[{"data":1,"prerenderedAt":632},["ShallowReactive",2],{"uc-account-and-card-servicing-agent":3,"uc-regulations":431},{"useCase":4,"evidence":215,"blitsAiDeployments":292,"benchmarks":293,"indicative":312,"related":315,"indexability":429,"includeUnpublished":222},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":18,"patterns":21,"channels":26,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":42,"valueDrivers":43,"kpis":48,"indicativeValue":55,"macroEstimates":95,"feasibility":100,"implementation":113,"risk":159,"blitsAi":191,"faq":193,"related":203,"datePublished":210,"dateModified":210,"lastVerified":210,"changelog":211,"slug":214},"AI agent for account and card servicing","Account and card servicing","AI agents for bank account and card servicing","AI agents that block cards, send statements and answer account questions. Microsoft reports Commonwealth Bank resolved about 84.6% of self service chats end to end.","published","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.",[12,13,14],"banking servicing chatbot","card servicing assistant","virtual banking assistant",[16,17],"banking","payments",[19,20],"customer-service","operations",[22,23,24,25],"conversational-agent","voice-agent","agentic-workflow","rag-knowledge-assistant",[27,28,29,30],"mobile-app","web-chat","whatsapp","voice","customer-facing","supervised-agent","mainstream","front-office","Routine servicing requests arrive in a constant stream in retail banking: where is my\ntransaction, send me a statement, my card is lost, raise my limit. Each request is simple, but\ntogether they fill queues, push up waiting times at the moments customers are most anxious (a\nlost card, a payment that did not arrive) and take human agents away from the conversations that\nneed judgment, such as hardship, fraud victims and complaints.\n\nMany first generation banking chatbots were rule based: the CFPB describes them as using decision\ntree logic or a database of keywords to trigger preset, limited responses. A preset answer can\nexplain a procedure, but the customer still has to finish the task somewhere else. The step change\nis an agent that is authenticated, can act in the core banking and card systems within strict\nlimits, and knows when to stop and hand over. DBS describes a similar shift: its DBS Joy assistant\nused to give customers instructions on how to find the information they needed, and now answers\nfrom their own transaction and account data.",[37],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"The US Consumer Financial Protection Bureau cites an estimate that in 2022 over 98 million users, about 37% of the US population, engaged with a bank's chatbot.","Chatbots in consumer finance","https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/",2023,"1. **Understand the request.** The agent detects the intent (\"freeze my card\", \"why was I\n   charged twice\") in the customer's own words and language, on any channel.\n2. **Authenticate proportionally.** Information requests need a logged in session; actions that\n   move money or change a card need step up authentication, such as an app confirmation or voice\n   biometrics on the phone.\n3. **Act through approved tools.** The agent calls a small allow list of banking APIs (card\n   block, replacement order, statement request, limit change within set bounds) and confirms the\n   result back to the customer.\n4. **Answer policy questions from approved content.** Fees, product terms and procedures come\n   from retrieval over the bank's own documents, so answers are grounded and current.\n5. **Hand over well.** Vulnerability signals, complaints, disputes and anything outside the\n   allow list go to a human specialist with a summary of the conversation, so the customer never\n   repeats themselves.",[44,45,46,47],"cost-to-serve","customer-experience","inclusion-and-access","employee-productivity",[49,50,51,52,53,54],"containment-rate","interactions-handled","contact-deflection","handling-time-reduction","customer-satisfaction","customer-satisfaction-uplift",{"referenceOrg":56,"inputs":57,"formula":90,"currency":91,"period":92,"resultLabel":93,"caveat":94},"A retail bank with 1 million digitally active customers",[58,63,70,77,83],{"key":59,"label":60,"low":61,"high":61,"unit":59,"note":62},"customers","Digitally active customers",1000000,"The reference bank.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"contactsPerCustomer","Assisted contacts per customer per year",2,4,"contacts per customer per year","Editorial assumption for a digitally active retail bank. Replace with your own contact volume.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"servicingShare","Share of contacts that are routine servicing",0.4,0.6,"fraction of contacts","Editorial assumption, replace with the share from your own contact reason report.",{"key":78,"label":79,"low":80,"high":74,"unit":81,"note":82},"containment","Share of servicing contacts the agent resolves",0.3,"fraction of servicing contacts","Conservative against the benchmarks on this page (Microsoft reports about 84.6% of self service messaging interactions resolved end to end at Commonwealth Bank; DBS reports nine in ten digibot queries resolved digitally), because both figures are for messaging and this range also covers the phone channel.",{"key":84,"label":85,"low":86,"high":87,"unit":88,"note":89},"costPerContact","Cost of a human handled contact",3,6,"USD per contact","Editorial assumption for a blended chat and phone contact. Replace with your own fully loaded cost.","customers * contactsPerCustomer * servicingShare * containment * costPerContact","USD","per year","Human handled contact cost avoided","Gross avoided contact cost only. It leaves out the cost of running the AI, the integration work, the revenue effect of faster service and any reduction in complaint handling.",[96],{"statement":97,"sourceTitle":98,"sourceUrl":99,"year":41},"McKinsey estimates that generative AI could add between USD 200 billion and USD 340 billion in value to global banking each year.","Capturing the full value of generative AI in banking","https://www.mckinsey.com/industries/financial-services/our-insights/capturing-the-full-value-of-generative-ai-in-banking",{"complexity":101,"complexityNote":102,"dataPrerequisites":103,"integrations":107},"medium","Answering questions is easy; acting is the hard part. The work is in the integrations with core banking and card platforms, step up authentication and a clean handover into the contact centre.",[104,105,106],"Approved, current product terms, fee tables and servicing procedures","A catalog of servicing intents with volumes from the contact centre","Customer and card data reachable through APIs, not screens",[108,109,110,111,112],"Core banking system (balances, transactions, statements)","Card management platform (block, replace, limits, PIN)","Identity and step up authentication (app push, one time passcode, voice biometrics)","Contact centre platform for handover with conversation context","CRM or case management for follow up tasks",{"steps":114,"guardrails":133,"humanInTheLoop":139,"kpisToInstrument":140,"failureModes":146},[115,118,121,124,127,130],{"title":116,"detail":117},"Pick the first intents by volume and risk","Take the contact reason report and choose five to ten high volume, low risk intents (statement request, card freeze, transaction lookup). Leave money movement for a later wave.",{"title":119,"detail":120},"Define the action allow list","For every action write down the API, the authentication level it needs, the limits (for example a maximum limit increase) and what the agent says when a limit is reached.",{"title":122,"detail":123},"Ground the answers","Load only approved product and fee content into the knowledge base, with an owner and a review date per document, and make the agent refuse when the answer is not in it.",{"title":125,"detail":126},"Design the handover","Decide which signals trigger a human (vulnerability, complaint, dispute, repeated failure) and pass a summary and the authenticated identity so the customer does not repeat anything.",{"title":128,"detail":129},"Test before customers do","Build a test set of real conversations per intent, including edge cases and attempts to make the agent act outside its limits, and run it on every change.",{"title":131,"detail":132},"Launch in one channel, then widen","Start in the logged in app, where authentication is strongest, measure containment and satisfaction per intent, then add web, messaging and voice.",[134,135,136,137,138],"Actions only through an allow list of APIs, each with its own authentication level and limits","Step up authentication before any card action or money movement","Answers only from approved content, with a refusal when the content does not cover the question","Automatic handover on vulnerability signals, complaints and disputes","Masking of card numbers and personal data in logs and model prompts","Humans own the exceptions: disputes, hardship, suspected fraud victims and complaints. They also review a sample of contained conversations every week to catch answers that were fluent but wrong, and approve every new intent and action before it goes live.",[141,142,143,144,145],"Containment rate per intent, counting repeat contacts within seven days as not contained","Handover rate and handover reasons","Customer satisfaction on contained conversations versus human handled ones","Share of actions completed without error, from the core system logs","Complaints that mention the assistant",[147,150,153,156],{"title":148,"detail":149},"Fluent but wrong policy answers","Answers drawn from outdated or unapproved content. Prevent with document ownership, review dates and refusal when retrieval finds nothing.",{"title":151,"detail":152},"Containment that is really abandonment","Customers give up rather than get helped, which looks like containment in the dashboard. Count repeat contacts and measure satisfaction per intent.",{"title":154,"detail":155},"Handover without context","The customer has to start again with a human, which is worse than no assistant. Pass the summary and the authentication state.",{"title":157,"detail":158},"Scope creep into risky actions","New actions are added without their own risk review. Treat every new action as a change with sign off.",{"euAiAct":160,"regulations":163,"guidance":172,"controls":184,"incidents":190},{"tier":161,"basis":162},"limited","Article 50(1): people must be informed that they are interacting with an AI system, unless that is obvious from the context. Servicing existing accounts and cards is not an Annex III use. It would become high risk under Annex III point 5(b) if the agent itself evaluated the creditworthiness of a natural person, for example to decide a credit limit increase.",[164,165,166,167,168,169,170,171],"eu-ai-act","gdpr","dora","pci-dss","uk-consumer-duty","apra-cps-230","eu-psd2","eu-accessibility-act",[173,179],{"title":174,"issuer":175,"region":176,"url":177,"note":178},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":39,"issuer":180,"region":181,"url":182,"note":183},"Consumer Financial Protection Bureau","north-america","https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/","Warns that chatbots which cannot resolve a problem and block access to a human (\"doom loops\") can harm customers and lead to violations of consumer financial law.",[185,186,187,188,189],"AI disclosure at the start of every conversation","Inventory entry for the assistant with an accountable owner and a documented action allow list","Immutable audit trail of every action the agent took, with the authentication level used","Change control and regression tests for every new intent or action","Outcome monitoring for vulnerable customers and complaint trends",[],{"howToBuild":192},"On Blits.ai this is an **AI agent** with a small set of **custom functions** that call the\nbank's servicing APIs as REST calls, each scoped to one action, combined with a **knowledge\nbase** that holds only approved product and fee content, retrieved with hybrid search. Regulated\njourneys such as a card block follow a **flow** with deterministic steps that calls the bank's\nown step up authentication through a custom function; open questions go to the agent.\n\nThe same agent serves **web chat, WhatsApp, SMS and voice**, and the bank's mobile app through\nthe REST or WebSocket API channel, with streaming speech recognition and synthesis on the phone.\n**Guardrails** check input and output, **PII masking** and card number tokenization happen at\nthe gateway before text reaches a model, and **human handover** passes the conversation to a\nlive agent platform such as Salesforce, Freshdesk or Zoho SalesIQ, with the history (optionally\nsummarized by AI) retrieved in the flow. **Test suites** run multi turn conversations per intent on\nevery change, and analytics show interactions, top intents and satisfaction. The platform is\nmodel agnostic, so the bank can choose or switch the underlying model per agent.",[194,197,200],{"question":195,"answer":196},"What share of servicing requests can an AI agent resolve?","It depends on the intent mix, the channel and whether the agent can act as well as answer. Microsoft reports that at Commonwealth Bank about 84.6% of self service messaging interactions were resolved end to end in May 2026, and DBS reports that DBS digibot resolved nine in every ten queries digitally in the first half of 2026. Both figures are for messaging at large banks, so plan more conservatively for voice and for a first launch.",{"question":198,"answer":199},"Is a banking servicing chatbot high risk under the EU AI Act?","Usually not. It falls under the transparency duty of Article 50: customers must know they are talking to AI. It would become high risk under Annex III point 5(b) if it evaluated the creditworthiness of a natural person, for example by deciding a credit limit increase itself, so keep credit decisions in the bank's existing credit process.",{"question":201,"answer":202},"Which requests should stay with humans?","Disputes, suspected fraud or scams, hardship and financial difficulty, complaints and any conversation where the customer shows signs of vulnerability.",[204,205,206,207,208,209],"first-line-contact-centre-agent","account-servicing-execution","card-dispute-and-chargeback-intake","fraud-alert-confirmation","atm-and-self-service-device-assistance","digital-onboarding-assistant","2026-09-27",[212],{"date":210,"note":213},"First published","account-and-card-servicing-agent",[216,261],{"title":217,"useCases":218,"organization":220,"vendors":225,"summary":228,"stage":229,"year":230,"channels":231,"languages":232,"metrics":233,"outcomeDisclosed":251,"sources":252,"verification":256,"grade":258,"id":259,"organizationSlug":260},"DBS: generative and agentic AI in the DBS Joy and DBS digibot virtual assistants",[214,219],"offers-and-rewards-agent",{"name":221,"anonymized":222,"country":223,"region":224,"industry":16},"DBS Bank",false,"SG","asia-pacific",[226],{"name":221,"role":227},"in-house","DBS runs two generative AI virtual assistants on its own AI platforms: DBS digibot for individual customers in Singapore, Hong Kong and Taiwan, and DBS Joy for corporate and SME customers. In July 2026 DBS Joy became agentic in Singapore and now answers questions such as payment status and fees from the customer's own transaction and account data. DBS digibot answers card, refund, fee waiver and remittance questions today; DBS plans to add agentic tasks such as checking card usage, tracking reward points and blocking or replacing cards in the fourth quarter of 2026, for logged in customers only.","scaled",2026,[27,28],[],[234,242,247],{"kpi":49,"value":235,"unit":236,"qualifier":237,"period":238,"claimant":239,"quote":240,"sourceUrl":241},90,"percent","approximately","DBS digibot, first half of 2026, queries resolved without a follow up call","organization","In the first half of 2026, DBS digibot successfully resolved nine in every 10 queries digitally, without customers needing to make a follow-up call.","https://www.dbs.com/newsroom/DBS_Gen_AI_enabled_virtual_assistants_reach_10_million_customers_and_go_agentic",{"kpi":51,"value":243,"unit":236,"qualifier":244,"period":245,"claimant":239,"quote":246,"sourceUrl":241},7,"exact","DBS Joy in Singapore, first six months of 2026, calls or emails to customer service","Active users increased by 61%, contributing to a 7% reduction in calls or emails to customer service.",{"kpi":54,"value":248,"unit":236,"qualifier":244,"period":249,"claimant":239,"quote":250,"sourceUrl":241},17,"DBS Joy in Singapore, first six months of 2026","Customer satisfaction scores for DBS Joy rose by 17% over the same period.",true,[253],{"url":241,"title":254,"publisher":221,"date":255},"DBS' Gen AI-enabled virtual assistants reach 10 million customers and go agentic","2026-07-28",{"level":257,"checkedAt":210},"source-verified","B","dbs-joy-and-digibot-virtual-assistants","dbs-bank",{"title":262,"useCases":263,"organization":264,"vendors":267,"summary":271,"stage":229,"year":272,"channels":273,"languages":274,"metrics":276,"outcomeDisclosed":251,"sources":283,"verification":288,"grade":289,"id":290,"organizationSlug":291},"Commonwealth Bank: AI orchestration for messaging service with human handoff",[214,206,204],{"name":265,"anonymized":222,"country":266,"region":224,"industry":16},"Commonwealth Bank of Australia","AU",[268],{"name":269,"role":270},"Microsoft","platform","Commonwealth Bank built a central AI orchestration agent that reads the customer's intent and routes it to a conversational AI, retrieval over public content, a deterministic guarded path for regulated journeys such as fraud disputes, or a human specialist with the full context, on its messaging channel. It migrated nearly 700 chatbot topics and launched a generative AI banking chatbot in November 2024. Voice bots are a planned extension of the orchestration layer.",2024,[],[275],"en",[277],{"kpi":49,"value":278,"unit":236,"qualifier":237,"period":279,"claimant":280,"quote":281,"sourceUrl":282},84.6,"May 2026, self service messaging","vendor","In May 2026, approximately 84.6% of self-service messaging interactions were resolved end-to-end in the messaging channel.","https://news.microsoft.com/source/asia/features/how-commonwealth-bank-and-microsoft-are-reimagining-the-future-of-customer-service/",[284],{"url":282,"title":285,"publisher":286,"date":287},"How Commonwealth Bank and Microsoft are reimagining the future of customer service","Microsoft Source Asia","2026-07-08",{"level":257,"checkedAt":210},"C","commonwealth-bank-customer-service-orchestration","commonwealth-bank-of-australia",0,[294,302,307],{"kpi":49,"label":295,"unit":236,"aggregate":251,"higherIsBetter":251,"n":66,"nUpTo":292,"median":296,"min":278,"max":235,"byClaimant":297,"vendorOnly":222,"points":299},"Containment rate",87.3,{"organization":298,"vendor":298,"regulator":292,"independent":292},1,[300,301],{"evidenceId":259,"organization":221,"value":235,"qualifier":237,"claimant":239,"grade":258,"pooled":251},{"evidenceId":290,"organization":265,"value":278,"qualifier":237,"claimant":280,"grade":289,"pooled":251},{"kpi":51,"label":303,"unit":236,"aggregate":251,"higherIsBetter":251,"n":298,"nUpTo":292,"median":243,"min":243,"max":243,"byClaimant":304,"vendorOnly":222,"points":305},"Contact deflection",{"organization":298,"vendor":292,"regulator":292,"independent":292},[306],{"evidenceId":259,"organization":221,"value":243,"qualifier":244,"claimant":239,"grade":258,"pooled":251},{"kpi":54,"label":308,"unit":236,"aggregate":251,"higherIsBetter":251,"n":298,"nUpTo":292,"median":248,"min":248,"max":248,"byClaimant":309,"vendorOnly":222,"points":310},"Satisfaction uplift",{"organization":298,"vendor":292,"regulator":292,"independent":292},[311],{"evidenceId":259,"organization":221,"value":248,"qualifier":244,"claimant":239,"grade":258,"pooled":251},{"low":313,"high":314},720000,8640000,[316,352,372,382,400,410],{"slug":204,"title":317,"shortTitle":318,"definition":319,"status":9,"industries":320,"functions":326,"patterns":327,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":329,"publicEvidenceCount":330,"organizations":331,"bestGrade":258,"headline":348,"lastVerified":210,"indexable":251},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[321,16,17,322,323,324,325],"cross-industry","telecommunications","travel-and-hospitality","retail-and-ecommerce","wealth-and-asset-management",[19],[22,23,25,328],"classification-and-routing",25,18,[332,333,334,335,336,265,337,338,339,340,341,342,343,344,345,346,347],"Air India","Airbnb","Bank of America","Bank of the Philippine Islands","BT Group","Ingka Group","JetBlue","Klarna","Lufthansa Group","Mobily","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":49,"label":295,"unit":236,"n":243,"nUpTo":292,"kind":349,"value":350,"qualifier":244,"claimant":351,"organization":351,"vendorReported":222},"median",47,null,{"slug":205,"title":353,"shortTitle":354,"definition":355,"status":9,"industries":356,"functions":358,"patterns":360,"audience":362,"autonomy":32,"adoptionStage":363,"segment":362,"evidenceCount":86,"publicEvidenceCount":66,"organizations":364,"bestGrade":289,"headline":367,"lastVerified":210,"indexable":251},"AI for back office account servicing execution","Account servicing execution","AI that executes the servicing requests that land in operations queues, such as address and mandate changes, standing instructions, beneficiary updates, reissues, payoff and reference letters and loan maintenance, by reading the request, checking it against policy and entitlements, and preparing or making the change in core systems under dual control.",[16,357,325],"insurance",[20,359],"lending-and-credit",[24,361,328],"document-processing","back-office","early-adopters",[365,366],"Banco Supervielle","SS&C Technologies",{"kpi":368,"label":369,"unit":236,"n":66,"nUpTo":292,"kind":370,"value":371,"qualifier":244,"claimant":280,"organization":366,"vendorReported":251},"processing-time-reduction","Cycle time reduction","reported",95,{"slug":206,"title":373,"shortTitle":374,"definition":375,"status":9,"industries":376,"functions":377,"patterns":379,"audience":31,"autonomy":32,"adoptionStage":363,"segment":34,"evidenceCount":67,"publicEvidenceCount":86,"organizations":380,"bestGrade":258,"headline":351,"lastVerified":210,"indexable":251},"AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[16,17],[19,378,20],"fraud-prevention",[22,23,328,361,24],[265,339,381],"Visa",{"slug":207,"title":383,"shortTitle":384,"definition":385,"status":9,"industries":386,"functions":387,"patterns":388,"audience":31,"autonomy":32,"adoptionStage":389,"segment":34,"evidenceCount":390,"publicEvidenceCount":390,"organizations":391,"bestGrade":258,"headline":396,"lastVerified":210,"indexable":251},"AI agent for fraud alert confirmation with cardholders","Fraud alert confirmation","A customer facing AI agent that contacts the cardholder as soon as the fraud engine flags a card transaction, in the channel they actually respond to, verifies them, asks whether they made the transaction and acts on the answer: releasing the block so a retry succeeds, or freezing the card and starting the fraud claim.",[16,17],[378,19],[22,23,24],"emerging",5,[392,265,393,394,395],"Capital One","Macquarie Bank","Revolut","Westpac",{"kpi":397,"label":398,"unit":236,"n":66,"nUpTo":292,"kind":370,"value":399,"qualifier":244,"claimant":239,"organization":265,"vendorReported":222},"fraud-loss-reduction","Fraud loss reduction",76,{"slug":208,"title":401,"shortTitle":402,"definition":403,"status":9,"industries":404,"functions":405,"patterns":406,"audience":31,"autonomy":32,"adoptionStage":389,"segment":34,"evidenceCount":298,"publicEvidenceCount":298,"organizations":407,"bestGrade":258,"headline":408,"lastVerified":210,"indexable":251},"AI agent for ATM and self service device assistance","ATM and device assistance","An AI agent that helps customers with problems at or around ATMs and other self service devices, such as a withdrawal that did not pay out, a retained card, a blocked PIN or finding a working machine with cash, over the app, chat or phone, and that opens and tracks the claim or hands it to a person when it cannot be resolved.",[16],[19,20],[22,23,24],[342],{"kpi":54,"label":308,"unit":236,"n":298,"nUpTo":292,"kind":370,"value":409,"qualifier":244,"claimant":239,"organization":342,"vendorReported":222},150,{"slug":209,"title":411,"shortTitle":412,"definition":413,"status":9,"industries":414,"functions":415,"patterns":418,"audience":31,"autonomy":32,"adoptionStage":363,"segment":34,"evidenceCount":87,"publicEvidenceCount":86,"organizations":420,"bestGrade":289,"headline":424,"lastVerified":210,"indexable":251},"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.",[16,17,325],[416,417,19],"onboarding-and-kyc","sales",[22,361,419,24],"computer-vision",[421,422,423],"Albo","Deutsche Bank","M-DAQ Global",{"kpi":425,"label":426,"unit":427,"n":298,"nUpTo":292,"kind":370,"value":428,"qualifier":244,"claimant":280,"organization":423,"vendorReported":251},"productivity-gain","Productivity gain","multiplier",30,{"indexable":251,"reasons":430},[],[432,436,441,449,456,461,468,473,480,485,491,497,504,511,517,522,529,534,540,546,552,558,564,569,573,580,586,591,596,603,609,615,621,626],{"id":164,"label":433,"issuer":175,"region":176,"url":177,"description":434,"useCases":435,"indexable":251},"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":165,"label":437,"issuer":175,"region":176,"url":438,"description":439,"useCases":440,"indexable":251},"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":442,"label":443,"issuer":444,"region":445,"url":446,"description":447,"useCases":448,"indexable":251},"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":450,"label":451,"issuer":452,"region":181,"url":453,"description":454,"useCases":455,"indexable":251},"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":457,"issuer":175,"region":176,"url":458,"description":459,"useCases":460,"indexable":251},"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":462,"label":463,"issuer":464,"region":176,"url":465,"description":466,"useCases":467,"indexable":251},"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":168,"label":469,"issuer":470,"region":176,"url":471,"description":472,"useCases":350,"indexable":251},"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.",{"id":474,"label":475,"issuer":476,"region":224,"url":477,"description":478,"useCases":479,"indexable":251},"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":169,"label":481,"issuer":482,"region":224,"url":483,"description":484,"useCases":329,"indexable":251},"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":167,"label":486,"issuer":487,"region":445,"url":488,"description":489,"useCases":490,"indexable":251},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":492,"label":493,"issuer":494,"region":181,"url":495,"description":496,"useCases":490,"indexable":251},"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":498,"label":499,"issuer":500,"region":176,"url":501,"description":502,"useCases":503,"indexable":251},"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":505,"label":506,"issuer":507,"region":445,"url":508,"description":509,"useCases":510,"indexable":251},"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":512,"label":513,"issuer":175,"region":176,"url":514,"description":515,"useCases":516,"indexable":251},"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":518,"label":519,"issuer":175,"region":176,"url":520,"description":521,"useCases":516,"indexable":251},"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":523,"label":524,"issuer":525,"region":181,"url":526,"description":527,"useCases":528,"indexable":251},"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":171,"label":530,"issuer":175,"region":176,"url":531,"description":532,"useCases":533,"indexable":251},"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":535,"label":536,"issuer":537,"region":181,"url":538,"description":539,"useCases":533,"indexable":251},"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":541,"label":542,"issuer":543,"region":445,"url":544,"description":545,"useCases":533,"indexable":251},"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":547,"label":548,"issuer":175,"region":176,"url":549,"description":550,"useCases":551,"indexable":251},"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":553,"label":554,"issuer":555,"region":181,"url":556,"description":557,"useCases":551,"indexable":251},"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":559,"label":560,"issuer":476,"region":224,"url":561,"description":562,"useCases":563,"indexable":251},"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":565,"label":566,"issuer":175,"region":176,"url":567,"description":568,"useCases":563,"indexable":251},"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":170,"label":570,"issuer":175,"region":176,"url":571,"description":572,"useCases":563,"indexable":251},"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":574,"label":575,"issuer":576,"region":176,"url":577,"description":578,"useCases":579,"indexable":251},"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":581,"label":582,"issuer":180,"region":181,"url":583,"description":584,"useCases":585,"indexable":251},"us-ecoa-reg-b","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":587,"label":588,"issuer":175,"region":176,"url":589,"description":590,"useCases":585,"indexable":251},"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":592,"label":593,"issuer":175,"region":176,"url":594,"description":595,"useCases":87,"indexable":251},"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":597,"label":598,"issuer":599,"region":600,"url":601,"description":602,"useCases":390,"indexable":251},"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":604,"label":605,"issuer":606,"region":176,"url":607,"description":608,"useCases":67,"indexable":251},"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":610,"label":611,"issuer":612,"region":176,"url":613,"description":614,"useCases":67,"indexable":251},"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":616,"label":617,"issuer":618,"region":224,"url":619,"description":620,"useCases":86,"indexable":251},"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":622,"label":623,"issuer":175,"region":176,"url":624,"description":625,"useCases":86,"indexable":251},"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":627,"label":628,"issuer":629,"region":181,"url":630,"description":631,"useCases":86,"indexable":251},"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.",1790598294162]