage runs an AI Digital Assistant across chat and voice that takes prospective borrowers from first questions to preapproval: it answers questions, collects information, pulls credit, presents personalised rates and loan options and hands the client to a human banker. According to the vendor, the programme started as a proof of concept and has grown to more than 400,000 successful chat conversations and over one million outbound dials a month. Clients who start with the assistant close at three times the rate of those who do not. Sierra also reports that clients who use both the AI chat and a banker convert four times better, without stating the comparison group.","scaled",[28,31],[276],[314],{"kpi":315,"value":316,"unit":317,"qualifier":318,"period":319,"claimant":320,"quote":321,"sourceUrl":322},"interactions-handled",400000,"count","at-least","successful chat conversations per month","vendor","What started as a proof of concept in May has grown to more than 400,000 successful chat conversations and over one million outbound dials each month, and both are rising fast.","https://sierra.ai/customers/rocket-mortgage",[324],{"url":322,"title":325,"publisher":308,"date":326},"How Rocket Mortgage is reimagining the journey home with AI","2025-10-27",{"level":243,"checkedAt":213},"rocket-mortgage-digital-assistant",[],{"low":331,"high":332},54000,600000,[334,346,371,391,415,434],{"slug":207,"title":335,"shortTitle":336,"definition":337,"status":9,"industries":338,"functions":340,"patterns":341,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":342,"publicEvidenceCount":342,"organizations":343,"bestGrade":244,"headline":246,"lastVerified":213,"indexable":278},"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.",[17,339],"real-estate",[19,20,21],[25,23,26],3,[251,344,345],"Loft","Safe Rate",{"slug":208,"title":347,"shortTitle":348,"definition":349,"status":9,"industries":350,"functions":353,"patterns":355,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":358,"publicEvidenceCount":342,"organizations":359,"bestGrade":244,"headline":363,"lastVerified":213,"indexable":278},"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.",[17,351,352],"payments","wealth-and-asset-management",[354,20,21],"onboarding-and-kyc",[23,24,356,357],"computer-vision","agentic-workflow",6,[360,361,362],"Albo","Deutsche Bank","M-DAQ Global",{"kpi":364,"label":365,"unit":366,"n":88,"nUpTo":367,"kind":368,"value":369,"qualifier":370,"claimant":320,"organization":362,"vendorReported":278},"productivity-gain","Productivity gain","multiplier",0,"reported",30,"exact",{"slug":209,"title":372,"shortTitle":373,"definition":374,"status":9,"industries":375,"functions":376,"patterns":379,"audience":381,"autonomy":33,"adoptionStage":34,"segment":382,"evidenceCount":383,"publicEvidenceCount":383,"organizations":384,"bestGrade":390,"headline":246,"lastVerified":299,"indexable":278},"AI credit scoring with alternative data for thin file applicants","Alternative data credit scoring","A machine learning credit model that adds consumer permissioned alternative data, such as bank account cash flow, rent, utility and telco payments or ecosystem data, to credit bureau data, so a lender can assess applicants with thin or no credit files and return a decision with specific reasons.",[17,351],[19,377,378],"underwriting","risk-management",[380,24,23],"prediction-and-scoring","back-office","lending",5,[385,386,387,388,389],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network","B",{"slug":210,"title":392,"shortTitle":393,"definition":394,"status":9,"industries":395,"functions":399,"patterns":401,"audience":381,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":358,"publicEvidenceCount":358,"organizations":403,"bestGrade":390,"headline":410,"lastVerified":299,"indexable":278},"AI for application and identity fraud detection","Application and identity fraud","AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.",[17,351,396,397,398],"cross-industry","government","telecommunications",[400,354,19],"fraud-prevention",[24,402,356,380],"anomaly-detection",[404,405,406,407,408,409],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":411,"label":412,"unit":366,"n":88,"nUpTo":367,"kind":368,"value":413,"qualifier":370,"claimant":414,"organization":407,"vendorReported":224},"detection-rate-improvement","Detection improvement",2.5,"organization",{"slug":211,"title":416,"shortTitle":417,"definition":418,"status":9,"industries":419,"functions":421,"patterns":423,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":425,"publicEvidenceCount":425,"organizations":426,"bestGrade":244,"headline":430,"lastVerified":213,"indexable":278},"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.",[396,289,420,17],"automotive",[20,422],"marketing",[23,26,424,357],"classification-and-routing",4,[427,428,305,429],"8x8","CarMax","SUSE",{"kpi":51,"label":431,"unit":432,"n":88,"nUpTo":367,"kind":368,"value":433,"qualifier":370,"claimant":320,"organization":427,"vendorReported":278},"Conversion uplift","percent",19,{"slug":212,"title":435,"shortTitle":436,"definition":437,"status":9,"industries":438,"functions":439,"patterns":441,"audience":443,"autonomy":444,"adoptionStage":445,"segment":382,"evidenceCount":446,"publicEvidenceCount":446,"organizations":447,"bestGrade":390,"headline":246,"lastVerified":213,"indexable":278},"AI drafted explanations for credit declines and adverse actions","Adverse action explanations","An assistant that turns the reason codes of a credit model into an accurate, specific and readable explanation of a decline, reduced limit or repricing for the customer, and a matching internal rationale for the file, without adding any reason the model did not produce.",[17,351],[19,440,21],"regulatory-compliance",[442,25,23],"content-generation","employee-facing","copilot","emerging",2,[448,449],"Discover Financial Services","Wells Fargo",{"indexable":278,"reasons":451},[],[453,458,463,471,478,483,490,496,503,510,516,522,529,536,542,547,554,560,566,572,578,584,590,595,600,606,611,616,621,628,634,640,646,651],{"id":162,"label":454,"issuer":171,"region":172,"url":455,"description":456,"useCases":457,"indexable":278},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":163,"label":459,"issuer":171,"region":172,"url":460,"description":461,"useCases":462,"indexable":278},"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":464,"label":465,"issuer":466,"region":467,"url":468,"description":469,"useCases":470,"indexable":278},"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":472,"label":473,"issuer":474,"region":178,"url":475,"description":476,"useCases":477,"indexable":278},"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":166,"label":479,"issuer":171,"region":172,"url":480,"description":481,"useCases":482,"indexable":278},"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":484,"label":485,"issuer":486,"region":172,"url":487,"description":488,"useCases":489,"indexable":278},"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":165,"label":491,"issuer":492,"region":172,"url":493,"description":494,"useCases":495,"indexable":278},"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":497,"label":498,"issuer":499,"region":184,"url":500,"description":501,"useCases":502,"indexable":278},"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":504,"label":505,"issuer":506,"region":184,"url":507,"description":508,"useCases":509,"indexable":278},"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":511,"label":512,"issuer":513,"region":467,"url":514,"description":515,"useCases":81,"indexable":278},"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":517,"label":518,"issuer":519,"region":178,"url":520,"description":521,"useCases":81,"indexable":278},"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":523,"label":524,"issuer":525,"region":172,"url":526,"description":527,"useCases":528,"indexable":278},"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":530,"label":531,"issuer":532,"region":467,"url":533,"description":534,"useCases":535,"indexable":278},"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":537,"label":538,"issuer":171,"region":172,"url":539,"description":540,"useCases":541,"indexable":278},"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":543,"label":544,"issuer":171,"region":172,"url":545,"description":546,"useCases":541,"indexable":278},"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":548,"label":549,"issuer":550,"region":178,"url":551,"description":552,"useCases":553,"indexable":278},"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":555,"label":556,"issuer":171,"region":172,"url":557,"description":558,"useCases":559,"indexable":278},"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":561,"label":562,"issuer":563,"region":178,"url":564,"description":565,"useCases":559,"indexable":278},"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":567,"label":568,"issuer":569,"region":467,"url":570,"description":571,"useCases":559,"indexable":278},"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":573,"label":574,"issuer":171,"region":172,"url":575,"description":576,"useCases":577,"indexable":278},"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":579,"label":580,"issuer":581,"region":178,"url":582,"description":583,"useCases":577,"indexable":278},"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":585,"label":586,"issuer":499,"region":184,"url":587,"description":588,"useCases":589,"indexable":278},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":591,"label":592,"issuer":171,"region":172,"url":593,"description":594,"useCases":589,"indexable":278},"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":596,"label":597,"issuer":171,"region":172,"url":598,"description":599,"useCases":589,"indexable":278},"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":164,"label":601,"issuer":602,"region":172,"url":603,"description":604,"useCases":605,"indexable":278},"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":167,"label":607,"issuer":177,"region":178,"url":608,"description":609,"useCases":610,"indexable":278},"ECOA and Regulation B","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":612,"label":613,"issuer":171,"region":172,"url":614,"description":615,"useCases":610,"indexable":278},"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":617,"label":618,"issuer":171,"region":172,"url":619,"description":620,"useCases":358,"indexable":278},"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":622,"label":623,"issuer":624,"region":625,"url":626,"description":627,"useCases":383,"indexable":278},"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":629,"label":630,"issuer":631,"region":172,"url":632,"description":633,"useCases":425,"indexable":278},"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":635,"label":636,"issuer":637,"region":172,"url":638,"description":639,"useCases":425,"indexable":278},"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":641,"label":642,"issuer":643,"region":184,"url":644,"description":645,"useCases":342,"indexable":278},"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":647,"label":648,"issuer":171,"region":172,"url":649,"description":650,"useCases":342,"indexable":278},"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":652,"label":653,"issuer":654,"region":178,"url":655,"description":656,"useCases":342,"indexable":278},"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.",1790598307167]