[{"data":1,"prerenderedAt":583},["ShallowReactive",2],{"uc-loyalty-rewards-personalization-agent":3,"uc-regulations":362},{"useCase":4,"evidence":182,"blitsAiDeployments":259,"benchmarks":260,"indicative":267,"related":270,"indexability":360,"includeUnpublished":188},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":23,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"problem":32,"problemStats":33,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":49,"macroEstimates":84,"feasibility":85,"implementation":97,"risk":138,"blitsAi":160,"faq":162,"related":172,"datePublished":176,"dateModified":176,"lastVerified":177,"changelog":178,"slug":181},"AI loyalty and rewards programme personalization","Loyalty programme personalization","AI loyalty programme personalization","AI personalizes loyalty rewards per member. Braze reports a 5% retention lift among at risk Panera Bread guests; Starbucks targets Rewards cohorts with Deep Brew AI.","published","AI loyalty personalization is AI that picks the offer, reward or message for each member of a retail, restaurant or travel loyalty programme, inside the programme's published rules. It unifies a member's profile across channels and predicts who is about to lapse, so each member gets a tailored action instead of the same blanket discount or point multiplier, and what a member has earned stays unchanged.",[12,13,14,15],"AI loyalty personalization","rewards programme AI","member personalization engine","loyalty churn prevention",[17,18,19],"retail-and-ecommerce","travel-and-hospitality","cross-industry",[21,22],"marketing","customer-service",[24,25],"recommendation-and-personalization","prediction-and-scoring",[27,28],"mobile-app","email","customer-facing","copilot","emerging","A loyalty programme only works if members actually use it. Many programmes still treat every member\nthe same: the same point multiplier, the same birthday coupon, the same generic email, whether a\nmember visits weekly or has not come back in months. That leaves two things on the table: members\nwho are about to lapse get no different treatment than loyal ones, and members close to a reward\nthey would value do not hear about it until it is too late to matter.\n\nThe job covers the loyalty programme itself. It runs inside the fixed rules of a membership\nprogramme (points, tiers, a redemption catalogue and a promise that status and benefits mean\nsomething) for the programme's own members. Salesforce reports that 65% of consumers say they\nwill remain loyal to companies that offer more personalized experiences, a high bar for a\nprogramme that still emails every member the same offer on the same day.",[34],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"65% of consumers say they will remain loyal to companies that offer more personalized experiences.","IHG Hotels & Resorts Aims to Boost Guest Loyalty with Salesforce","https:\u002F\u002Fwww.salesforce.com\u002Fnews\u002Fpress-releases\u002F2024\u002F04\u002F22\u002Fihg-customer-news\u002F",2024,"1. **Unify the member profile.** Purchase history, app and web behaviour, loyalty tier and\n   redemption history come together into one profile per member, across every brand and channel\n   the programme spans.\n2. **Predict who needs attention.** A model scores each member's risk of lapsing, or their\n   readiness for a specific reward, from recency, frequency and engagement signals.\n3. **Pick the action for the member.** Instead of one offer for everyone in a segment, the\n   system chooses among many possible offers, rewards or messages the one most likely to matter\n   to that specific member, within the programme's point and budget rules.\n4. **Deliver it where the member already is.** The action reaches the member through the app, a\n   push notification or email.\n5. **Test, learn and stay inside the programme's promise.** Results feed back into the model, and\n   the programme keeps its published rules (how points are earned, what a tier guarantees) intact,\n   so personalization only shapes which message a member receives.",[41,42,43],"customer-experience","revenue-growth","cost-to-serve",[45,46,47,48],"churn-reduction","conversion-rate-uplift","revenue-uplift","customer-satisfaction",{"referenceOrg":50,"inputs":51,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A retail or restaurant loyalty programme with 5 million active members",[52,58,65,72],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"activeMembers","Active members",5000000,"members","The reference programme.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"avgAnnualSpendPerMember","Average annual spend per active member",200,500,"USD per member per year","Editorial assumption, replace with your own average member spend.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"atRiskShare","Share of members at risk of lapsing in a given year",0.1,0.2,"fraction of active members","Editorial assumption, replace with your own lapse rate.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"retentionLift","Additional share of at risk members retained with personalization",0.03,0.04,"fraction of at risk members","Braze reports a 5% retention lift among at risk guests for Panera Bread from an AI powered, Braze delivered campaign, but the source does not say whether that is percentage points or a relative change. The range is set below that figure, with a high end of 0.04, because it covers one campaign around a single menu change, not a full year of personalization, and it is Braze's own reported number, not an independently measured result.","activeMembers * atRiskShare * retentionLift * avgAnnualSpendPerMember","USD","per year","Member spend retained per year","Gross retained spend only. It leaves out the cost of the platform and of any reward or discount given to retain a member, the share of members who would have stayed without any intervention (only a control group removes that), and any effect on members who are not flagged as at risk.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":92},"medium","The personalization logic is well understood. The member data takes more work: unifying profiles across brands, channels and sometimes acquired loyalty programmes, while keeping the published programme rules (how points are earned and what a tier guarantees) completely consistent for every member regardless of what the AI recommends.",[89,90,91],"A unified member profile across every brand, channel and touchpoint the programme spans","Programme rules and the current reward or redemption catalogue, kept in sync with what members were promised","At least a year of member transaction and engagement history with lapse outcomes",[93,94,95,96],"Loyalty and rewards management platform (points, tiers, redemption)","Customer data platform or CRM holding the unified member profile","Email, push notification and in app messaging systems","SMS and WhatsApp messaging, where members have opted in",{"steps":98,"guardrails":114,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":125},[99,102,105,108,111],{"title":100,"detail":101},"Unify the profile before personalizing anything","A single view of the member, across every brand and channel the programme spans, has to exist before any model can decide what to say to them.",{"title":103,"detail":104},"Separate the programme's rules from the personalization layer","Points earning, tier thresholds and what a reward is worth must stay exactly what was published to members; the AI decides what to communicate and when, and the programme rules decide what a member is entitled to.",{"title":106,"detail":107},"Score lapse risk and reward readiness, not just RFM segments","Build models that flag members close to lapsing and members close to a reward they would value, so the same member can get a different message next month as their behaviour changes.",{"title":109,"detail":110},"Pilot on one moment that matters","Panera Bread built its personalization around one concrete event, a menu transformation that risked confusing or losing guests, rather than personalizing everything on day one.",{"title":112,"detail":113},"Keep a holdout and measure against it","Compare personalized members against a matched group that gets the standard treatment, so a real lift can be told apart from members who would have stayed or redeemed anyway.",[115,116,117,118],"Programme rules (points, tiers, redemption value) enforced identically for every member, independent of any AI recommendation","A cap on how many personalized offers or messages a member receives in a period","Marketing consent and channel preferences enforced before any personalized outreach","A member can always see and reach a human about their actual points balance and tier status","A loyalty or CRM team owns the reward catalogue, the message cadence caps and the model's performance, reviewing lapse and redemption results against a holdout regularly. Member service handles any dispute about points, tier status or a reward, since those are programme commitments the AI does not have authority to change.",[121,122,123,124],"Lapse rate among at risk members reached with personalization versus a holdout","Reward redemption rate versus a holdout","Member complaints about incorrect points, tier status or reward value","Message frequency per member, to catch fatigue before it shows up in lapse",[126,129,132,135],{"title":127,"detail":128},"Personalization contradicts the programme's own rules","A message implies a reward or tier benefit the programme does not actually grant. Generate messages from the same rules engine that governs the programme, never from a separate, driftable copy of it.",{"title":130,"detail":131},"Over messaging the members most worth keeping","The best, most engaged members get the most personalized attention and end up fatigued by it. Cap frequency per member and monitor unsubscribes and complaints by engagement tier, not only by segment.",{"title":133,"detail":134},"No holdout, no real lift","Comparing treated members to their own past behaviour overstates the effect, since overall demand and seasonality move too. Keep a matched holdout group for as long as the programme runs.",{"title":136,"detail":137},"A single lapsed brand poisons the unified profile","When a loyalty programme spans several brands, a bad signal from one (a returns spike, a service outage) can misclassify a member's risk everywhere. Validate signals per brand before they feed a cross brand profile.",{"euAiAct":139,"regulations":142,"guidance":147,"controls":154,"incidents":159},{"tier":140,"basis":141},"context-dependent","Deciding a loyalty offer or reward for a customer is not listed in Annex III and does not itself evaluate creditworthiness, employment or access to an essential service, so a back office personalization engine is minimal risk. If a conversational channel is added to talk to members directly, Article 50 would apply and members must be told they are dealing with AI. Personalization that deploys manipulative or deceptive techniques, or exploits a member's vulnerabilities due to age, disability or a specific social or economic situation, in a way that causes or is reasonably likely to cause significant harm, is prohibited under Article 5(1)(a) and (b).",[143,144,145,146],"eu-ai-act","gdpr","uk-gdpr","us-tcpa",[148],{"title":149,"issuer":150,"region":151,"url":152,"note":153},"Article 5: Prohibited AI practices","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Farticle\u002F5\u002F","Bans manipulative techniques and the exploitation of vulnerabilities, including those due to a person's age, disability, social or economic situation.",[155,156,157,158],"Programme rules (points, tiers, redemption value) served from one authoritative source, never duplicated into the personalization layer","Message frequency caps enforced per member across every channel and brand","Consent and channel preference checks before any personalized outreach","Regular review of personalization outcomes by member segment, to catch fatigue or unequal treatment early",[],{"howToBuild":161},"On Blits.ai the lapse and reward scoring typically comes from the loyalty platform's own\nmodels; Blits.ai turns those scores into the personalized outreach. An **agentic workflow**,\nrun on a schedule or triggered by an event such as a lapse risk score crossing a threshold,\ndraws on a **knowledge base** holding the current programme rules and reward catalogue to help\nprevent a message from stating a benefit the programme does not actually offer, then prepares\nthe message and hands it to a **custom function** that reaches the member's preferred\n**channel**: **WhatsApp**, **email**, **SMS** or the brand's own app through the **API\nchannel**, with **human in the loop approval** for any outreach above a configurable\nthreshold.\n\n**Guardrails** help prevent the workflow from promising anything outside the programme rules, **human\nhandover** passes any points or tier dispute a message triggers to a member service team, and\n**test suites** check the generated messages against the programme rules on every change.\n**Analytics** dashboards and run history show how often each message is sent and how members\nrespond; redemption and lapse outcomes stay measured in the loyalty platform itself. The\nplatform is model agnostic with EU and UAE data residency.",[163,166,169],{"question":164,"answer":165},"How is this different from marketing personalization at scale and from an offers and rewards agent?","It is scoped to a membership programme's own members and its published rules: points, tiers and a redemption catalogue that promise something specific to each member. A general marketing personalization engine picks the next offer or message for any customer across any campaign. This use case personalizes what a loyalty member sees inside their programme and leaves what a member has earned to the programme rules. The offers and rewards agent covers the same kind of job for a bank or card issuer's own rewards programme; this page covers retail, restaurant and travel membership programmes instead.",{"question":167,"answer":168},"What results has AI loyalty personalization actually shown?","Braze reports that Panera Bread achieved a 5% retention lift among at risk guests and a doubling of loyalty offer redemptions from an AI powered, Braze delivered campaign around a menu change. Starbucks Chief Executive Officer Laxman Narasimhan told investors that Starbucks had activated new capabilities in its Deep Brew data analytics and AI tool to identify and incentivize specific Rewards member cohorts, as one of several initiatives he said were already having a positive impact on occasional customers. IHG Hotels & Resorts has announced plans to standardize its guest CRM on Salesforce's platform and grow IHG One Rewards membership with predictive AI, but this is a stated intent, not yet a published result.",{"question":170,"answer":171},"Does AI loyalty personalization need to be disclosed to members under the EU AI Act?","Choosing an offer is minimal risk, so no disclosure is required for that alone. Article 50 requires telling a person they are interacting with AI, unless that is obvious from the context, where a conversational channel talks to members directly. Article 5 bans manipulative techniques and the exploitation of vulnerabilities in any personalization.",[173,174,175],"personalized-marketing-at-scale","offers-and-rewards-agent","churn-prediction-and-retention-offers","2026-10-13","2026-09-29",[179],{"date":176,"note":180},"First published","loyalty-rewards-personalization-agent",[183,211,233],{"title":184,"useCases":185,"organization":186,"vendors":192,"summary":193,"stage":194,"year":38,"channels":195,"languages":196,"metrics":198,"outcomeDisclosed":199,"sources":200,"verification":206,"grade":208,"id":209,"organizationSlug":210},"Starbucks: Deep Brew AI personalizes Rewards member offers",[181],{"name":187,"anonymized":188,"country":189,"region":190,"industry":17,"wikidata":191},"Starbucks",false,"US","north-america","Q37158",[],"Starbucks Chief Executive Officer Laxman Narasimhan told investors that Starbucks activated new capabilities within its proprietary Deep Brew data analytics and AI tool to identify and incentivize specific Starbucks Rewards member cohorts, part of a set of initiatives aimed at bringing occasional customers back into the loyalty programme. Narasimhan said the company had already seen a positive impact from these initiatives, with occasional customers beginning to rebound in December 2023. US 90 day active Starbucks Rewards members grew 13% year over year to a record 34.3 million in the quarter.","production",[],[197],"en",[],true,[201],{"url":202,"title":203,"publisher":204,"date":205},"https:\u002F\u002Fs203.q4cdn.com\u002F326826266\u002Ffiles\u002Fdoc_financials\u002F2024\u002Fq1\u002FSBUX-Q1-2024-Earnings-Call.pdf","Starbucks Corporation Q1 Fiscal 2024 Earnings Conference Call, Corrected Transcript","Starbucks Corporation","2024-01-30",{"level":207,"checkedAt":177},"source-verified","B","starbucks-deep-brew-rewards-personalization",null,{"title":212,"useCases":213,"organization":214,"vendors":218,"summary":222,"stage":223,"year":38,"channels":224,"languages":225,"metrics":226,"outcomeDisclosed":188,"sources":227,"verification":230,"grade":231,"id":232,"organizationSlug":210},"IHG Hotels & Resorts: an expanded Salesforce relationship for loyalty personalization",[181],{"name":215,"anonymized":188,"country":216,"region":151,"industry":18,"wikidata":217},"IHG Hotels & Resorts","GB","Q1424962",[219],{"name":220,"role":221},"Salesforce","platform","IHG Hotels & Resorts announced an expanded relationship with Salesforce to standardize its guest CRM on the Einstein 1 Platform (since renamed the Salesforce Platform) and adopt Salesforce Loyalty Management, which Salesforce says will make IHG \"the first global hospitality company to standardize on Salesforce Loyalty Management.\" Salesforce describes IHG as being able to grow its IHG One Rewards membership \"with predictive AI capabilities\" once the rollout is in place, and names generative AI for guest stay management as a further step it is laying the foundation for. The one guest facing capability the announcement describes as already running is plain segmentation, not AI: when a guest starts but does not complete a booking, a segment is created in Data Cloud and sent to Marketing Cloud to prompt a return visit.","announced",[27,28],[197],[],[228],{"url":37,"title":36,"publisher":220,"date":229},"2024-04-22",{"level":207,"checkedAt":177},"C","ihg-hotels-resorts-salesforce-loyalty-personalization",{"title":234,"useCases":235,"organization":236,"vendors":239,"summary":242,"stage":194,"year":38,"channels":243,"languages":244,"metrics":245,"outcomeDisclosed":199,"sources":254,"verification":257,"grade":231,"id":258,"organizationSlug":210},"Panera Bread: AI powered loyalty personalization for a menu transformation",[181],{"name":237,"anonymized":188,"country":189,"region":190,"industry":17,"wikidata":238},"Panera Bread","Q7130852",[240],{"name":241,"role":221},"Braze","For its largest menu transformation, Panera Bread combined its own AI powered decision engine with the Braze customer engagement platform to personalize MyPanera loyalty communications during the change. AI decisioning segmented guests likely to lapse because of the menu changes; Panera then created over 4,000 unique combinations of personalized offers and product recommendations delivered across email, app and web, and used Braze's Custom Events to trigger reactivation messages to guests who browsed new items but did not buy. Braze reports the campaign achieved a 5% retention lift among at risk guests, a doubling of loyalty offer redemptions, and a doubling of purchase conversion rates from abandoned order follow ups.",[27,28],[197],[246],{"kpi":46,"value":247,"unit":248,"qualifier":249,"period":250,"claimant":251,"quote":252,"sourceUrl":253},2,"multiplier","exact","April 2024 menu transformation campaign, abandoned order follow ups","vendor","By leveraging Braze, Panera achieved a 5% retention lift among at-risk guests, 2X loyalty offer redemptions, and 2X purchase conversion rates from abandoned order follow-ups.","https:\u002F\u002Fwww.braze.com\u002Fcustomers\u002Fpanera-case-study",[255],{"url":253,"title":256,"publisher":241},"How Panera Bread Drove Retention with Braze",{"level":207,"checkedAt":177},"panera-bread-braze-menu-transformation-retention",0,[261],{"kpi":46,"label":262,"unit":248,"aggregate":199,"higherIsBetter":199,"n":263,"nUpTo":259,"median":247,"min":247,"max":247,"byClaimant":264,"vendorOnly":199,"points":265},"Conversion uplift",1,{"organization":259,"vendor":263,"regulator":259,"independent":259},[266],{"evidenceId":258,"organization":237,"value":247,"qualifier":249,"claimant":251,"grade":231,"pooled":199},{"low":268,"high":269},3000000,20000000,[271,302,318,338],{"slug":173,"title":272,"shortTitle":273,"definition":274,"status":9,"industries":275,"functions":278,"patterns":280,"audience":282,"autonomy":283,"adoptionStage":284,"evidenceCount":285,"publicEvidenceCount":286,"organizations":287,"bestGrade":208,"headline":297,"lastVerified":301,"indexable":199},"AI marketing personalization at scale","Marketing personalization at scale","AI marketing personalization at scale is AI that decides, for each customer, which product, offer, message or content to show next, for a marketing team running many campaigns. It works across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the team's engine across many campaigns and channels, not an agent that converses with the customer.",[19,18,276,17,277],"media-and-entertainment","banking",[21,279],"sales",[24,25,281],"content-generation","back-office","supervised-agent","mainstream",10,9,[288,289,290,291,292,293,294,295,296],"Amazon","Catchtable","Commonwealth Bank of Australia","Natura","Radisson Hotel Group","Square Enix","Swarovski","Techcombank","Virgin Voyages",{"kpi":46,"label":262,"unit":298,"n":263,"nUpTo":259,"kind":299,"value":300,"qualifier":249,"claimant":251,"organization":289,"vendorReported":199},"percent","reported",30,"2026-09-27",{"slug":174,"title":303,"shortTitle":304,"definition":305,"status":9,"industries":306,"functions":308,"patterns":309,"audience":29,"autonomy":311,"adoptionStage":284,"segment":312,"evidenceCount":313,"publicEvidenceCount":314,"organizations":315,"bestGrade":208,"headline":210,"lastVerified":301,"indexable":199},"AI agent for personalized offers and rewards","Offers and rewards","AI offers and rewards is a customer facing agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment. It draws on the customer's transactions and context, delivers the offer in the app, in messaging or through a colleague, helps the customer understand, track and redeem rewards in conversation, and works inside their own account and loyalty relationship, unlike campaign personalization.",[277,307],"payments",[21,279,22],[24,25,310],"conversational-agent","autonomous","front-office",8,3,[316,290,317],"Bank of America","DBS Bank",{"slug":175,"title":319,"shortTitle":320,"definition":321,"status":9,"industries":322,"functions":324,"patterns":326,"audience":282,"autonomy":283,"adoptionStage":284,"segment":327,"evidenceCount":328,"publicEvidenceCount":328,"organizations":329,"bestGrade":208,"headline":335,"lastVerified":301,"indexable":199},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI churn prediction and retention is AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals. It explains the likely reason and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[323],"telecommunications",[21,22,279,325],"analytics-and-reporting",[25,24,310],"middle-office",5,[330,331,332,333,334],"BT Business","Etisalat","Telenet","Virgin Media O2","Vodafone UK",{"kpi":45,"label":336,"unit":298,"n":247,"nUpTo":259,"kind":299,"value":337,"qualifier":249,"claimant":251,"organization":332,"vendorReported":199},"Churn reduction",20,{"slug":339,"title":340,"shortTitle":341,"definition":342,"status":9,"industries":343,"functions":344,"patterns":345,"audience":29,"autonomy":311,"adoptionStage":348,"evidenceCount":349,"publicEvidenceCount":286,"organizations":350,"bestGrade":208,"headline":359,"lastVerified":301,"indexable":199},"conversational-shopping-assistant","AI shopping assistant for product discovery and recommendations","Conversational shopping assistant","An AI conversational shopping assistant answers product questions and recommends items from a retailer's own catalog, for a need, project or occasion a shopper describes. It works on the retailer's site or app, compares items grounded in the retailer's product data, reviews and stock, and hands the shopper to a basket, a store or a human expert.",[19,17],[279,21,22],[310,24,346,347],"rag-knowledge-assistant","agentic-workflow","early-adopters",14,[288,351,352,353,354,355,356,357,358],"Carrefour","The Home Depot","Lowe's","Omoda","Pick n Pay","Sun & Ski Sports","Walmart","Zalando",{"kpi":46,"label":262,"unit":248,"n":247,"nUpTo":259,"kind":299,"value":314,"qualifier":249,"claimant":251,"organization":356,"vendorReported":199},{"indexable":199,"reasons":361},[],[363,368,373,381,388,394,400,407,415,422,429,435,441,448,455,462,467,472,479,484,489,496,502,509,514,519,524,529,535,541,548,554,560,566,572,577],{"id":143,"label":364,"issuer":150,"region":151,"url":365,"description":366,"useCases":367,"indexable":199},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":144,"label":369,"issuer":150,"region":151,"url":370,"description":371,"useCases":372,"indexable":199},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":374,"label":375,"issuer":376,"region":377,"url":378,"description":379,"useCases":380,"indexable":199},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":382,"label":383,"issuer":384,"region":190,"url":385,"description":386,"useCases":387,"indexable":199},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":145,"label":389,"issuer":390,"region":151,"url":391,"description":392,"useCases":393,"indexable":199},"UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":395,"label":396,"issuer":150,"region":151,"url":397,"description":398,"useCases":399,"indexable":199},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":401,"label":402,"issuer":403,"region":151,"url":404,"description":405,"useCases":406,"indexable":199},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":408,"label":409,"issuer":410,"region":411,"url":412,"description":413,"useCases":414,"indexable":199},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":416,"label":417,"issuer":418,"region":411,"url":419,"description":420,"useCases":421,"indexable":199},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":423,"label":424,"issuer":425,"region":190,"url":426,"description":427,"useCases":428,"indexable":199},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":430,"label":431,"issuer":150,"region":151,"url":432,"description":433,"useCases":434,"indexable":199},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":436,"label":437,"issuer":438,"region":377,"url":439,"description":440,"useCases":434,"indexable":199},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":442,"label":443,"issuer":444,"region":151,"url":445,"description":446,"useCases":447,"indexable":199},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":449,"label":450,"issuer":451,"region":190,"url":452,"description":453,"useCases":454,"indexable":199},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":456,"label":457,"issuer":458,"region":377,"url":459,"description":460,"useCases":461,"indexable":199},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":463,"label":464,"issuer":150,"region":151,"url":465,"description":466,"useCases":349,"indexable":199},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",{"id":146,"label":468,"issuer":469,"region":190,"url":470,"description":471,"useCases":349,"indexable":199},"Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":475,"region":190,"url":476,"description":477,"useCases":478,"indexable":199},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":480,"label":481,"issuer":150,"region":151,"url":482,"description":483,"useCases":478,"indexable":199},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":485,"label":486,"issuer":150,"region":151,"url":487,"description":488,"useCases":478,"indexable":199},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":490,"label":491,"issuer":492,"region":190,"url":493,"description":494,"useCases":495,"indexable":199},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":497,"label":498,"issuer":499,"region":377,"url":500,"description":501,"useCases":495,"indexable":199},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":503,"label":504,"issuer":505,"region":151,"url":506,"description":507,"useCases":508,"indexable":199},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":510,"label":511,"issuer":150,"region":151,"url":512,"description":513,"useCases":508,"indexable":199},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":515,"label":516,"issuer":150,"region":151,"url":517,"description":518,"useCases":508,"indexable":199},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":520,"label":521,"issuer":410,"region":411,"url":522,"description":523,"useCases":285,"indexable":199},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":525,"label":526,"issuer":150,"region":151,"url":527,"description":528,"useCases":285,"indexable":199},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":530,"label":531,"issuer":532,"region":190,"url":533,"description":534,"useCases":313,"indexable":199},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":536,"label":537,"issuer":150,"region":151,"url":538,"description":539,"useCases":540,"indexable":199},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":542,"label":543,"issuer":544,"region":545,"url":546,"description":547,"useCases":328,"indexable":199},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":549,"label":550,"issuer":150,"region":151,"url":551,"description":552,"useCases":553,"indexable":199},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":555,"label":556,"issuer":557,"region":151,"url":558,"description":559,"useCases":553,"indexable":199},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":561,"label":562,"issuer":563,"region":151,"url":564,"description":565,"useCases":553,"indexable":199},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":567,"label":568,"issuer":569,"region":411,"url":570,"description":571,"useCases":314,"indexable":199},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":573,"label":574,"issuer":150,"region":151,"url":575,"description":576,"useCases":314,"indexable":199},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":578,"label":579,"issuer":580,"region":190,"url":581,"description":582,"useCases":314,"indexable":199},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904360039]