[{"data":1,"prerenderedAt":547},["ShallowReactive",2],{"uc-atm-and-self-service-device-assistance":3,"uc-regulations":343},{"useCase":4,"evidence":185,"blitsAiDeployments":224,"benchmarks":225,"indicative":232,"related":235,"indexability":341,"includeUnpublished":191},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":24,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":48,"macroEstimates":77,"feasibility":78,"implementation":92,"risk":129,"blitsAi":162,"faq":164,"related":174,"datePublished":180,"dateModified":180,"lastVerified":180,"changelog":181,"slug":184},"AI agent for ATM and self service device assistance","ATM and device assistance","AI assistant for ATM disputes and card problems","An AI agent for failed ATM withdrawals, retained cards and PIN blocks. NatWest starts ATM disputes in Cora; rules such as US Regulation E set deadlines.","published","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.",[12,13,14],"ATM dispute assistant","cash machine help bot","self service device support agent",[16],"banking",[18,19],"customer-service","operations",[21,22,23],"conversational-agent","voice-agent","agentic-workflow",[25,26,27,28],"mobile-app","web-chat","voice","kiosk","customer-facing","supervised-agent","emerging","front-office","ATM problems arrive at the worst moment: the customer needs cash now, the machine kept their\ncard or debited the account without paying out, and the branch is closed. The customer calls,\nwaits, explains the machine location and time, and is told a claim will take days. Behind the\nscenes, the bank checks the claim against the device and transaction records.\n\nThe work splits into three kinds of request. Simple information (where is the nearest machine\nthat has cash and accepts deposits). Card and PIN problems after a retained card or too many PIN\nattempts. And cash disputes, which are regulated: in the United States, for example, Regulation E\nin the general case gives the bank 10 business days to investigate an incorrect amount from an\nelectronic terminal, or up to 45 days if it provisionally credits the account, with longer limits\nfor new accounts and for withdrawals outside the US. Each kind needs a different mix of\nlookups, authentication and human review.",[],"1. **Recognize the device problem.** The agent detects the intent (\"the ATM took my card\", \"no\n   cash came out\") and asks for the machine, time and amount, pulling the candidate transaction\n   from the account instead of asking the customer to type it.\n2. **Authenticate before acting.** Card and PIN actions and any claim require step up\n   authentication in the app or on the phone.\n3. **Resolve the simple cases.** It locates working machines, explains what happens to a\n   retained card, blocks it and orders a replacement through approved card APIs.\n4. **Open the cash dispute correctly.** For a withdrawal that did not pay out it opens the claim\n   with the required data, explains the investigation timeline and any provisional credit the\n   rules require, and tracks the status.\n5. **Hand over when needed.** Claims that fail automatic reconciliation, suspected fraud or\n   distressed customers go to a person with the case already assembled.",[37,38,39,40],"customer-experience","cost-to-serve","speed","compliance",[42,43,44,45,46,47],"containment-rate","processing-time-reduction","cycle-time-days","customer-satisfaction","customer-satisfaction-uplift","interactions-handled",{"referenceOrg":49,"inputs":50,"formula":72,"currency":73,"period":74,"resultLabel":75,"caveat":76},"A retail bank with a network of about 2,000 ATMs",[51,58,65],{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"deviceContacts","Contacts per year about ATM and device problems",50000,100000,"contacts per year","Editorial assumption, replace with your own contact reason and claim volumes.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"resolvedShare","Share of those contacts the agent resolves or files without a human",0.3,0.5,"fraction of contacts","Editorial assumption; no deployment on this page discloses a rate for ATM journeys.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"costPerContact","Cost of a human handled contact or manually filed claim",4,8,"USD per contact","Editorial assumption, replace with your own fully loaded cost.","deviceContacts * resolvedShare * costPerContact","USD","per year","Contact and claim handling cost avoided","Handling cost only. It leaves out the cost of the AI and integrations, faster reconciliation in back office operations, fewer regulatory breaches on dispute deadlines and the customer value of getting help when branches are closed.",[],{"complexity":79,"complexityNote":80,"dataPrerequisites":81,"integrations":86},"medium","Machine location and card actions are standard integrations. Cash disputes are harder: the agent needs the transaction record, the device journal or reconciliation result and the dispute rules of each market, and the claim must be auditable.",[82,83,84,85],"ATM and device inventory with location, status, cash and deposit capability","Transaction data that links a withdrawal to a device and time","Dispute rules per market, including deadlines and provisional credit","Contact reason data for device related contacts",[87,88,89,90,91],"ATM monitoring or device management system","Card management platform (block, replace, PIN unblock)","Core banking transactions and the dispute or claims system","Step up authentication in the app and on the phone","Contact centre platform for handover",{"steps":93,"guardrails":106,"humanInTheLoop":112,"kpisToInstrument":113,"failureModes":119},[94,97,100,103],{"title":95,"detail":96},"Split the intents","Separate information requests, card and PIN problems and cash disputes. Launch the first two, which need no investigation, before the dispute journey.",{"title":98,"detail":99},"Wire the dispute journey to the rules","Encode deadlines, required data and provisional credit logic per market as configuration owned by the disputes team, not as model instructions, and test it against past claims.",{"title":101,"detail":102},"Use the data the bank already has","Prefill the machine, time and amount from the transaction record and reconciliation data so the customer confirms rather than types, which cuts errors in filed claims.",{"title":104,"detail":105},"Close the loop","Send status updates on open claims in the same channel and let customers ask about a claim without calling.",[107,108,109,110,111],"Step up authentication before any card, PIN or claim action","Card numbers and PINs never pass through the model; card data is tokenized before it reaches the conversation","Dispute deadlines and provisional credit decided by rules, not generated by the model","Suspected fraud, repeated claims and distressed customers always go to a person","An auditable record of every claim the agent opened, with the data it used","Dispute analysts own every claim that does not reconcile automatically and every refusal. The disputes team signs off the rules the agent follows and reviews a sample of filed claims each week for completeness.",[114,115,116,117,118],"Share of device contacts resolved or correctly filed without a human","Median days from claim to resolution","Share of filed claims that were complete on first submission","Dispute deadline breaches","Customer satisfaction on device journeys",[120,123,126],{"title":121,"detail":122},"Wrong promises on refunds","The agent tells a customer money will be back by a date the rules do not support. Generate timelines from rules and approved wording only.",{"title":124,"detail":125},"Card data in the conversation","Customers type card numbers or PINs into chat. Detect and tokenize them before they reach the model or logs, and tell the customer not to share a PIN.",{"title":127,"detail":128},"Stale device data","The agent sends a customer to a machine that is out of cash. Use live device status and say when data may be out of date.",{"euAiAct":130,"regulations":133,"guidance":142,"controls":155,"incidents":161},{"tier":131,"basis":132},"limited","A customer facing assistant must tell people they are interacting with an AI system unless that is obvious (Article 50(1)). It does not evaluate creditworthiness (Annex III point 5(b)) or eligibility for public assistance benefits (point 5(a)), so it is not high risk; biometric verification whose sole purpose is to confirm identity is excluded from Annex III point 1(a).",[134,135,136,137,138,139,140,141],"eu-ai-act","gdpr","uk-gdpr","eu-psd2","pci-dss","dora","uk-consumer-duty","eu-accessibility-act",[143,149],{"title":144,"issuer":145,"region":146,"url":147,"note":148},"§ 1005.11 Procedures for resolving errors (Regulation E)","Consumer Financial Protection Bureau","north-america","https://www.consumerfinance.gov/rules-policy/regulations/1005/11/","The US rule for investigating electronic fund transfer errors, including receipt of an incorrect amount of money from an electronic terminal such as an ATM. In the general case the bank has 10 business days to investigate, or up to 45 days if it provisionally credits the account. For transfers within 30 days of the first deposit to a new account, the limits are 20 business days (instead of 10) and 90 days (instead of 45). For transfers not initiated in the US, such as a withdrawal abroad, and for point of sale debit card transfers, the 45 day limit becomes 90 days. An example of the dispute rules the agent must follow.",{"title":150,"issuer":151,"region":152,"url":153,"note":154},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context.",[156,157,158,159,160],"Dispute rules per market as reviewed configuration with a named owner","PCI DSS scoping of every component that could see card data","Audit trail of every card action and claim with the authentication level used","Regression tests on past claims for every change","Monitoring of deadline breaches and complaints about device journeys",[],{"howToBuild":163},"On Blits.ai the device journeys are **flows** with deterministic steps for authentication,\ncard actions and claim filing, calling the bank's device, card and dispute systems through\n**custom functions**, with an **AI agent** for the open questions around them. Location and\nprocedure questions are answered from a **knowledge base** of approved content.\n\nThe same journeys run in **web chat**, inside the bank's mobile app through the **REST or\nWebSocket API channel**, and on the **phone**, with real time streaming speech recognition and\nDTMF input on voice. Card numbers typed in free text are detected and\n**tokenized at the gateway**, **PII masking** keeps personal data out of prompts, and\n**human handover** passes the assembled claim to the disputes team. **Test suites** replay\npast claims before each release, and analytics show interactions, top intents, satisfaction\nand unexpected answers per channel.",[165,168,171],{"question":166,"answer":167},"Do banks already use AI assistants for ATM problems?","Yes, as the entry point. NatWest tells customers whose ATM withdrawal did not pay out to start the dispute by typing \"ATM dispute\" to Cora, its AI assistant, in online or mobile banking. NatWest reports a 150% improvement in customer satisfaction for Cora+ overall, but public outcome figures for ATM journeys specifically are scarce.",{"question":169,"answer":170},"Can the agent refund a failed withdrawal automatically?","Only where the bank's rules allow it, for example when reconciliation confirms the machine did not dispense. Everything else is an investigation with regulated deadlines, owned by a human analyst.",{"question":172,"answer":173},"How quickly must a bank resolve an ATM cash dispute?","It depends on the market. In the general case, US Regulation E requires the bank to decide within 10 business days of the notice, or within 45 days if it provisionally credits the account within those 10 business days. For transfers within 30 days of the first deposit to a new account, the limits are 20 business days (instead of 10) and 90 days (instead of 45). For transfers not initiated in the US, such as a withdrawal abroad, and for point of sale debit card transfers, the 45 day limit becomes 90 days. The rule covers consumer accounts only. The agent should quote these timelines from configured rules, never from the model.",[175,176,177,178,179],"account-and-card-servicing-agent","card-dispute-and-chargeback-intake","branch-and-appointment-booking-agent","first-line-contact-centre-agent","retail-store-and-kiosk-assistant","2026-09-27",[182],{"date":180,"note":183},"First published","atm-and-self-service-device-assistance",[186],{"title":187,"useCases":188,"organization":189,"vendors":193,"summary":194,"stage":195,"year":196,"channels":197,"languages":198,"metrics":200,"outcomeDisclosed":209,"sources":210,"verification":218,"grade":221,"id":222,"organizationSlug":223},"NatWest: Cora AI assistant as the front door for everyday queries, including ATM disputes",[178,184],{"name":190,"anonymized":191,"country":192,"region":152,"industry":16},"NatWest Group",false,"GB",[],"NatWest routes a wide range of everyday customer queries through Cora, its AI assistant in online banking and the mobile app, now with generative AI (Cora+). Customers whose ATM withdrawal did not pay out are sent to Cora with the phrase \"ATM dispute\" as the first step of the claim, and the assistant is available before login as well. NatWest says the generative AI version improved customer satisfaction and reduced how often a colleague has to step in, and in 2025 it began a collaboration with OpenAI to extend the assistant to more complex tasks.","scaled",2025,[25,26],[199],"en",[201],{"kpi":46,"value":202,"unit":203,"qualifier":204,"period":205,"claimant":206,"quote":207,"sourceUrl":208},150,"percent","exact","Cora+ generative AI functionality","organization","The GenAI functionality offered by Cora+ has shown a 150% improvement in customer satisfaction, while reducing the number of times a colleague needs to intervene.","https://www.natwestgroup.com/news-and-insights/news-room/press-releases/ai-and-data/2025/mar/natwest-open-ai-collaborate-to-accelerate-cutting-edge-ai-transf.html",true,[211,214],{"url":208,"title":212,"publisher":190,"date":213},"NatWest & OpenAI collaborate to accelerate cutting-edge AI transformation in support of bank-wide simplification and enhanced customer experience","2025-03-20",{"url":215,"title":216,"publisher":217},"https://www.natwest.com/support-centre/banking-near-me/withdrawals/i-have-used-an-atm-to-withdraw-money-and-i-didnt-receive-anything.html","I have used an ATM to withdraw money and I didn't receive anything?","NatWest",{"level":219,"checkedAt":220},"source-verified","2026-09-26","B","natwest-cora-ai-assistant","natwest-group",0,[226],{"kpi":46,"label":227,"unit":203,"aggregate":209,"higherIsBetter":209,"n":228,"nUpTo":224,"median":202,"min":202,"max":202,"byClaimant":229,"vendorOnly":191,"points":230},"Satisfaction uplift",1,{"organization":228,"vendor":224,"regulator":224,"independent":224},[231],{"evidenceId":222,"organization":190,"value":202,"qualifier":204,"claimant":206,"grade":221,"pooled":209},{"low":233,"high":234},60000,400000,[236,255,271,289,319],{"slug":175,"title":237,"shortTitle":238,"definition":239,"status":9,"industries":240,"functions":242,"patterns":243,"audience":29,"autonomy":30,"adoptionStage":245,"segment":32,"evidenceCount":68,"publicEvidenceCount":246,"organizations":247,"bestGrade":221,"headline":250,"lastVerified":180,"indexable":209},"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.",[16,241],"payments",[18,19],[21,22,23,244],"rag-knowledge-assistant","mainstream",2,[248,249],"Commonwealth Bank of Australia","DBS Bank",{"kpi":42,"label":251,"unit":203,"n":246,"nUpTo":224,"kind":252,"value":253,"qualifier":254,"claimant":206,"organization":249,"vendorReported":191},"Containment rate","reported",90,"approximately",{"slug":176,"title":256,"shortTitle":257,"definition":258,"status":9,"industries":259,"functions":260,"patterns":262,"audience":29,"autonomy":30,"adoptionStage":265,"segment":32,"evidenceCount":68,"publicEvidenceCount":266,"organizations":267,"bestGrade":221,"headline":270,"lastVerified":180,"indexable":209},"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.",[16,241],[18,261,19],"fraud-prevention",[21,22,263,264,23],"classification-and-routing","document-processing","early-adopters",3,[248,268,269],"Klarna","Visa",null,{"slug":177,"title":272,"shortTitle":273,"definition":274,"status":9,"industries":275,"functions":280,"patterns":282,"audience":29,"autonomy":283,"adoptionStage":265,"evidenceCount":68,"publicEvidenceCount":68,"organizations":284,"bestGrade":221,"headline":270,"lastVerified":180,"indexable":209},"AI agent for branch finding and appointment booking","Branch and appointment booking","A conversational agent that finds the nearest suitable location, checks opening hours and which services it offers, books an in person or video appointment with the right specialist, and records the reason for the visit so staff are prepared. In banking it answers \"where is my nearest branch\" and books the mortgage or business banker; the same job exists in retail, healthcare and property.",[276,16,277,278,279],"cross-industry","retail-and-ecommerce","healthcare","real-estate",[18,281],"sales",[21,23,244,22],"autonomous",[285,286,287,288],"Bank of America","Best Buy","Hemominas","MOGUL.sg",{"slug":178,"title":290,"shortTitle":291,"definition":292,"status":9,"industries":293,"functions":297,"patterns":298,"audience":29,"autonomy":30,"adoptionStage":245,"segment":32,"evidenceCount":299,"publicEvidenceCount":300,"organizations":301,"bestGrade":221,"headline":315,"lastVerified":180,"indexable":209},"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.",[276,16,241,294,295,277,296],"telecommunications","travel-and-hospitality","wealth-and-asset-management",[18],[21,22,244,263],25,18,[302,303,285,304,305,248,306,307,268,308,309,190,310,311,312,313,314],"Air India","Airbnb","Bank of the Philippine Islands","BT Group","Ingka Group","JetBlue","Lufthansa Group","Mobily","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":42,"label":251,"unit":203,"n":316,"nUpTo":224,"kind":317,"value":318,"qualifier":204,"claimant":270,"organization":270,"vendorReported":191},7,"median",47,{"slug":179,"title":320,"shortTitle":321,"definition":322,"status":9,"industries":323,"functions":324,"patterns":326,"audience":329,"autonomy":330,"adoptionStage":265,"segment":32,"evidenceCount":266,"publicEvidenceCount":266,"organizations":331,"bestGrade":335,"headline":336,"lastVerified":180,"indexable":209},"AI assistant for telecom retail stores, from associate copilot to digital human kiosk","Retail store and kiosk assistant","An AI assistant for telecom shops that gives store associates quick, sourced answers on plans, promotions, devices and the customer's account during the conversation, and that can also greet and serve customers directly on an in store screen or kiosk, sometimes as a digital human, handing them to an associate when they are ready to buy or need help.",[294],[281,18,325],"knowledge-management",[244,327,21,328],"digital-human","recommendation-and-personalization","employee-facing","assist",[332,333,334],"Bouygues Telecom","Deutsche Telekom","T-Mobile","C",{"kpi":337,"label":338,"unit":203,"n":228,"nUpTo":224,"kind":252,"value":339,"qualifier":204,"claimant":340,"organization":332,"vendorReported":209},"accuracy","Accuracy",95,"vendor",{"indexable":209,"reasons":342},[],[344,349,354,362,369,374,380,385,393,399,405,411,418,425,431,436,443,448,454,460,466,472,478,483,487,494,499,504,510,518,524,530,536,541],{"id":134,"label":345,"issuer":151,"region":152,"url":346,"description":347,"useCases":348,"indexable":209},"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":135,"label":350,"issuer":151,"region":152,"url":351,"description":352,"useCases":353,"indexable":209},"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":355,"label":356,"issuer":357,"region":358,"url":359,"description":360,"useCases":361,"indexable":209},"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":363,"label":364,"issuer":365,"region":146,"url":366,"description":367,"useCases":368,"indexable":209},"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":139,"label":370,"issuer":151,"region":152,"url":371,"description":372,"useCases":373,"indexable":209},"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":136,"label":375,"issuer":376,"region":152,"url":377,"description":378,"useCases":379,"indexable":209},"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":140,"label":381,"issuer":382,"region":152,"url":383,"description":384,"useCases":318,"indexable":209},"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":386,"label":387,"issuer":388,"region":389,"url":390,"description":391,"useCases":392,"indexable":209},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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":394,"label":395,"issuer":396,"region":389,"url":397,"description":398,"useCases":299,"indexable":209},"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":138,"label":400,"issuer":401,"region":358,"url":402,"description":403,"useCases":404,"indexable":209},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":406,"label":407,"issuer":408,"region":146,"url":409,"description":410,"useCases":404,"indexable":209},"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":412,"label":413,"issuer":414,"region":152,"url":415,"description":416,"useCases":417,"indexable":209},"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":419,"label":420,"issuer":421,"region":358,"url":422,"description":423,"useCases":424,"indexable":209},"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":426,"label":427,"issuer":151,"region":152,"url":428,"description":429,"useCases":430,"indexable":209},"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":432,"label":433,"issuer":151,"region":152,"url":434,"description":435,"useCases":430,"indexable":209},"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":437,"label":438,"issuer":439,"region":146,"url":440,"description":441,"useCases":442,"indexable":209},"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":141,"label":444,"issuer":151,"region":152,"url":445,"description":446,"useCases":447,"indexable":209},"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":449,"label":450,"issuer":451,"region":146,"url":452,"description":453,"useCases":447,"indexable":209},"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":455,"label":456,"issuer":457,"region":358,"url":458,"description":459,"useCases":447,"indexable":209},"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":461,"label":462,"issuer":151,"region":152,"url":463,"description":464,"useCases":465,"indexable":209},"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":467,"label":468,"issuer":469,"region":146,"url":470,"description":471,"useCases":465,"indexable":209},"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":473,"label":474,"issuer":388,"region":389,"url":475,"description":476,"useCases":477,"indexable":209},"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":479,"label":480,"issuer":151,"region":152,"url":481,"description":482,"useCases":477,"indexable":209},"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":137,"label":484,"issuer":151,"region":152,"url":485,"description":486,"useCases":477,"indexable":209},"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":488,"label":489,"issuer":490,"region":152,"url":491,"description":492,"useCases":493,"indexable":209},"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":495,"label":496,"issuer":145,"region":146,"url":497,"description":498,"useCases":69,"indexable":209},"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.",{"id":500,"label":501,"issuer":151,"region":152,"url":502,"description":503,"useCases":69,"indexable":209},"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":505,"label":506,"issuer":151,"region":152,"url":507,"description":508,"useCases":509,"indexable":209},"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.",6,{"id":511,"label":512,"issuer":513,"region":514,"url":515,"description":516,"useCases":517,"indexable":209},"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.",5,{"id":519,"label":520,"issuer":521,"region":152,"url":522,"description":523,"useCases":68,"indexable":209},"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":525,"label":526,"issuer":527,"region":152,"url":528,"description":529,"useCases":68,"indexable":209},"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":531,"label":532,"issuer":533,"region":389,"url":534,"description":535,"useCases":266,"indexable":209},"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":537,"label":538,"issuer":151,"region":152,"url":539,"description":540,"useCases":266,"indexable":209},"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":542,"label":543,"issuer":544,"region":146,"url":545,"description":546,"useCases":266,"indexable":209},"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.",1790598294219]