[{"data":1,"prerenderedAt":619},["ShallowReactive",2],{"uc-offers-and-rewards-agent":3,"uc-regulations":412},{"useCase":4,"evidence":193,"blitsAiDeployments":307,"benchmarks":308,"indicative":316,"related":319,"indexability":410,"includeUnpublished":199},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":24,"channels":28,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":43,"indicativeValue":49,"macroEstimates":84,"feasibility":85,"implementation":99,"risk":142,"blitsAi":170,"faq":172,"related":182,"datePublished":188,"dateModified":188,"lastVerified":188,"changelog":189,"slug":192},"AI agent for personalized offers and rewards","Offers and rewards","AI agents for card offers and loyalty rewards","Banks use AI to pick the next best offer or reward for each customer. See how Commonwealth Bank and DBS target rewards, with the compliance risks to manage.","published","A customer facing AI agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment from their transactions and context, delivers it in the app, in messaging or through a colleague, and helps the customer understand, track and redeem rewards in conversation. Unlike campaign personalization, it works inside the customer's own account and loyalty relationship, one moment at a time.",[12,13,14,15,16],"next best action","next best offer","personalized card offers","rewards assistant","loyalty chatbot",[18,19],"banking","payments",[21,22,23],"marketing","sales","customer-service",[25,26,27],"recommendation-and-personalization","prediction-and-scoring","conversational-agent",[29,30,31,32],"mobile-app","web-chat","whatsapp","agent-desktop","customer-facing","autonomous","mainstream","front-office","Banks and card issuers run many offers (card linked merchant deals, rewards points, fee waivers,\nproduct upgrades) and mostly send them as campaigns to broad segments. Customers ignore what is\nnot relevant, points go unredeemed and the rewards budget buys little loyalty. Staff in branches\nand contact centres have no view of which conversation matters most for the customer in front of\nthem.\n\nThe opposite failure is just as real: aggressive targeting that pushes credit at customers who\nare struggling, or offers that systematically skip some groups. The job is to choose the one\nrelevant thing for this customer now, including \"nothing to sell, here is help instead\", and to\nmake it explainable.",[],"1. **Build the candidate list.** All offers, rewards and service messages the customer is\n   eligible for, filtered by business rules, consent and suitability.\n2. **Score and choose.** Propensity and value models rank the candidates; an arbitration layer\n   picks the next best action for the customer across all channels, so the app, the contact\n   centre and the branch show the same priority.\n3. **Deliver in context.** The action appears where the customer is: an in app card, a message\n   after a relevant purchase, or a prompt on a colleague's screen during a call.\n4. **Converse about rewards.** An assistant explains the points balance, what a reward is worth,\n   how to redeem and what is needed for the next tier, and completes the redemption through\n   approved APIs.\n5. **Learn.** Responses (accepted, ignored, dismissed) feed back into the models, and outcomes\n   are monitored for fairness and customer harm.",[41,42],"revenue-growth","customer-experience",[44,45,46,47,48],"conversion-rate-uplift","revenue-uplift","interactions-handled","churn-reduction","nps-change",{"referenceOrg":50,"inputs":51,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A card issuer with 1 million active cardholders",[52,58,65,72],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"cardholders","Active cardholders",1000000,"customers","The reference issuer.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"reachedShare","Share of cardholders who see personalized offers each year",0.3,0.5,"fraction of cardholders","Editorial assumption, depends on app usage and consent.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"extraAcceptance","Additional offer acceptances per reached cardholder versus generic campaigns",0.005,0.015,"acceptances per reached cardholder per year","Editorial assumption; no deployment on this page discloses a conversion uplift. Measure it against a control group.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"marginPerAcceptance","Margin per accepted offer",50,150,"USD per acceptance","Editorial assumption, replace with your own offer economics.","cardholders * reachedShare * extraAcceptance * marginPerAcceptance","USD","per year","Additional margin from personalized offers","Incremental offer margin only. It leaves out loyalty and retention effects, rewards costs saved by better targeting, merchant funded revenue, and the cost of the decisioning platform and data work.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Decisioning engines are mature. The effort is in clean eligibility and consent data, one arbitration across channels, measurement with control groups and the fairness and suitability rules that keep targeting safe.",[89,90,91,92],"Transaction and product holding data per customer","Offer catalog with eligibility rules, costs and expiry","Marketing consent and contact preferences per channel","Response history with control groups",[94,95,96,97,98],"Decisioning or next best action engine","Rewards and loyalty platform (balance, redemption)","Card linked offer provider where used","App, messaging and agent desktop channels","Consent management platform",{"steps":100,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[101,104,107,110,113],{"title":102,"detail":103},"Define the action catalog, including service","List every offer and every service action (a fee refund, a hardship check in, a reward reminder) and give each eligibility, suitability and consent rules. Service actions must be able to win against sales.",{"title":105,"detail":106},"Arbitrate in one place","Use one decisioning layer for all channels so the customer does not get three different offers from the app, email and a call. Commonwealth Bank's engine serves branches, the contact centre and digital channels from one decisioning engine.",{"title":108,"detail":109},"Measure with control groups","Hold out a random control group from the start, or no one will be able to say what the engine added.",{"title":111,"detail":112},"Add the rewards conversation","Let customers ask about points, value and redemption in the app assistant, with balances and redemption through the loyalty platform's APIs.",{"title":114,"detail":115},"Review fairness and harm regularly","Check who is shown and who is excluded from offers, and suppress credit offers for customers with signs of financial difficulty.",[117,118,119,120,121],"Eligibility, suitability and consent checked by rules before any model ranks an offer","No credit offers to customers with financial difficulty or vulnerability markers","Offers are clearly labelled as offers and respect opt outs on every channel","Reward values and redemption terms come from the loyalty platform, never generated text","Periodic bias review of targeting outcomes across customer groups","Marketing and product owners approve every offer, rule and model change. A conduct review checks targeting outcomes each quarter. Colleagues in branches and contact centres decide whether to raise a suggested conversation at all.",[124,125,126,127,128],"Acceptance rate versus a control group, per offer","Incremental revenue or margin per treated customer","Reward redemption rate and points balance age","Opt outs and complaints about offers","Offer exposure by customer segment",[130,133,136,139],{"title":131,"detail":132},"Optimising for the wrong customers","Models learn that struggling customers accept credit offers. Exclude them by rule and monitor outcomes.",{"title":134,"detail":135},"Invisible uplift","Without a control group, gains cannot be separated from seasonality. Hold out from day one.",{"title":137,"detail":138},"Channel conflict","Each channel runs its own targeting and customers get contradictory offers. Arbitrate centrally.",{"title":140,"detail":141},"Unexplainable targeting","A customer or regulator asks why an offer was shown or withheld and nobody can answer. Log the rules and scores behind each decision.",{"euAiAct":143,"regulations":146,"guidance":151,"controls":163,"incidents":169},{"tier":144,"basis":145},"context-dependent","Ranking offers is generally minimal risk and the conversational part carries the Article 50 transparency duty. Using AI to evaluate creditworthiness for a credit offer is high risk (Annex III point 5(b)), and Article 5 prohibits techniques that exploit vulnerabilities due to a person's social or economic situation to distort their behaviour in a harmful way.",[147,148,149,150],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty",[152,158],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Direct marketing and privacy and electronic communications","Information Commissioner's Office","europe","https://ico.org.uk/for-organisations/direct-marketing-and-privacy-and-electronic-communications/","The ICO's hub for UK direct marketing rules under PECR and data protection law, covering the lawful basis and consent for marketing messages, with its detailed direct marketing guide.",{"title":159,"issuer":160,"region":155,"url":161,"note":162},"Article 5: Prohibited AI practices","European Union","https://artificialintelligenceact.eu/article/5/","Bans manipulative techniques and the exploitation of vulnerabilities, including those due to a person's economic situation.",[164,165,166,167,168],"Decisioning models in the AI inventory with owners, validation and periodic bias review","Documented suitability rules for credit and high cost products","Consent and opt out enforcement across all channels","Decision logs that explain why each offer was shown or suppressed","Complaint and outcome monitoring for customers in vulnerable circumstances",[],{"howToBuild":171},"On Blits.ai the decisioning itself stays in the bank's next best action engine; the\nplatform delivers and explains its decisions. An **AI agent** in the app or on **WhatsApp**\ncalls the engine and the loyalty platform through **custom functions**, shows offers and\npoints with rich cards (loyalty points, credit cards, product recommendations) and completes\nredemptions through **flows** with confirmation.\n\nOffer terms and rewards rules sit in a **knowledge base** so explanations are grounded.\nReward values come from the loyalty platform, and **guardrails** help prevent the agent from\ninventing values or pushing credit to customers who mention financial difficulty, while\n**human handover** routes those conversations to people. **Analytics** track interactions,\nsatisfaction and sentiment, and **test suites** check the suppression rules before each release. The platform is model agnostic.",[173,176,179],{"question":174,"answer":175},"Is next best action the same as a recommendation engine?","It is broader. A next best action engine chooses among offers, service messages and doing nothing, across channels. Commonwealth Bank said in 2022 that its engine made over 35 million decisions a day and used it to suggest the next best conversation to have with each customer, including same day support for customers affected by natural disasters and matching customers to government benefits through its Benefits finder.",{"question":177,"answer":178},"Can an assistant help customers use their rewards?","Yes. Bank of America's Erica highlights cash back deals based on the client's spending and notifies clients of their eligibility for its Preferred Rewards program, and DBS plans to add reward point tracking to its digibot assistant in a later phase of its agentic rollout.",{"question":180,"answer":181},"What is the main compliance risk?","Unfair or harmful targeting, such as pushing credit at customers who are struggling or systematically excluding groups. Rules for suitability and consent, control groups and regular bias reviews are the defence.",[183,184,185,186,187],"personalized-marketing-at-scale","next-best-action-for-advisors","proactive-outbound-engagement-agent","financial-wellbeing-coach","account-and-card-servicing-agent","2026-09-27",[190],{"date":188,"note":191},"First published","offers-and-rewards-agent",[194,241,280],{"title":195,"useCases":196,"organization":197,"vendors":202,"summary":205,"stage":206,"year":207,"channels":208,"languages":209,"metrics":210,"outcomeDisclosed":231,"sources":232,"verification":236,"grade":238,"id":239,"organizationSlug":240},"DBS: generative and agentic AI in the DBS Joy and DBS digibot virtual assistants",[187,192],{"name":198,"anonymized":199,"country":200,"region":201,"industry":18},"DBS Bank",false,"SG","asia-pacific",[203],{"name":198,"role":204},"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,[29,30],[],[211,220,226],{"kpi":212,"value":213,"unit":214,"qualifier":215,"period":216,"claimant":217,"quote":218,"sourceUrl":219},"containment-rate",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":221,"value":222,"unit":214,"qualifier":223,"period":224,"claimant":217,"quote":225,"sourceUrl":219},"contact-deflection",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":227,"value":228,"unit":214,"qualifier":223,"period":229,"claimant":217,"quote":230,"sourceUrl":219},"customer-satisfaction-uplift",17,"DBS Joy in Singapore, first six months of 2026","Customer satisfaction scores for DBS Joy rose by 17% over the same period.",true,[233],{"url":219,"title":234,"publisher":198,"date":235},"DBS' Gen AI-enabled virtual assistants reach 10 million customers and go agentic","2026-07-28",{"level":237,"checkedAt":188},"source-verified","B","dbs-joy-and-digibot-virtual-assistants","dbs-bank",{"title":242,"useCases":243,"organization":246,"vendors":250,"summary":252,"stage":206,"year":253,"channels":254,"languages":255,"metrics":257,"outcomeDisclosed":231,"sources":270,"verification":277,"grade":238,"id":278,"organizationSlug":279},"Bank of America: Erica, a virtual financial assistant with proactive insights",[186,192,244,185,245],"first-line-contact-centre-agent","branch-and-appointment-booking-agent",{"name":247,"anonymized":199,"country":248,"region":249,"industry":18},"Bank of America","US","north-america",[251],{"name":247,"role":204},"Erica, launched in 2018, is Bank of America's virtual financial assistant in its Mobile Banking app. Beyond answering questions it delivers proactive, personalized insights: BankAmeriDeals cash back deals based on the client's spending, where balances are trending over the next seven days and eligibility for the Preferred Rewards program. It also gives guidance on investment topics for Merrill clients and hands off to people by scheduling appointments. The bank reports that clients have received and interacted with more than 1.7 billion of these insights, and that most users find the information they need, which it links to lower call centre volume. Bank of America says Erica selects answers from a predefined set and does not use generative AI or large language models.",2025,[29],[256],"en",[258,265],{"kpi":259,"value":260,"unit":261,"qualifier":215,"period":262,"claimant":217,"quote":263,"sourceUrl":264},"users-served",50000000,"count","since launch in 2018, as of August 2025","assisting nearly 50 million users since launch, surpassing 3 billion client interactions, and now averaging more than 58 million interactions per month","https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/08/a-decade-of-ai-innovation--bofa-s-virtual-assistant-erica-surpas.html",{"kpi":46,"value":266,"unit":261,"qualifier":267,"period":268,"claimant":217,"quote":269,"sourceUrl":264},3000000000,"at-least","client interactions since launch in 2018, as of August 2025","surpassing 3 billion client interactions",[271,274],{"url":264,"title":272,"publisher":247,"date":273},"A Decade of AI Innovation: BofA's Virtual Assistant Erica Surpasses 3 Billion Client Interactions","2025-08-20",{"url":275,"title":276,"publisher":247},"https://info.bankofamerica.com/en/digital-banking/erica","Erica: Virtual Financial Assistant",{"level":237,"checkedAt":188},"bank-of-america-erica-virtual-assistant","bank-of-america",{"title":281,"useCases":282,"organization":284,"vendors":287,"summary":291,"stage":206,"year":292,"channels":293,"languages":294,"metrics":295,"outcomeDisclosed":199,"sources":296,"verification":304,"grade":238,"id":305,"organizationSlug":306},"Commonwealth Bank: Customer Engagement Engine for next best conversations",[192,186,283,185,183],"loan-restructuring-recommendations",{"name":285,"anonymized":199,"country":286,"region":201,"industry":18},"Commonwealth Bank of Australia","AU",[288],{"name":289,"role":290},"Pegasystems","platform","Commonwealth Bank's Customer Engagement Engine (CEE), built on Pega Customer Decision Hub, suggests in real time the next best conversation to have with each customer, whether in the branch, on the phone, online or on a mobile device. Beyond suggesting conversations, the bank uses it to match customers to government benefits and rebates they may be missing (Benefits finder) and to reach customers hit by natural disasters with same day support such as a loan deferral. The same decisions feed digital channels and prompts for branch and contact centre staff.",2022,[29,32],[256],[],[297,301],{"url":298,"title":299,"publisher":285,"date":300},"https://www.commbank.com.au/articles/newsroom/2022/06/CBA-artificial-intelligence-usages.html","How artificial intelligence is changing the face of banking","2022-06-24",{"url":302,"title":303,"publisher":289},"https://www.pega.com/customers/cba-marketing","Delivering next best conversations with Pega",{"level":237,"checkedAt":188},"commonwealth-bank-customer-engagement-engine","commonwealth-bank-of-australia",5,[309],{"kpi":46,"label":310,"unit":261,"aggregate":199,"higherIsBetter":231,"n":311,"nUpTo":312,"median":266,"min":266,"max":266,"byClaimant":313,"vendorOnly":199,"points":314},"Interactions handled",1,0,{"organization":311,"vendor":312,"regulator":312,"independent":312},[315],{"evidenceId":278,"organization":247,"value":266,"qualifier":267,"claimant":217,"grade":238,"pooled":231},{"low":317,"high":318},75000,1125000,[320,347,369,383,396],{"slug":183,"title":321,"shortTitle":322,"definition":323,"status":9,"industries":324,"functions":329,"patterns":330,"audience":332,"autonomy":333,"adoptionStage":35,"evidenceCount":334,"publicEvidenceCount":222,"organizations":335,"bestGrade":238,"headline":342,"lastVerified":188,"indexable":231},"AI marketing personalization at scale","Marketing personalization at scale","AI that runs marketing campaigns at the level of the individual: it decides for each customer which product, offer, message or content to show next across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the marketing team's engine across many campaigns and channels, not an agent that converses with the customer.",[325,326,327,328,18],"cross-industry","travel-and-hospitality","media-and-entertainment","retail-and-ecommerce",[21,22],[25,26,331],"content-generation","back-office","supervised-agent",8,[336,337,285,338,339,340,341],"Amazon","Catchtable","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":44,"label":343,"unit":214,"n":311,"nUpTo":312,"kind":344,"value":345,"qualifier":223,"claimant":346,"organization":337,"vendorReported":231},"Conversion uplift","reported",30,"vendor",{"slug":184,"title":348,"shortTitle":349,"definition":350,"status":9,"industries":351,"functions":353,"patterns":355,"audience":356,"autonomy":357,"adoptionStage":358,"segment":36,"evidenceCount":307,"publicEvidenceCount":307,"organizations":359,"bestGrade":238,"headline":365,"lastVerified":188,"indexable":231},"AI next best action prompts for wealth advisors","Advisor next best action","An AI engine for wealth advisors, not customers, that scans an advisor's whole book and surfaces a short, ranked list of client specific prompts, such as idle cash, a maturing deposit, a concentration to review, a life event or an early sign of attrition, each with the reasoning and data behind it, for the advisor to act on or dismiss.",[352,18],"wealth-and-asset-management",[22,21,354],"analytics-and-reporting",[25,26,331],"employee-facing","assist","early-adopters",[360,361,362,363,364],"CIMB Niaga","Citi","JPMorgan Chase","Morgan Stanley","UBS",{"kpi":366,"label":367,"unit":214,"n":311,"nUpTo":312,"kind":344,"value":368,"qualifier":223,"claimant":217,"organization":364,"vendorReported":199},"employee-adoption","Employee adoption",80,{"slug":185,"title":370,"shortTitle":371,"definition":372,"status":9,"industries":373,"functions":374,"patterns":375,"audience":33,"autonomy":333,"adoptionStage":378,"segment":36,"evidenceCount":379,"publicEvidenceCount":379,"organizations":380,"bestGrade":238,"headline":382,"lastVerified":188,"indexable":231},"AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[18,19],[21,22,23],[27,376,377,25],"voice-agent","agentic-workflow","emerging",3,[247,381,285],"Capital One",null,{"slug":186,"title":384,"shortTitle":385,"definition":386,"status":9,"industries":387,"functions":388,"patterns":389,"audience":33,"autonomy":333,"adoptionStage":358,"segment":36,"evidenceCount":334,"publicEvidenceCount":390,"organizations":391,"bestGrade":238,"headline":382,"lastVerified":188,"indexable":231},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","An in app AI assistant that the customer opens to understand their own money: it uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[18],[23,21],[27,25,26,377],6,[247,285,392,393,394,395],"Hyundai Card","Royal Bank of Canada","Starling Bank","Westpac",{"slug":187,"title":397,"shortTitle":398,"definition":399,"status":9,"industries":400,"functions":401,"patterns":403,"audience":33,"autonomy":333,"adoptionStage":35,"segment":36,"evidenceCount":405,"publicEvidenceCount":406,"organizations":407,"bestGrade":238,"headline":408,"lastVerified":188,"indexable":231},"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.",[18,19],[23,402],"operations",[27,376,377,404],"rag-knowledge-assistant",4,2,[285,198],{"kpi":212,"label":409,"unit":214,"n":406,"nUpTo":312,"kind":344,"value":213,"qualifier":215,"claimant":217,"organization":198,"vendorReported":199},"Containment rate",{"indexable":231,"reasons":411},[],[413,418,423,431,438,444,449,455,462,469,476,482,489,496,502,507,514,520,526,532,538,544,550,555,560,567,573,578,583,590,596,602,608,613],{"id":147,"label":414,"issuer":160,"region":155,"url":415,"description":416,"useCases":417,"indexable":231},"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":148,"label":419,"issuer":160,"region":155,"url":420,"description":421,"useCases":422,"indexable":231},"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":424,"label":425,"issuer":426,"region":427,"url":428,"description":429,"useCases":430,"indexable":231},"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":432,"label":433,"issuer":434,"region":249,"url":435,"description":436,"useCases":437,"indexable":231},"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":439,"label":440,"issuer":160,"region":155,"url":441,"description":442,"useCases":443,"indexable":231},"dora","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":149,"label":445,"issuer":154,"region":155,"url":446,"description":447,"useCases":448,"indexable":231},"UK GDPR","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":150,"label":450,"issuer":451,"region":155,"url":452,"description":453,"useCases":454,"indexable":231},"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":456,"label":457,"issuer":458,"region":201,"url":459,"description":460,"useCases":461,"indexable":231},"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":463,"label":464,"issuer":465,"region":201,"url":466,"description":467,"useCases":468,"indexable":231},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":470,"label":471,"issuer":472,"region":427,"url":473,"description":474,"useCases":475,"indexable":231},"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":477,"label":478,"issuer":479,"region":249,"url":480,"description":481,"useCases":475,"indexable":231},"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":483,"label":484,"issuer":485,"region":155,"url":486,"description":487,"useCases":488,"indexable":231},"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":490,"label":491,"issuer":492,"region":427,"url":493,"description":494,"useCases":495,"indexable":231},"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":497,"label":498,"issuer":160,"region":155,"url":499,"description":500,"useCases":501,"indexable":231},"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":503,"label":504,"issuer":160,"region":155,"url":505,"description":506,"useCases":501,"indexable":231},"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":508,"label":509,"issuer":510,"region":249,"url":511,"description":512,"useCases":513,"indexable":231},"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":515,"label":516,"issuer":160,"region":155,"url":517,"description":518,"useCases":519,"indexable":231},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":521,"label":522,"issuer":523,"region":249,"url":524,"description":525,"useCases":519,"indexable":231},"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":527,"label":528,"issuer":529,"region":427,"url":530,"description":531,"useCases":519,"indexable":231},"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":533,"label":534,"issuer":160,"region":155,"url":535,"description":536,"useCases":537,"indexable":231},"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":539,"label":540,"issuer":541,"region":249,"url":542,"description":543,"useCases":537,"indexable":231},"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":545,"label":546,"issuer":458,"region":201,"url":547,"description":548,"useCases":549,"indexable":231},"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":551,"label":552,"issuer":160,"region":155,"url":553,"description":554,"useCases":549,"indexable":231},"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":556,"label":557,"issuer":160,"region":155,"url":558,"description":559,"useCases":549,"indexable":231},"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":561,"label":562,"issuer":563,"region":155,"url":564,"description":565,"useCases":566,"indexable":231},"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":568,"label":569,"issuer":570,"region":249,"url":571,"description":572,"useCases":334,"indexable":231},"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.",{"id":574,"label":575,"issuer":160,"region":155,"url":576,"description":577,"useCases":334,"indexable":231},"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":579,"label":580,"issuer":160,"region":155,"url":581,"description":582,"useCases":390,"indexable":231},"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":584,"label":585,"issuer":586,"region":587,"url":588,"description":589,"useCases":307,"indexable":231},"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":591,"label":592,"issuer":593,"region":155,"url":594,"description":595,"useCases":405,"indexable":231},"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":597,"label":598,"issuer":599,"region":155,"url":600,"description":601,"useCases":405,"indexable":231},"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":603,"label":604,"issuer":605,"region":201,"url":606,"description":607,"useCases":379,"indexable":231},"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":609,"label":610,"issuer":160,"region":155,"url":611,"description":612,"useCases":379,"indexable":231},"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":614,"label":615,"issuer":616,"region":249,"url":617,"description":618,"useCases":379,"indexable":231},"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.",1790598295766]