[{"data":1,"prerenderedAt":560},["ShallowReactive",2],{"uc-dealer-aftersales-retention-agent":3,"uc-regulations":340},{"useCase":4,"evidence":175,"blitsAiDeployments":242,"benchmarks":243,"indicative":258,"related":261,"indexability":338,"includeUnpublished":181},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":44,"macroEstimates":79,"feasibility":80,"implementation":93,"risk":127,"blitsAi":153,"faq":155,"related":168,"datePublished":170,"dateModified":170,"lastVerified":170,"changelog":171,"slug":174},"AI agent for dealer service retention and aftersales outreach","Dealer service retention agent","AI service retention for auto dealerships","AI mines service records and books visits. Impel reports $1.1M revenue at Fred Anderson Toyota in six months, $110,000 influenced at Murfreesboro Nissan in two.","published","An AI agent that continuously mines a dealer's own service records for each vehicle's factory intervals, declined work, open recalls and lapsed visits, reaches the owner by text or email at the right moment, answers what is due, and books the appointment in the same conversation, so a lean service team keeps more of the visits it would otherwise lose to time or a competitor.",[12,13,14,15],"service retention AI","aftersales outreach agent","recall outreach AI","fixed operations AI",[17],"automotive",[19,20],"customer-service","operations",[22,23,24],"conversational-agent","agentic-workflow","prediction-and-scoring",[26,27],"sms","email","customer-facing","supervised-agent","early-adopters","aftersales","Service departments carry tens of thousands of vehicle records, and a factory interval, a\ndeclined repair or a recall is due somewhere in that population every day. Fred Anderson Toyota\ndescribed the position many dealers are in: \"we have a very lean operation and our staff is busy\nrunning the service drive effectively\", with capacity in the bay but no consistent way to reach\ncustomers about outreach, missed appointment follow up and service reminders, without adding\nheadcount (Impel).\n\nLeft alone, that gap can become lost revenue. Many customers who missed a service or declined a\nrepair do not call back on their own. Fred Anderson Toyota found its one way marketing tools to\nthe whole list expensive and ineffective (Impel), since a blast is not tied to what a specific\nvehicle actually needs right now.",[],"1. **Mine the service history.** The AI reads the dealer management system continuously for\n   each vehicle's purchase date, service history, factory intervals, declined work and open\n   recalls.\n2. **Pick the moment and the message.** It targets each customer at a defined lifecycle moment,\n   first service, next service due, a declined repair, a missed interval or a recall, with a\n   message specific to their vehicle rather than a generic blast.\n3. **Reach out by text or email.** Outreach goes out on the channel and consent the customer has\n   given, at a pace the service team sets.\n4. **Hold the conversation.** When the customer replies, the agent answers what is due and why,\n   using the dealer's own service menu and recall wording, not only a link to an online\n   scheduler.\n5. **Book directly.** It checks real availability and books the appointment inside the\n   conversation, confirming the slot back to the customer.\n6. **Hand over what it should not answer.** Price disputes, warranty coverage questions and\n   complaints go to a service advisor with the conversation attached.",[36,37,38],"revenue-growth","customer-experience","employee-productivity",[40,41,42,43],"revenue-recovered","users-served","interactions-handled","hours-saved",{"referenceOrg":45,"inputs":46,"formula":74,"currency":75,"period":76,"resultLabel":77,"caveat":78},"A dealership group with 10 stores and 40,000 vehicles in its service population",[47,53,60,67],{"key":48,"label":49,"low":50,"high":50,"unit":51,"note":52},"vehiclesInPopulation","Vehicles in the service population",40000,"vehicles","The reference dealer group.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"engagedShare","Share of the population the AI reaches and engages in a year",0.4,0.6,"fraction of vehicles","Editorial assumption, replace with your own; the evidence does not give a service population size to check this share against (Fred Anderson Toyota's more than 23,000 customers engaged in six months has no stated population denominator).",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"bookedShare","Share of engaged customers who book an appointment",0.03,0.06,"fraction of engaged customers","At or below Fred Anderson Toyota's reported 1,500 appointments from more than 23,000 customers engaged in six months, about 6.5 percent (Impel).",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"avgRoValue","Average repair order value from a retained visit",400,700,"USD per repair order","At or below Fred Anderson Toyota's reported more than 1.1 million US dollars in service revenue generated across six months, against 1,500 appointments facilitated in the same period, about 733 US dollars each if every dollar came from those appointments, which the source does not say (Impel).","vehiclesInPopulation * engagedShare * bookedShare * avgRoValue","USD","per year","Annual incremental repair order revenue from retained visits","Counts incremental repair order revenue only, before the platform's own cost and the parts and labour cost already inside that revenue. It assumes the booked visits would not have happened without the outreach, which is optimistic for some share of them, since some customers would have called on their own.",[],{"complexity":81,"complexityNote":82,"dataPrerequisites":83,"integrations":88},"medium","Holding the conversation and booking is the easier part once the dealer management system connects. The harder part is a clean, vehicle specific service history so the AI knows what is genuinely due, and a live scheduler integration so it books a real slot instead of sending a link to a portal.",[84,85,86,87],"Vehicle purchase and service history by vehicle in the dealer management system","Factory service intervals and open recalls by model and vehicle","A defined set of outreach moments, first service, next service, declined work, lapsed visit, recall","Consent records for text, email and any other channel used for outreach",[89,90,91,92],"Dealer management system for vehicle, customer and service history","Online service scheduler for direct booking","SMS and email delivery channels","Recall data from the manufacturer, where the dealer group receives it directly",{"steps":94,"guardrails":110,"humanInTheLoop":114,"kpisToInstrument":115,"failureModes":120},[95,98,101,104,107],{"title":96,"detail":97},"Start with the outreach moments that pay back fastest","Declined repairs and lapsed customers are usually the highest value first moments, because the work was already identified once; launch there before covering every lifecycle moment.",{"title":99,"detail":100},"Get vehicle specific history clean before scaling outreach","Confirm factory intervals and service history are accurate per vehicle before sending outreach at volume, since a wrong interval undermines trust in every later message.",{"title":102,"detail":103},"Let it book, not only remind","Connect the agent to live scheduler availability so it can confirm a real slot in the conversation, which tends to convert better than a link to a booking page.",{"title":105,"detail":106},"Route anything sensitive to a person","Write down which topics, price disputes, warranty coverage, complaints, always go to a service advisor, and pass the full conversation so the customer does not repeat themselves.",{"title":108,"detail":109},"Track retained visits, not only messages sent","Measure appointments and revenue that came from the outreach, and read a sample of conversations each week to catch replies the agent handled badly.",[111,112,113],"Outreach follows the consent the customer gave for each channel, and stops the moment they opt out","Recall outreach uses the manufacturer's own wording and never diagnoses or promises a fix outside the recall's stated scope","Any pricing, warranty coverage or complaint question outside the written script goes to a service advisor","Service advisors handle anything the agent flags outside its script: price disputes, warranty coverage questions and any customer who asks for a person. Advisors also review a sample of booked appointments each week to check the right work was set up for the visit.",[116,117,118,119],"Share of the service population engaged and share who book, by outreach moment","Repair order revenue and count from visits the outreach generated","Labour hours the service team no longer spends on manual outreach","Opt out and complaint rate from the outreach itself",[121,124],{"title":122,"detail":123},"Recall outreach that reads like marketing","A recall message that sounds like a sales pitch gets ignored or distrusted. Use the manufacturer's own recall language and keep its tone distinct from promotional outreach.",{"title":125,"detail":126},"Booking without checking real capacity","An agent that books appointments the shop cannot actually keep creates the exact frustration it was meant to remove. Connect it to live scheduler availability, not a static calendar.",{"euAiAct":128,"regulations":131,"guidance":135,"controls":148,"incidents":152},{"tier":129,"basis":130},"limited","Article 50(1): customers must be told they are dealing with an AI system, unless that is obvious from the context, when it holds a conversation and books on the dealer's behalf. This is not an Annex III use: it does not decide credit, employment or access to an essential service, so it stays at the transparency tier as long as it keeps to outreach and booking.",[132,133,134],"eu-ai-act","gdpr","us-tcpa",[136,142],{"title":137,"issuer":138,"region":139,"url":140,"note":141},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":143,"issuer":144,"region":145,"url":146,"note":147},"Stop unwanted robocalls and texts","Federal Communications Commission","north-america","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated calls and texts under the Telephone Consumer Protection Act; the FCC has confirmed that AI generated voices count as artificial voices under the same rules.",[149,150,151],"AI disclosure at the start of any conversation that is not obviously automated","Documented consent basis and an easy opt out for every channel used","Inventory entry for the outreach agent with an owner and the outreach moments it covers",[],{"howToBuild":154},"On Blits.ai the outreach itself runs as an agentic workflow: a scheduled task reads the dealer\nmanagement system through a custom function each day, checks each vehicle against factory\nintervals, open recalls and declined work, and starts a conversation for the ones due. Email\ngoes out on the outbound email channel; the first SMS is sent through a custom function that\ncalls the messaging provider, and replies land on the SMS channel. A knowledge base holds the\ndealer's own service menu and recall wording, so the agent answers what is due and why from\napproved content instead of improvising.\n\nAn AI agent holds the reply conversation, clarifies what the customer needs, and, through a\ncustom function, checks and books a slot in the scheduler directly, confirming the appointment\nin the same thread. Guardrails keep the agent to the outreach moments and service topics it is\nscoped for, and human handover passes the full conversation to a service advisor for anything\nabout price, warranty coverage or a complaint, so the customer does not repeat themselves. Test\nsuites replay the recall and service due conversations before each change goes live, monitors\nrun scheduled health checks against the outreach agent, and workflow run history with\nanalytics shows how the daily check and each conversation performed. The platform is model\nagnostic, so the dealer group can choose the model per agent.",[156,159,162,165],{"question":157,"answer":158},"Is this the same as a reminder for an appointment already booked?","No. A reminder and confirmation agent contacts a customer about something already on the calendar. This agent decides, from the vehicle's own service history, that something is due in the first place, whether the customer has booked anything or not, and then tries to get it booked.",{"question":160,"answer":161},"Does it replace the service advisor?","No, in the deployments on this page it frees advisor time rather than replacing anyone. Impel reports over 1,000 labour hours freed up at Murfreesboro Nissan so service staff could focus on customers already in the bay. This page's own design keeps advisors handling disputes, warranty questions and anything outside the written script.",{"question":163,"answer":164},"What happens with a vehicle recall?","The agent can include open recalls in its outreach using the manufacturer's own wording, but it does not diagnose the fault or promise a repair beyond what the recall covers; anything outside that scope goes to a service advisor.",{"question":166,"answer":167},"How do you know a booked visit is retention and not one that would have happened anyway?","You cannot know for certain for any single visit. Track the appointment and revenue trend for the outreach segment against a comparable group that did not receive it where possible, and treat the indicative value on this page as an upper estimate rather than a guarantee.",[169],"outbound-reminder-and-confirmation-agent","2026-09-29",[172],{"date":170,"note":173},"First published","dealer-aftersales-retention-agent",[176,216],{"title":177,"useCases":178,"organization":179,"vendors":183,"summary":187,"stage":188,"year":189,"channels":190,"languages":191,"metrics":193,"outcomeDisclosed":206,"sources":207,"verification":211,"grade":213,"id":214,"organizationSlug":215},"Fred Anderson Toyota: AI service outreach grows repair order revenue without new hires",[174],{"name":180,"anonymized":181,"country":182,"region":145,"industry":17},"Fred Anderson Toyota",false,"US",[184],{"name":185,"role":186},"Impel","platform","Fred Anderson Toyota, a family owned Toyota dealership in North Carolina, deployed Impel Service AI to mine vehicle purchase and service records in its dealer management system and reach customers at the right point in the ownership lifecycle, without adding headcount to its service team. The AI brings back abandoned customers, follows up on missed appointments with reminders, and helped the dealership increase show rates, facilitating over 1,500 appointments in six months, so the existing team can stay focused on customers already in the bay.","production",2026,[26,27],[192],"en",[194,202],{"kpi":40,"value":195,"unit":196,"currency":75,"qualifier":197,"period":198,"claimant":199,"quote":200,"sourceUrl":201},1100000,"currency","at-least","after six months","vendor","$1.1M service revenue generated in 6 months","https://impel.ai/wp-content/uploads/2026/09/CaseStudy_FredAndToyota.pdf",{"kpi":41,"value":203,"unit":204,"qualifier":197,"period":198,"claimant":199,"quote":205,"sourceUrl":201},23000,"count","Service AI reached out to more than 23,000 customers",true,[208],{"url":201,"title":209,"publisher":185,"date":210},"Impel Service AI Drives Fixed Ops Revenue and Customer Retention","2026-09-01",{"level":212,"checkedAt":170},"source-verified","C","fred-anderson-toyota-service-retention-ai",null,{"title":217,"useCases":218,"organization":219,"vendors":221,"summary":223,"stage":188,"year":189,"channels":224,"languages":225,"metrics":226,"outcomeDisclosed":206,"sources":233,"verification":240,"grade":213,"id":241,"organizationSlug":215},"Murfreesboro Nissan: AI service retention outreach drives incremental repair order revenue",[174],{"name":220,"anonymized":181,"country":182,"region":145,"industry":17},"Murfreesboro Nissan",[222],{"name":185,"role":186},"Murfreesboro Nissan, a Nissan dealership in Tennessee, deployed Impel Service AI to mine its dealer management system for VIN specific service intervals, declined services and recalls, so a service team stretched thin could automate 22 outreach initiatives by email, SMS and direct mail without adding headcount. When a customer replies, the AI holds the conversation, clarifies what service is due and books the appointment directly, instead of only sending a link to an online scheduler.",[26,27],[192],[227],{"kpi":40,"value":228,"unit":196,"currency":75,"qualifier":229,"period":230,"claimant":199,"quote":231,"sourceUrl":232},110000,"approximately","in two months","Impel Service AI helped the team drive $110K in incremental RO revenue in just two months.","https://impel.ai/case-study/",[234,238],{"url":235,"title":236,"publisher":185,"date":237},"https://impel.ai/wp-content/uploads/2026/04/Murfreesboro_Nissan_CaseStudy.pdf","From Overwhelmed to Unstoppable: How Murfreesboro Nissan Turned AI Into Six Figures of Service Revenue","2026-04-01",{"url":232,"title":239,"publisher":185,"date":170},"Case Studies - Impel AI",{"level":212,"checkedAt":170},"murfreesboro-nissan-service-retention-ai",0,[244,252],{"kpi":40,"label":245,"unit":196,"currency":75,"aggregate":181,"higherIsBetter":206,"n":246,"nUpTo":242,"median":247,"min":228,"max":195,"byClaimant":248,"vendorOnly":206,"points":249},"Revenue recovered",2,605000,{"organization":242,"vendor":246,"regulator":242,"independent":242},[250,251],{"evidenceId":214,"organization":180,"value":195,"qualifier":197,"claimant":199,"grade":213,"pooled":206},{"evidenceId":241,"organization":220,"value":228,"qualifier":229,"claimant":199,"grade":213,"pooled":206},{"kpi":41,"label":253,"unit":204,"aggregate":181,"higherIsBetter":206,"n":254,"nUpTo":242,"median":203,"min":203,"max":203,"byClaimant":255,"vendorOnly":206,"points":256},"Users served",1,{"organization":242,"vendor":254,"regulator":242,"independent":242},[257],{"evidenceId":214,"organization":180,"value":203,"qualifier":197,"claimant":199,"grade":213,"pooled":206},{"low":259,"high":260},192000,1008000,[262,282,304,323],{"slug":169,"title":263,"shortTitle":264,"definition":265,"status":9,"industries":266,"functions":270,"patterns":271,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":273,"publicEvidenceCount":274,"organizations":275,"bestGrade":280,"headline":215,"lastVerified":281,"indexable":206},"AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[267,268,269],"cross-industry","healthcare","government",[19,20],[272,22,23,24],"voice-agent",5,4,[276,277,278,279],"Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","U.S. Department of Veterans Affairs","WellSpan Health","B","2026-09-26",{"slug":283,"title":284,"shortTitle":285,"definition":286,"status":9,"industries":287,"functions":288,"patterns":289,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":290,"publicEvidenceCount":291,"organizations":292,"bestGrade":280,"headline":296,"lastVerified":303,"indexable":206},"patient-appointment-scheduling-and-reminders-agent","AI agent for patient appointment scheduling, reminders and no show reduction","Patient scheduling and reminders","An AI agent that books, moves and cancels patient appointments by phone and messaging while following the provider's scheduling rules (referral, triage level, clinician and visit type, preparation), confirms and reminds patients in two way conversations, predicts who is likely to miss an appointment, and offers freed slots to patients on the waiting list. Unlike a general branch and appointment booking agent, it writes into the electronic health record and must respect clinical constraints, so anything clinical goes to staff.",[268],[19,20],[272,22,24,23],9,6,[293,294,295,276,277,279],"Audibel","Howard Brown Health","Mid and South Essex NHS Foundation Trust",{"kpi":297,"label":298,"unit":299,"n":254,"nUpTo":242,"kind":300,"value":301,"qualifier":302,"claimant":199,"organization":294,"vendorReported":206},"containment-rate","Containment rate","percent","reported",30,"exact","2026-09-27",{"slug":305,"title":306,"shortTitle":307,"definition":308,"status":9,"industries":309,"functions":312,"patterns":313,"audience":28,"autonomy":29,"adoptionStage":315,"segment":316,"evidenceCount":274,"publicEvidenceCount":246,"organizations":317,"bestGrade":280,"headline":320,"lastVerified":303,"indexable":206},"account-and-card-servicing-agent","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.",[310,311],"banking","payments",[19,20],[22,272,23,314],"rag-knowledge-assistant","mainstream","front-office",[318,319],"Commonwealth Bank of Australia","DBS Bank",{"kpi":297,"label":298,"unit":299,"n":246,"nUpTo":242,"kind":300,"value":321,"qualifier":229,"claimant":322,"organization":319,"vendorReported":181},90,"organization",{"slug":324,"title":325,"shortTitle":326,"definition":327,"status":9,"industries":328,"functions":330,"patterns":332,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":333,"publicEvidenceCount":333,"organizations":334,"bestGrade":280,"headline":215,"lastVerified":303,"indexable":206},"apartment-leasing-and-resident-service-agent","AI agent for apartment leasing inquiries and resident service","Leasing and resident service agent","An AI agent that answers rental prospects and residents by chat, text, email and phone for a property manager: it answers questions about apartments and policies, books tours, takes maintenance requests, sends renewal and payment reminders, and hands anything that needs judgment to leasing or service staff.",[329],"real-estate",[19,331,20],"sales",[22,272,23,314],3,[335,336,337],"Asset Living","AvalonBay Communities","Equity Residential",{"indexable":206,"reasons":339},[],[341,345,350,358,365,372,378,385,393,400,407,414,420,426,433,440,446,453,456,462,468,475,480,487,492,497,502,507,514,519,526,532,538,544,549,554],{"id":132,"label":342,"issuer":138,"region":139,"url":140,"description":343,"useCases":344,"indexable":206},"EU AI Act","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",230,{"id":133,"label":346,"issuer":138,"region":139,"url":347,"description":348,"useCases":349,"indexable":206},"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.",207,{"id":351,"label":352,"issuer":353,"region":354,"url":355,"description":356,"useCases":357,"indexable":206},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":359,"label":360,"issuer":361,"region":145,"url":362,"description":363,"useCases":364,"indexable":206},"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.",92,{"id":366,"label":367,"issuer":368,"region":139,"url":369,"description":370,"useCases":371,"indexable":206},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":373,"label":374,"issuer":138,"region":139,"url":375,"description":376,"useCases":377,"indexable":206},"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":379,"label":380,"issuer":381,"region":139,"url":382,"description":383,"useCases":384,"indexable":206},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":386,"label":387,"issuer":388,"region":389,"url":390,"description":391,"useCases":392,"indexable":206},"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.",37,{"id":394,"label":395,"issuer":396,"region":389,"url":397,"description":398,"useCases":399,"indexable":206},"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":401,"label":402,"issuer":403,"region":145,"url":404,"description":405,"useCases":406,"indexable":206},"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.",22,{"id":408,"label":409,"issuer":410,"region":354,"url":411,"description":412,"useCases":413,"indexable":206},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":415,"label":416,"issuer":138,"region":139,"url":417,"description":418,"useCases":419,"indexable":206},"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.",17,{"id":421,"label":422,"issuer":423,"region":139,"url":424,"description":425,"useCases":419,"indexable":206},"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.",{"id":427,"label":428,"issuer":429,"region":145,"url":430,"description":431,"useCases":432,"indexable":206},"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.",16,{"id":434,"label":435,"issuer":436,"region":354,"url":437,"description":438,"useCases":439,"indexable":206},"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":441,"label":442,"issuer":138,"region":139,"url":443,"description":444,"useCases":445,"indexable":206},"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":447,"label":448,"issuer":449,"region":145,"url":450,"description":451,"useCases":452,"indexable":206},"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":134,"label":454,"issuer":144,"region":145,"url":146,"description":455,"useCases":452,"indexable":206},"Telephone Consumer Protection Act","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":457,"label":458,"issuer":138,"region":139,"url":459,"description":460,"useCases":461,"indexable":206},"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":463,"label":464,"issuer":465,"region":354,"url":466,"description":467,"useCases":461,"indexable":206},"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":469,"label":470,"issuer":471,"region":145,"url":472,"description":473,"useCases":474,"indexable":206},"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.",11,{"id":476,"label":477,"issuer":138,"region":139,"url":478,"description":479,"useCases":474,"indexable":206},"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.",{"id":481,"label":482,"issuer":483,"region":139,"url":484,"description":485,"useCases":486,"indexable":206},"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.",10,{"id":488,"label":489,"issuer":388,"region":389,"url":490,"description":491,"useCases":486,"indexable":206},"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.",{"id":493,"label":494,"issuer":138,"region":139,"url":495,"description":496,"useCases":486,"indexable":206},"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":498,"label":499,"issuer":138,"region":139,"url":500,"description":501,"useCases":486,"indexable":206},"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":503,"label":504,"issuer":138,"region":139,"url":505,"description":506,"useCases":290,"indexable":206},"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":508,"label":509,"issuer":510,"region":145,"url":511,"description":512,"useCases":513,"indexable":206},"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.",7,{"id":515,"label":516,"issuer":138,"region":139,"url":517,"description":518,"useCases":291,"indexable":206},"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":520,"label":521,"issuer":522,"region":523,"url":524,"description":525,"useCases":273,"indexable":206},"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":527,"label":528,"issuer":529,"region":139,"url":530,"description":531,"useCases":274,"indexable":206},"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":533,"label":534,"issuer":535,"region":139,"url":536,"description":537,"useCases":274,"indexable":206},"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":539,"label":540,"issuer":541,"region":389,"url":542,"description":543,"useCases":333,"indexable":206},"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":545,"label":546,"issuer":138,"region":139,"url":547,"description":548,"useCases":333,"indexable":206},"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":550,"label":551,"issuer":138,"region":139,"url":552,"description":553,"useCases":333,"indexable":206},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":555,"label":556,"issuer":557,"region":145,"url":558,"description":559,"useCases":333,"indexable":206},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683487339]