[{"data":1,"prerenderedAt":631},["ShallowReactive",2],{"uc-digital-onboarding-assistant":3,"uc-regulations":429},{"useCase":4,"evidence":204,"blitsAiDeployments":290,"benchmarks":291,"indicative":299,"related":302,"indexability":427,"includeUnpublished":211},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":24,"channels":29,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"problem":38,"problemStats":39,"howItWorks":40,"valueDrivers":41,"kpis":47,"indicativeValue":53,"macroEstimates":88,"feasibility":89,"implementation":103,"risk":146,"blitsAi":180,"faq":182,"related":192,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI assistant for digital account onboarding and KYC","Digital onboarding","AI assistant for digital onboarding and KYC","An AI assistant guides applicants through identity and KYC checks and sends unclear cases to reviewers. Deutsche Bank and M-DAQ Global use AI for KYC and KYB checks.","published","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.",[12,13,14,15],"digital account opening assistant","KYC onboarding agent","e-KYC assistant","customer onboarding chatbot",[17,18,19],"banking","payments","wealth-and-asset-management",[21,22,23],"onboarding-and-kyc","sales","customer-service",[25,26,27,28],"conversational-agent","document-processing","computer-vision","agentic-workflow",[30,31,32,33],"mobile-app","web-chat","whatsapp","internal-tools","customer-facing","supervised-agent","early-adopters","front-office","Onboarding is where a bank wins or loses a new customer, and it is also one of the most\nheavily regulated steps. The applicant has to choose a product, fill in long forms, photograph an identity\ndocument, pass a liveness check and sometimes explain where their money comes from. Every extra\nstep or unclear question costs completions, and every applicant who stalls either leaves or\ncalls the contact centre.\n\nBehind the form, operations and compliance teams do the slow part by hand: checking documents\nthat are blurred or expired, chasing missing information, screening names and, for wealth and\nbusiness clients, researching source of wealth or the structure of a company. Rules differ by\ncountry, so a bank that operates in several markets maintains several processes. Fully manual\nreview does not scale; fully automated rejection loses good customers and still misses\nsophisticated fraud such as synthetic identities and deepfakes.",[],"1. **Help the applicant choose.** The assistant asks a few needs questions and explains the\n   products the applicant is eligible for, in plain language and the applicant's own language,\n   without giving personal advice.\n2. **Collect data once.** It prefills from what the bank already knows (a lead form, an existing\n   relationship or a government digital identity where one is available) and asks only for what\n   is missing.\n3. **Capture and verify identity.** Document capture, authenticity checks, liveness and face\n   matching run through the bank's identity verification provider; the assistant explains\n   failures (\"the photo is blurred, try again in better light\") instead of ending the journey.\n4. **Run the checks.** An agentic workflow calls sanctions and politically exposed person\n   screening, fraud signals and, where needed, source of wealth or company registry research,\n   and records every result with its evidence.\n5. **Decide the route.** Clean cases continue straight to account opening under the bank's\n   rules; unclear or high risk cases go to a human reviewer with a summary of what is missing\n   or inconsistent. The approval decision on edge cases stays with a person.",[42,43,44,45,46],"revenue-growth","speed","compliance","cost-to-serve","inclusion-and-access",[48,49,50,51,52],"processing-time-reduction","conversion-rate-uplift","automation-rate","productivity-gain","cycle-time-days",{"referenceOrg":54,"inputs":55,"formula":83,"currency":84,"period":85,"resultLabel":86,"caveat":87},"A retail bank that receives 100,000 digital account applications a year",[56,62,69,76],{"key":57,"label":58,"low":59,"high":59,"unit":60,"note":61},"applications","Digital applications started per year",100000,"applications per year","The reference bank.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"abandonRate","Share of started applications that are abandoned today",0.3,0.5,"fraction of applications","Editorial assumption, replace with your own funnel data.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"recoveredShare","Share of abandoned applications the assistant recovers",0.1,0.2,"fraction of abandoned applications","Editorial assumption, deliberately conservative because no deployment on this page discloses a completion uplift.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"valuePerAccount","First year revenue of a new account",50,150,"USD per account","Editorial assumption, replace with your own product economics.","applications * abandonRate * recoveredShare * valuePerAccount","USD","per year","First year revenue from recovered applications","Revenue from recovered applications only. It leaves out the review hours saved in operations, lower fraud losses, the cost of the identity verification provider and the platform, and the lifetime value of an account beyond the first year.",[],{"complexity":90,"complexityNote":91,"dataPrerequisites":92,"integrations":97},"high","The conversation is the easy part. The work is in orchestrating identity verification, screening and core account opening systems, keeping the rules configurable per jurisdiction, and producing an audit trail that satisfies compliance and the regulator.",[93,94,95,96],"Product eligibility rules and approved product descriptions per market","The bank's know your customer and anti money laundering policy, including risk ratings and document lists per jurisdiction","Funnel data showing where applicants abandon today","Labelled historical cases of manual review outcomes, to test routing",[98,99,100,101,102],"Identity verification provider (document authenticity, liveness, face match)","Sanctions, politically exposed person and adverse media screening","Government digital identity schemes where they exist","Customer onboarding or account origination platform and core banking","Case management for manual review, with the assistant's summary attached",{"steps":104,"guardrails":120,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":133},[105,108,111,114,117],{"title":106,"detail":107},"Map the funnel and the rules","Take the current application funnel and mark where applicants drop out and why. Write the verification rules per product and jurisdiction as configuration, not as prompts, so compliance can own and change them.",{"title":109,"detail":110},"Start with guidance and document help","The first release explains products, answers questions and helps applicants get their documents right. It changes no decisions, so it can ship early and still move completion.",{"title":112,"detail":113},"Automate the checks behind the form","Add the agentic workflow that runs screening and collects evidence, with every tool call logged. Measure how often the reviewer agrees with the prepared summary before you let any case skip review.",{"title":115,"detail":116},"Set the routing thresholds with compliance","Agree which cases may proceed without a human and which must be reviewed (high risk countries, politically exposed persons, mismatched data, low verification confidence). Keep human approval for every edge case.",{"title":118,"detail":119},"Test against fraud as well as friction","Build a test set with genuine applicants who struggle (poor lighting, unusual names, foreign documents) and with attack patterns (edited documents, replayed selfies, synthetic identities), and run it on every change.",[121,122,123,124,125],"Verification rules are configuration owned by compliance, never free text instructions to a model","The assistant cannot approve an edge case; ambiguous or high risk cases always go to a person","Personal data and identity images are masked in logs and never used for model training without consent","Clear disclosure of what data is collected, why, and that the applicant is talking to AI","No personal financial advice during product selection, only eligibility and product facts","Onboarding analysts review every case the rules mark as unclear or high risk, with the assistant's summary and evidence in front of them, and they own the approval. Compliance samples straight through cases every week and signs off every change to thresholds.",[128,129,130,131,132],"Application completion rate per step and per channel, before and after","Median time from start to account open","Share of cases sent to manual review, and reviewer agreement with the prepared summary","Fraud found after onboarding in accounts that went straight through","Applicant satisfaction and contact centre calls about applications",[134,137,140,143],{"title":135,"detail":136},"Fast onboarding for fraudsters","Straight through rules tuned for conversion let synthetic identities in. Track fraud on new accounts by route and tighten thresholds when it rises.",{"title":138,"detail":139},"Silent unfair rejection","Document or liveness checks fail more often for some groups of applicants, who then give up. Monitor failure rates by document type, age band and language and offer an assisted route.",{"title":141,"detail":142},"One rulebook for many countries","A single process copied across markets breaks local rules. Keep verification logic configurable per jurisdiction with a named owner.",{"title":144,"detail":145},"Unexplainable decisions","A reviewer or regulator cannot see why a case was routed. Log every check, its result and the rule that applied.",{"euAiAct":147,"regulations":150,"guidance":160,"controls":173,"incidents":179},{"tier":148,"basis":149},"context-dependent","The conversational assistant falls under the Article 50 transparency duty. Biometric verification whose sole purpose is to confirm that a person is who they claim to be is excluded from the Annex III biometric category. The system becomes high risk when the same journey assesses creditworthiness or a credit score of a natural person, for example for a credit card or overdraft (Annex III point 5(b)).",[151,152,153,154,155,156,157,158,159],"eu-ai-act","gdpr","fatf-recommendations","dora","iso-42001","eu-amlr","us-bsa","mas-notice-626","eu-accessibility-act",[161,167],{"title":162,"issuer":163,"region":164,"url":165,"note":166},"Annex III: High-risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 1(a) excludes biometric verification that only confirms identity; point 5(b) makes creditworthiness assessment high risk.",{"title":168,"issuer":169,"region":170,"url":171,"note":172},"NIST SP 800-63 Digital Identity Guidelines","NIST","north-america","https://pages.nist.gov/800-63-4/","Reference for identity proofing and assurance levels, useful for setting verification strength per product.",[174,175,176,177,178],"Documented verification rules per jurisdiction with a named compliance owner","Audit trail of every check, tool call and routing decision per application","Bias monitoring of verification failure rates across applicant groups","Human approval for every case outside the straight through rules","AI disclosure and a data collection notice at the start of the journey",[],{"howToBuild":181},"On Blits.ai the applicant facing side is an **AI agent** combined with a **flow** for the\nregulated steps: the flow collects fields with slot filling, receives document uploads with\nallowed file types and calls the bank's identity verification, screening and account opening\nservices through **custom functions** (REST calls). Product and\npolicy questions are answered from a **knowledge base** with hybrid retrieval over approved\ncontent only.\n\nThe checks behind the form run as an **agentic workflow** with a tool execution policy,\n**human in the loop** confirmation (approve or reject) before the workflow takes the actions you\nmark as sensitive, and a full audit trail per run. Which cases count as high risk and go to a\nreviewer is decided by rules in the flow or in custom functions that the bank's compliance\nteam owns. The same journey serves **web chat and WhatsApp**, and the bank's mobile\napp through the **REST or WebSocket API channel**, in several languages. **PII masking** at the gateway keeps personal data such as ID numbers out\nof model prompts and logs, **test suites** replay difficult applications on every change, and **EU or UAE data\nresidency** keeps applicant data in region. The platform is model agnostic.",[183,186,189],{"question":184,"answer":185},"Can AI approve new customers without a human?","A bank can let its rules approve clean, low risk cases straight through, with the AI preparing the evidence. Unclear or high risk cases should always reach a person. Deutsche Bank describes its Source of Wealth agent this way: it prepares assessments for staff to review and accountability stays with people.",{"question":187,"answer":188},"Is identity verification with face matching high risk under the EU AI Act?","Not by itself. Annex III excludes biometric verification whose only purpose is to confirm that someone is who they claim to be. The journey becomes high risk if it also scores the applicant's creditworthiness.",{"question":190,"answer":191},"Where does AI help most in onboarding?","In two places: helping applicants finish (explaining products and fixing document problems in the moment) and preparing the checks for reviewers. The deployments on this page show both, from a neobank chatbot for first time bank users to automated Know Your Business and Source of Wealth research.",[193,194,195,196,197,198],"business-onboarding-and-ubo-discovery","application-and-identity-fraud-detection","perpetual-kyc","pep-and-adverse-media-screening","account-and-card-servicing-agent","conversational-loan-application-intake","2026-09-27",[201],{"date":199,"note":202},"First published","digital-onboarding-assistant",[205,233,258],{"title":206,"useCases":207,"organization":209,"vendors":214,"summary":215,"stage":216,"year":217,"channels":218,"languages":219,"metrics":220,"outcomeDisclosed":211,"sources":221,"verification":227,"grade":230,"id":231,"organizationSlug":232},"Deutsche Bank: agentic AI for Source of Wealth checks in private bank onboarding",[203,208],"source-of-wealth-diligence",{"name":210,"anonymized":211,"country":212,"region":213,"industry":17},"Deutsche Bank",false,"DE","asia-pacific",[],"Deutsche Bank Private Bank put an agentic AI solution live in its Singapore and Hong Kong booking centres that researches, documents and prepares Source of Wealth assessments, which the bank calls one of the most resource intensive parts of know your customer checks. It reads client documents alongside approved external data, flags gaps and inconsistencies, and hands the assessment to bank staff for review; relationship managers in Dubai use it for accounts booked in Singapore. The bank stresses that accountability stays with its people. No outcome figures are disclosed; the growth figures in the coverage are forecasts.","production",2026,[33],[],[],[222],{"url":223,"title":224,"publisher":225,"date":226},"https://www.fstech.co.uk/fst/Deutsche_Bank_Rolls_Out_Agentic_AI_To_Streamline_KYC_Onboarding_In_Private_Banking.php","Deutsche Bank rolls out agentic AI to streamline KYC onboarding in private banking","FStech","2026-09-23",{"level":228,"checkedAt":229},"source-verified","2026-09-26","C","deutsche-bank-source-of-wealth-kyc-agent","deutsche-bank",{"title":234,"useCases":235,"organization":236,"vendors":240,"summary":244,"stage":216,"year":245,"channels":246,"languages":247,"metrics":248,"outcomeDisclosed":211,"sources":249,"verification":255,"grade":230,"id":256,"organizationSlug":257},"Albo: Albot AI chatbot for onboarding and support of first time bank users",[203],{"name":237,"anonymized":211,"country":238,"region":239,"industry":17},"Albo","MX","latin-america",[241],{"name":242,"role":243},"Google","model-provider","Albo, a Mexican neobank, uses Gemini models in Albot, a chatbot that handles customer onboarding and offers support and financial guidance around the clock to millions of first time banking users. Google Cloud lists the deployment and says it supports financial inclusion and regulatory compliance; no outcome figures are disclosed.",2025,[],[],[],[250],{"url":251,"title":252,"publisher":253,"date":254},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","Real-world gen AI use cases from the world's leading organizations","Google Cloud","2026-04-22",{"level":228,"checkedAt":229},"albo-albot-ai-chatbot",null,{"title":259,"useCases":260,"organization":261,"vendors":264,"summary":267,"stage":216,"year":268,"channels":269,"languages":270,"metrics":271,"outcomeDisclosed":279,"sources":280,"verification":288,"grade":230,"id":289,"organizationSlug":257},"M-DAQ Global: AI driven Know Your Business checks for business customer onboarding",[203,193],{"name":262,"anonymized":211,"country":263,"region":213,"industry":18},"M-DAQ Global","SG",[265],{"name":253,"role":266},"platform","M-DAQ Global, a fintech group headquartered in Singapore that specialises in foreign exchange and cross border payments, runs a Know Your Business compliance solution on Vertex AI and Google Kubernetes Engine that uses natural language processing to automate the verification work behind onboarding business customers. The vendor reports a productivity gain of 30 times and shorter onboarding times.",2024,[33],[],[272],{"kpi":51,"value":273,"unit":274,"qualifier":275,"period":276,"claimant":277,"quote":278,"sourceUrl":251},30,"multiplier","exact","compliance tasks, as reported by the vendor","vendor","The natural language processing-based system automates compliance tasks and improves productivity by 30 times, reducing onboarding times and eliminating manual bottlenecks in customer verification.",true,[281,282,285],{"url":251,"title":252,"publisher":253,"date":254},{"url":283,"title":284,"publisher":262},"https://www.m-daq.com/about-us","About Us",{"url":286,"title":287,"publisher":253},"https://cloud.google.com/customers/mdaq","Improve productivity by 30x with Vertex AI automation and data analysis",{"level":228,"checkedAt":199},"m-daq-global-kyb-onboarding",3,[292],{"kpi":51,"label":293,"unit":274,"aggregate":279,"higherIsBetter":279,"n":294,"nUpTo":295,"median":273,"min":273,"max":273,"byClaimant":296,"vendorOnly":279,"points":297},"Productivity gain",1,0,{"organization":295,"vendor":294,"regulator":295,"independent":295},[298],{"evidenceId":289,"organization":262,"value":273,"qualifier":275,"claimant":277,"grade":230,"pooled":279},{"low":300,"high":301},150000,1500000,[303,326,353,372,394,414],{"slug":193,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":309,"patterns":311,"audience":314,"autonomy":35,"adoptionStage":315,"segment":316,"evidenceCount":290,"publicEvidenceCount":290,"organizations":317,"bestGrade":230,"headline":320,"lastVerified":199,"indexable":279},"AI for business onboarding (KYB) and beneficial ownership discovery","Business onboarding and UBO","An AI agent that builds the know your business (KYB) due diligence file for a new or reviewed corporate client, before any account is opened: it collects registry, incorporation and ownership documents, resolves the entity across sources, maps the ownership chain through holding companies, nominees and trusts to the ultimate beneficial owners, screens the entity and its owners, and presents a risk scored case for a compliance analyst to decide.",[17,18,308],"capital-markets",[21,310],"financial-crime-compliance",[26,28,312,313],"classification-and-routing","summarization","back-office","emerging","specialized-businesses",[318,319,262],"BNY","Incore Bank",{"kpi":50,"label":321,"unit":322,"n":294,"nUpTo":295,"kind":323,"value":324,"qualifier":275,"claimant":325,"organization":318,"vendorReported":211},"Automation rate","percent","reported",25,"organization",{"slug":194,"title":327,"shortTitle":328,"definition":329,"status":9,"industries":330,"functions":334,"patterns":337,"audience":314,"autonomy":35,"adoptionStage":36,"segment":37,"evidenceCount":340,"publicEvidenceCount":340,"organizations":341,"bestGrade":348,"headline":349,"lastVerified":229,"indexable":279},"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.",[17,18,331,332,333],"cross-industry","government","telecommunications",[335,21,336],"fraud-prevention","lending-and-credit",[26,338,27,339],"anomaly-detection","prediction-and-scoring",6,[342,343,344,345,346,347],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra","B",{"kpi":350,"label":351,"unit":274,"n":294,"nUpTo":295,"kind":323,"value":352,"qualifier":275,"claimant":325,"organization":345,"vendorReported":211},"detection-rate-improvement","Detection improvement",2.5,{"slug":195,"title":354,"shortTitle":355,"definition":356,"status":9,"industries":357,"functions":358,"patterns":359,"audience":314,"autonomy":35,"adoptionStage":315,"segment":361,"evidenceCount":362,"publicEvidenceCount":362,"organizations":363,"bestGrade":348,"headline":368,"lastVerified":229,"indexable":279},"AI for perpetual KYC and event driven customer due diligence","Perpetual KYC","AI that keeps each customer's due diligence file current by replacing calendar driven KYC reviews with continuous, event driven refreshes: it watches for trigger events such as a change of ownership, address, behaviour or a new adverse finding, refreshes the file automatically where it can, and involves an analyst only when something material has changed. The risk rating itself and the first file for a new business client are separate use cases.",[17,18,19],[21,310],[28,26,360,313],"rag-knowledge-assistant","middle-office",5,[210,364,365,366,367],"First National Bank of Omaha (FNBO)","JPMorgan Chase","OCBC","Origin Bank",{"kpi":369,"label":370,"unit":322,"n":294,"nUpTo":295,"kind":323,"value":371,"qualifier":275,"claimant":325,"organization":365,"vendorReported":211},"cost-reduction","Cost reduction",40,{"slug":196,"title":373,"shortTitle":374,"definition":375,"status":9,"industries":376,"functions":377,"patterns":378,"audience":380,"autonomy":381,"adoptionStage":36,"segment":361,"evidenceCount":382,"publicEvidenceCount":382,"organizations":383,"bestGrade":348,"headline":389,"lastVerified":199,"indexable":279},"AI for PEP and adverse media screening","PEP and adverse media screening","AI that continuously scans news, court records, registries and other open sources in many languages for negative information and political exposure linked to customers, counterparties and beneficial owners, discards look alikes, and summarises credible risk for the analyst with the sources attached.",[17,18,19],[310,21],[360,313,312,379],"translation","employee-facing","copilot",7,[210,384,385,366,386,387,388],"HSBC","Mashreq","Santander UK","Save the Children","Scotiabank",{"kpi":390,"label":391,"unit":322,"n":294,"nUpTo":294,"kind":323,"value":392,"qualifier":393,"claimant":277,"organization":387,"vendorReported":279},"handling-time-reduction","Handling time reduction",60,"at-least",{"slug":197,"title":395,"shortTitle":396,"definition":397,"status":9,"industries":398,"functions":399,"patterns":401,"audience":34,"autonomy":35,"adoptionStage":403,"segment":37,"evidenceCount":404,"publicEvidenceCount":405,"organizations":406,"bestGrade":348,"headline":409,"lastVerified":199,"indexable":279},"AI agent for account and card servicing","Account and card servicing","An AI agent that resolves routine account and card requests end to end, such as balances, statements, card blocks and replacements, PIN resets and limit changes, across app, web, messaging and phone, and hands anything sensitive or unusual to a human with the full context.",[17,18],[23,400],"operations",[25,402,28,360],"voice-agent","mainstream",4,2,[407,408],"Commonwealth Bank of Australia","DBS Bank",{"kpi":410,"label":411,"unit":322,"n":405,"nUpTo":295,"kind":323,"value":412,"qualifier":413,"claimant":325,"organization":408,"vendorReported":211},"containment-rate","Containment rate",90,"approximately",{"slug":198,"title":415,"shortTitle":416,"definition":417,"status":9,"industries":418,"functions":419,"patterns":420,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"evidenceCount":340,"publicEvidenceCount":362,"organizations":421,"bestGrade":230,"headline":257,"lastVerified":199,"indexable":279},"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],[336,22,23],[25,26,360,402],[422,423,424,425,426],"Absa Bank","Figure","Lloyds Banking Group","Oper Credits","Rocket Mortgage",{"indexable":279,"reasons":428},[],[430,435,440,447,453,458,465,472,479,485,492,498,505,511,516,521,527,532,538,544,550,556,561,566,571,578,585,590,595,602,608,614,620,625],{"id":151,"label":431,"issuer":163,"region":164,"url":432,"description":433,"useCases":434,"indexable":279},"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":152,"label":436,"issuer":163,"region":164,"url":437,"description":438,"useCases":439,"indexable":279},"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":155,"label":441,"issuer":442,"region":443,"url":444,"description":445,"useCases":446,"indexable":279},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":448,"label":449,"issuer":169,"region":170,"url":450,"description":451,"useCases":452,"indexable":279},"nist-ai-rmf","NIST AI Risk Management Framework","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":154,"label":454,"issuer":163,"region":164,"url":455,"description":456,"useCases":457,"indexable":279},"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":459,"label":460,"issuer":461,"region":164,"url":462,"description":463,"useCases":464,"indexable":279},"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":466,"label":467,"issuer":468,"region":164,"url":469,"description":470,"useCases":471,"indexable":279},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":473,"label":474,"issuer":475,"region":213,"url":476,"description":477,"useCases":478,"indexable":279},"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":480,"label":481,"issuer":482,"region":213,"url":483,"description":484,"useCases":324,"indexable":279},"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.",{"id":486,"label":487,"issuer":488,"region":443,"url":489,"description":490,"useCases":491,"indexable":279},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":493,"label":494,"issuer":495,"region":170,"url":496,"description":497,"useCases":491,"indexable":279},"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":499,"label":500,"issuer":501,"region":164,"url":502,"description":503,"useCases":504,"indexable":279},"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":153,"label":506,"issuer":507,"region":443,"url":508,"description":509,"useCases":510,"indexable":279},"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":156,"label":512,"issuer":163,"region":164,"url":513,"description":514,"useCases":515,"indexable":279},"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":517,"label":518,"issuer":163,"region":164,"url":519,"description":520,"useCases":515,"indexable":279},"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":157,"label":522,"issuer":523,"region":170,"url":524,"description":525,"useCases":526,"indexable":279},"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":159,"label":528,"issuer":163,"region":164,"url":529,"description":530,"useCases":531,"indexable":279},"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":533,"label":534,"issuer":535,"region":170,"url":536,"description":537,"useCases":531,"indexable":279},"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":539,"label":540,"issuer":541,"region":443,"url":542,"description":543,"useCases":531,"indexable":279},"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":545,"label":546,"issuer":163,"region":164,"url":547,"description":548,"useCases":549,"indexable":279},"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":551,"label":552,"issuer":553,"region":170,"url":554,"description":555,"useCases":549,"indexable":279},"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":158,"label":557,"issuer":475,"region":213,"url":558,"description":559,"useCases":560,"indexable":279},"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":562,"label":563,"issuer":163,"region":164,"url":564,"description":565,"useCases":560,"indexable":279},"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":567,"label":568,"issuer":163,"region":164,"url":569,"description":570,"useCases":560,"indexable":279},"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":572,"label":573,"issuer":574,"region":164,"url":575,"description":576,"useCases":577,"indexable":279},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":579,"label":580,"issuer":581,"region":170,"url":582,"description":583,"useCases":584,"indexable":279},"us-ecoa-reg-b","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":586,"label":587,"issuer":163,"region":164,"url":588,"description":589,"useCases":584,"indexable":279},"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":591,"label":592,"issuer":163,"region":164,"url":593,"description":594,"useCases":340,"indexable":279},"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":596,"label":597,"issuer":598,"region":599,"url":600,"description":601,"useCases":362,"indexable":279},"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":603,"label":604,"issuer":605,"region":164,"url":606,"description":607,"useCases":404,"indexable":279},"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":609,"label":610,"issuer":611,"region":164,"url":612,"description":613,"useCases":404,"indexable":279},"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":615,"label":616,"issuer":617,"region":213,"url":618,"description":619,"useCases":290,"indexable":279},"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":621,"label":622,"issuer":163,"region":164,"url":623,"description":624,"useCases":290,"indexable":279},"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":626,"label":627,"issuer":628,"region":170,"url":629,"description":630,"useCases":290,"indexable":279},"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.",1790598296776]