[{"data":1,"prerenderedAt":608},["ShallowReactive",2],{"uc-branch-and-appointment-booking-agent":3,"uc-regulations":401},{"useCase":4,"evidence":184,"blitsAiDeployments":307,"benchmarks":308,"indicative":314,"related":317,"indexability":399,"includeUnpublished":194},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":22,"patterns":25,"channels":30,"audience":35,"autonomy":36,"adoptionStage":37,"problem":38,"problemStats":39,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":52,"macroEstimates":86,"feasibility":87,"implementation":101,"risk":140,"blitsAi":161,"faq":163,"related":173,"datePublished":179,"dateModified":179,"lastVerified":179,"changelog":180,"slug":183},"AI agent for branch finding and appointment booking","Branch and appointment booking","AI agents for branch and appointment booking","An AI agent finds the right branch and books the specialist. Bank of America schedules appointments via Erica; Best Buy and MOGUL.sg book appointments with AI.","published","A conversational agent that finds the nearest suitable location, checks opening hours and which services it offers, books an in person or video appointment with the right specialist, and records the reason for the visit so staff are prepared. In banking it answers \"where is my nearest branch\" and books the mortgage or business banker; the same job exists in retail, healthcare and property.",[12,13,14,15],"branch locator chatbot","appointment booking assistant","branch appointment scheduling","specialist booking agent",[17,18,19,20,21],"cross-industry","banking","retail-and-ecommerce","healthcare","real-estate",[23,24],"customer-service","sales",[26,27,28,29],"conversational-agent","agentic-workflow","rag-knowledge-assistant","voice-agent",[31,32,33,34],"web-chat","mobile-app","whatsapp","voice","customer-facing","autonomous","early-adopters","When not every branch offers every service, the question \"where do I go\" is hard to answer.\nOpening hours vary, and specialists such as mortgage or business bankers may work by\nappointment in only a few locations. Customers who turn up at the\nwrong place, at the wrong time or without the right documents waste a trip, and staff meet them\nunprepared.\n\nBooking by phone ties up contact centre time for a simple task, and web booking forms often do not\nknow which specialist handles which need. The same pattern appears wherever physical visits need\nto be planned: a retailer's service desk, a clinic, a property viewing.",[],"1. **Understand the need.** The agent asks what the visit is for (a mortgage, a business account,\n   a cash deposit, help with the app) because that decides where and with whom.\n2. **Find the right location.** It searches location data for the nearest branch or ATM that\n   offers that service, with opening hours, accessibility details and any temporary closures.\n3. **Offer alternatives.** Where a video call, the app or a phone call would serve the customer\n   better, it says so, and books that instead if the customer prefers.\n4. **Book the slot.** It checks the specialist calendars, offers real times and books the\n   appointment through the scheduling system, with a confirmation and a reminder.\n5. **Prepare the visit.** It records the reason for the visit and tells the customer which\n   documents to bring, so the specialist is ready.\n6. **Pass special needs to a person.** Accessibility requests and anything unusual go to staff\n   rather than being handled by a rule.",[42,43,44,45],"customer-experience","cost-to-serve","revenue-growth","inclusion-and-access",[47,48,49,50,51],"interactions-handled","first-contact-resolution","customer-satisfaction","conversion-rate-uplift","handling-time-reduction",{"referenceOrg":53,"inputs":54,"formula":81,"currency":82,"period":83,"resultLabel":84,"caveat":85},"A bank that books 100,000 branch and specialist appointments a year",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"appointments","Appointments booked per year",100000,"appointments per year","The reference bank.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"agentShare","Share of bookings moved from phone and branch staff to the agent",0.3,0.6,"fraction of appointments","Editorial assumption, replace with your own channel mix.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"minutesPerBooking","Staff minutes per booking handled by a person",4,8,"minutes per booking","Editorial assumption covering the call, calendar lookup and confirmation.",{"key":76,"label":77,"low":65,"high":78,"unit":79,"note":80},"costPerMinute","Fully loaded cost of a staff minute",1,"USD per minute","Editorial assumption, replace with your own fully loaded cost.","appointments * agentShare * minutesPerBooking * costPerMinute","USD","per year","Booking handling cost avoided","Counts booking time only. It leaves out fewer wasted visits and no shows, better prepared appointments that convert more often, the cost of the AI and the calendar integration.",[],{"complexity":88,"complexityNote":89,"dataPrerequisites":90,"integrations":95},"low","Mostly reads location data and writes to one scheduling system. The effort goes into clean, current branch and service data, and a calendar integration that reflects real specialist availability.",[91,92,93,94],"Branch and ATM locations with opening hours, services, accessibility details and closures","A mapping from visit reasons to services and specialist roles","Specialist calendars and booking rules","Document checklists per visit reason",[96,97,98,99,100],"Scheduling or appointment system","Location data or maps service","CRM for visit reasons and follow up","Messaging for confirmations and reminders","Contact centre for handover",{"steps":102,"guardrails":118,"humanInTheLoop":123,"kpisToInstrument":124,"failureModes":130},[103,106,109,112,115],{"title":104,"detail":105},"Clean the location data","Make one owned source for every location's hours, services, accessibility and closures. A branch assistant can only be as current as the branch data behind it, so fix this first.",{"title":107,"detail":108},"Map reasons to specialists","Write down which visit reasons need which service and role, and which can be handled by video or in the app instead.",{"title":110,"detail":111},"Integrate the real calendar","Offer only slots the scheduling system confirms, and write the booking back with the reason for the visit.",{"title":113,"detail":114},"Add reminders and rescheduling","Send a confirmation and a reminder with the document checklist, and let the customer move or cancel the appointment in the same conversation.",{"title":116,"detail":117},"Measure kept appointments","Track bookings, no shows and outcomes by channel so you can see whether guided booking actually improves visits.",[119,120,121,122],"Services and hours only from the owned location data, never inferred by the model","Bookings only into slots the scheduling system confirms","Accessibility needs and special requests passed to staff, not handled by rule","Personal data in bookings disclosed, minimised and stored in region","Branch staff own the appointment once booked and handle accessibility and special requests. Location data owners keep hours and services current, and a sample of conversations is reviewed monthly for wrong locations or services.",[125,126,127,128,129],"Bookings completed by the agent and share of all bookings","No show rate by booking channel","Wrong location or service reports from customers and staff","Handover rate and reasons","Customer satisfaction after the visit",[131,134,137],{"title":132,"detail":133},"Promising a service the branch does not offer","The customer arrives and cannot be helped. Keep service lists owned and current, and answer only from them.",{"title":135,"detail":136},"Phantom slots","The agent offers times that are no longer free. Read and write the live calendar.",{"title":138,"detail":139},"Accessibility handled by rule","A wheelchair user is sent to a branch with steps. Pass accessibility needs to a person and keep accessibility data current.",{"euAiAct":141,"regulations":144,"guidance":148,"controls":155,"incidents":160},{"tier":142,"basis":143},"limited","Article 50(1): people must be told they are interacting with an AI system unless that is obvious. Finding locations and booking appointments does not fall under any Annex III category. If a healthcare version starts to triage patients by urgency, or a public body uses it to decide eligibility for a public service, reassess it against Annex III point 5.",[145,146,147],"eu-ai-act","gdpr","eu-accessibility-act",[149],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"Directive (EU) 2019/882 on the accessibility requirements for products and services","European Union","europe","https://eur-lex.europa.eu/eli/dir/2019/882/oj","The European Accessibility Act sets accessibility requirements for consumer banking services and ecommerce services, so digital booking channels in those sectors need to meet them.",[156,157,158,159],"AI disclosure and a clear route to a person","Owned, dated location and service data with change control","Consent and retention rules for booking data","Monitoring of wrong location reports and no shows",[],{"howToBuild":162},"On Blits.ai this is an **AI agent** with a **custom function** that queries the location data\n(for example a **SQL knowledge base** of branches, hours and services) and another that reads\nand writes the scheduling system over REST. A **flow** handles the booking steps with **show\noptions** for time slots and a **multiple entity check** for the visit details, and the agent\nhandles free questions such as \"which branch near the station opens on Saturday\".\n\nThe same agent runs on **web chat**, **WhatsApp** and **voice**, inside your own app through the\n**REST or WebSocket API channel**, and as a **digital human** in a browser on a branch or lobby screen.\nConfirmations and reminders go out by **email**, sent from a **scheduled workflow**. **Human\nhandover** passes accessibility and special requests to staff, and the **GDPR toolkit** handles consent and data\nremoval. **Monitors** run scheduled checks that the agent returns correct hours for sample\nbranches, and **analytics** show bookings and handovers.",[164,167,170],{"question":165,"answer":166},"Is a branch booking agent worth it when most banking is digital?","It depends on your visit mix. Where branch visits are mostly for specialist needs such as mortgages or business banking, sending the customer to the right person, prepared, saves a wasted trip on both sides. Bank of America, whose Erica assistant has served nearly 50 million users since 2018, uses it to schedule appointments as a handoff to human service.",{"question":168,"answer":169},"Who already books appointments with AI agents?","Bank of America uses Erica to schedule appointments as a handoff to high touch service channels. Outside banking, Google Cloud reports that Best Buy uses AI to guide shoppers through appointment scheduling, and MOGUL.sg books property viewings through a WhatsApp agent. In healthcare, Hemominas in Brazil worked with Xertica on a chatbot for donor search and scheduling. Public outcome data specific to booking is scarce.",{"question":171,"answer":172},"What should you get right first?","Stale location data is the risk to plan for first. Wrong opening hours or a service a branch no longer offers send the customer on a wasted trip however good the conversation is, so fix data ownership before tuning the agent.",[174,175,176,177,178],"patient-appointment-scheduling-and-reminders-agent","outbound-reminder-and-confirmation-agent","home-loan-assistant-and-prequalification","atm-and-self-service-device-assistance","account-and-card-servicing-agent","2026-09-27",[181],{"date":179,"note":182},"First published","branch-and-appointment-booking-agent",[185,234,266,284],{"title":186,"useCases":187,"organization":192,"vendors":197,"summary":200,"stage":201,"year":202,"channels":203,"languages":204,"metrics":206,"outcomeDisclosed":221,"sources":222,"verification":229,"grade":231,"id":232,"organizationSlug":233},"Bank of America: Erica, a virtual financial assistant with proactive insights",[188,189,190,191,183],"financial-wellbeing-coach","offers-and-rewards-agent","first-line-contact-centre-agent","proactive-outbound-engagement-agent",{"name":193,"anonymized":194,"country":195,"region":196,"industry":18},"Bank of America",false,"US","north-america",[198],{"name":193,"role":199},"in-house","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.","scaled",2025,[32],[205],"en",[207,216],{"kpi":208,"value":209,"unit":210,"qualifier":211,"period":212,"claimant":213,"quote":214,"sourceUrl":215},"users-served",50000000,"count","approximately","since launch in 2018, as of August 2025","organization","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":47,"value":217,"unit":210,"qualifier":218,"period":219,"claimant":213,"quote":220,"sourceUrl":215},3000000000,"at-least","client interactions since launch in 2018, as of August 2025","surpassing 3 billion client interactions",true,[223,226],{"url":215,"title":224,"publisher":193,"date":225},"A Decade of AI Innovation: BofA's Virtual Assistant Erica Surpasses 3 Billion Client Interactions","2025-08-20",{"url":227,"title":228,"publisher":193},"https://info.bankofamerica.com/en/digital-banking/erica","Erica: Virtual Financial Assistant",{"level":230,"checkedAt":179},"source-verified","B","bank-of-america-erica-virtual-assistant","bank-of-america",{"title":235,"useCases":236,"organization":238,"vendors":240,"summary":247,"stage":248,"year":249,"channels":250,"languages":251,"metrics":252,"outcomeDisclosed":194,"sources":253,"verification":262,"grade":231,"id":264,"organizationSlug":265},"Best Buy: generative AI virtual assistant for support, deliveries and appointments",[183,237],"order-status-and-returns-agent",{"name":239,"anonymized":194,"country":195,"region":196,"industry":19},"Best Buy",[241,244],{"name":242,"role":243},"Google Cloud","platform",{"name":245,"role":246},"Accenture","integrator","In April 2024 Best Buy announced, with Google Cloud and Accenture, a generative AI virtual assistant for BestBuy.com, its app and its customer support line, expected to launch in late summer 2024, to help customers troubleshoot product issues, change order delivery and scheduling, and manage subscriptions and memberships. Google Cloud reported in its April 2026 list that Best Buy now guides shoppers through technical specifications, issue resolution and appointment scheduling autonomously, using Agent Assist powered by Gemini Enterprise for Customer Experience. It is a retail example of the same job a bank has when it books a branch or specialist appointment. No outcome figures for the assistant were published.","production",2024,[31,32,34],[205],[],[254,258],{"url":255,"title":256,"publisher":239,"date":257},"https://corporate.bestbuy.com/2024/generative-ai-customer-support/","How Best Buy is using generative AI to create better customer support experiences","2024-04-09",{"url":259,"title":260,"publisher":242,"date":261},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","1,302 real-world gen AI use cases from the world's leading organizations","2026-04-22",{"level":230,"checkedAt":263},"2026-09-26","best-buy-appointment-scheduling-agent",null,{"title":267,"useCases":268,"organization":269,"vendors":273,"summary":275,"stage":248,"year":202,"channels":276,"languages":277,"metrics":278,"outcomeDisclosed":221,"sources":279,"verification":281,"grade":282,"id":283,"organizationSlug":265},"MOGUL.sg: WhatsApp agent for property searches and viewing appointments",[183],{"name":270,"anonymized":194,"country":271,"region":272,"industry":21},"MOGUL.sg","SG","asia-pacific",[274],{"name":242,"role":243},"MOGUL.sg, a Singapore property platform, launched MAIA in February 2025: an AI agent on WhatsApp that searches listings and books viewing appointments, built with Vertex AI, Gemini and the Google Maps API. Location lookup plus booking in one conversation is the same pattern a bank needs for \"find my nearest branch and book me in\".",[33],[],[],[280],{"url":259,"title":260,"publisher":242,"date":261},{"level":230,"checkedAt":263},"C","mogul-whatsapp-viewing-appointments",{"title":285,"useCases":286,"organization":287,"vendors":291,"summary":295,"stage":296,"year":249,"channels":297,"languages":298,"metrics":299,"outcomeDisclosed":194,"sources":300,"verification":305,"grade":282,"id":306,"organizationSlug":265},"Hemominas: omnichannel chatbot for blood donor search and scheduling",[183],{"name":288,"anonymized":194,"country":289,"region":290,"industry":20},"Hemominas","BR","latin-america",[292,294],{"name":293,"role":246},"Xertica",{"name":242,"role":243},"Hemominas, Brazil's largest blood bank, partnered with Xertica to develop an omnichannel chatbot for donor search and scheduling of donations. Google Cloud describes the expected impact in terms of potential lives saved, which is a projection, not a measured result, so the record is kept at the announced stage.","announced",[],[],[],[301,302],{"url":259,"title":260,"publisher":242,"date":261},{"url":303,"title":304,"publisher":242},"https://web.archive.org/web/20241003233844/https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","185 real-world gen AI use cases from the world's leading organizations",{"level":230,"checkedAt":179},"hemominas-donor-scheduling-chatbot",0,[309],{"kpi":47,"label":310,"unit":210,"aggregate":194,"higherIsBetter":221,"n":78,"nUpTo":307,"median":217,"min":217,"max":217,"byClaimant":311,"vendorOnly":194,"points":312},"Interactions handled",{"organization":78,"vendor":307,"regulator":307,"independent":307},[313],{"evidenceId":232,"organization":193,"value":217,"qualifier":218,"claimant":213,"grade":231,"pooled":221},{"low":315,"high":316},72000,480000,[318,345,356,370,384],{"slug":174,"title":319,"shortTitle":320,"definition":321,"status":9,"industries":322,"functions":323,"patterns":325,"audience":35,"autonomy":327,"adoptionStage":37,"evidenceCount":328,"publicEvidenceCount":329,"organizations":330,"bestGrade":231,"headline":337,"lastVerified":179,"indexable":221},"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.",[20],[23,324],"operations",[29,26,326,27],"prediction-and-scoring","supervised-agent",9,6,[331,332,333,334,335,336],"Audibel","Howard Brown Health","Mid and South Essex NHS Foundation Trust","Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","WellSpan Health",{"kpi":338,"label":339,"unit":340,"n":78,"nUpTo":307,"kind":341,"value":342,"qualifier":343,"claimant":344,"organization":332,"vendorReported":221},"containment-rate","Containment rate","percent","reported",30,"exact","vendor",{"slug":175,"title":346,"shortTitle":347,"definition":348,"status":9,"industries":349,"functions":351,"patterns":352,"audience":35,"autonomy":327,"adoptionStage":37,"evidenceCount":353,"publicEvidenceCount":71,"organizations":354,"bestGrade":231,"headline":265,"lastVerified":263,"indexable":221},"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.",[17,20,350],"government",[23,324],[29,26,27,326],5,[334,335,355,336],"U.S. Department of Veterans Affairs",{"slug":176,"title":357,"shortTitle":358,"definition":359,"status":9,"industries":360,"functions":361,"patterns":363,"audience":35,"autonomy":327,"adoptionStage":37,"segment":364,"evidenceCount":365,"publicEvidenceCount":365,"organizations":366,"bestGrade":282,"headline":265,"lastVerified":179,"indexable":221},"AI home loan assistant with pre qualification","Home loan assistant","A customer facing assistant that answers home loan questions (rates, loan to value, fees, the documents needed), runs indicative affordability and borrowing estimates from the bank's published rules, and books the customer with a mortgage specialist, grounded in the bank's current, versioned product and policy documents.",[18,21],[362,24,23],"lending-and-credit",[28,26,29],"front-office",3,[367,368,369],"Figure","Loft","Safe Rate",{"slug":177,"title":371,"shortTitle":372,"definition":373,"status":9,"industries":374,"functions":375,"patterns":376,"audience":35,"autonomy":327,"adoptionStage":377,"segment":364,"evidenceCount":78,"publicEvidenceCount":78,"organizations":378,"bestGrade":231,"headline":380,"lastVerified":179,"indexable":221},"AI agent for ATM and self service device assistance","ATM and device assistance","An AI agent that helps customers with problems at or around ATMs and other self service devices, such as a withdrawal that did not pay out, a retained card, a blocked PIN or finding a working machine with cash, over the app, chat or phone, and that opens and tracks the claim or hands it to a person when it cannot be resolved.",[18],[23,324],[26,29,27],"emerging",[379],"NatWest Group",{"kpi":381,"label":382,"unit":340,"n":78,"nUpTo":307,"kind":341,"value":383,"qualifier":343,"claimant":213,"organization":379,"vendorReported":194},"customer-satisfaction-uplift","Satisfaction uplift",150,{"slug":178,"title":385,"shortTitle":386,"definition":387,"status":9,"industries":388,"functions":390,"patterns":391,"audience":35,"autonomy":327,"adoptionStage":392,"segment":364,"evidenceCount":71,"publicEvidenceCount":393,"organizations":394,"bestGrade":231,"headline":397,"lastVerified":179,"indexable":221},"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.",[18,389],"payments",[23,324],[26,29,27,28],"mainstream",2,[395,396],"Commonwealth Bank of Australia","DBS Bank",{"kpi":338,"label":339,"unit":340,"n":393,"nUpTo":307,"kind":341,"value":398,"qualifier":211,"claimant":213,"organization":396,"vendorReported":194},90,{"indexable":221,"reasons":400},[],[402,407,412,420,427,433,440,447,454,461,468,474,481,488,494,499,506,510,516,522,528,534,540,545,550,556,562,567,572,579,585,591,597,602],{"id":145,"label":403,"issuer":151,"region":152,"url":404,"description":405,"useCases":406,"indexable":221},"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":146,"label":408,"issuer":151,"region":152,"url":409,"description":410,"useCases":411,"indexable":221},"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":413,"label":414,"issuer":415,"region":416,"url":417,"description":418,"useCases":419,"indexable":221},"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":421,"label":422,"issuer":423,"region":196,"url":424,"description":425,"useCases":426,"indexable":221},"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":428,"label":429,"issuer":151,"region":152,"url":430,"description":431,"useCases":432,"indexable":221},"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":434,"label":435,"issuer":436,"region":152,"url":437,"description":438,"useCases":439,"indexable":221},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":441,"label":442,"issuer":443,"region":152,"url":444,"description":445,"useCases":446,"indexable":221},"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.",47,{"id":448,"label":449,"issuer":450,"region":272,"url":451,"description":452,"useCases":453,"indexable":221},"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":455,"label":456,"issuer":457,"region":272,"url":458,"description":459,"useCases":460,"indexable":221},"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":462,"label":463,"issuer":464,"region":416,"url":465,"description":466,"useCases":467,"indexable":221},"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":469,"label":470,"issuer":471,"region":196,"url":472,"description":473,"useCases":467,"indexable":221},"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":475,"label":476,"issuer":477,"region":152,"url":478,"description":479,"useCases":480,"indexable":221},"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":482,"label":483,"issuer":484,"region":416,"url":485,"description":486,"useCases":487,"indexable":221},"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":489,"label":490,"issuer":151,"region":152,"url":491,"description":492,"useCases":493,"indexable":221},"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":495,"label":496,"issuer":151,"region":152,"url":497,"description":498,"useCases":493,"indexable":221},"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":500,"label":501,"issuer":502,"region":196,"url":503,"description":504,"useCases":505,"indexable":221},"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":147,"label":507,"issuer":151,"region":152,"url":153,"description":508,"useCases":509,"indexable":221},"European Accessibility Act","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":511,"label":512,"issuer":513,"region":196,"url":514,"description":515,"useCases":509,"indexable":221},"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":517,"label":518,"issuer":519,"region":416,"url":520,"description":521,"useCases":509,"indexable":221},"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":523,"label":524,"issuer":151,"region":152,"url":525,"description":526,"useCases":527,"indexable":221},"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":529,"label":530,"issuer":531,"region":196,"url":532,"description":533,"useCases":527,"indexable":221},"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":535,"label":536,"issuer":450,"region":272,"url":537,"description":538,"useCases":539,"indexable":221},"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":541,"label":542,"issuer":151,"region":152,"url":543,"description":544,"useCases":539,"indexable":221},"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":546,"label":547,"issuer":151,"region":152,"url":548,"description":549,"useCases":539,"indexable":221},"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":551,"label":552,"issuer":553,"region":152,"url":554,"description":555,"useCases":328,"indexable":221},"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.",{"id":557,"label":558,"issuer":559,"region":196,"url":560,"description":561,"useCases":72,"indexable":221},"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":563,"label":564,"issuer":151,"region":152,"url":565,"description":566,"useCases":72,"indexable":221},"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":568,"label":569,"issuer":151,"region":152,"url":570,"description":571,"useCases":329,"indexable":221},"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":573,"label":574,"issuer":575,"region":576,"url":577,"description":578,"useCases":353,"indexable":221},"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":580,"label":581,"issuer":582,"region":152,"url":583,"description":584,"useCases":71,"indexable":221},"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":586,"label":587,"issuer":588,"region":152,"url":589,"description":590,"useCases":71,"indexable":221},"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":592,"label":593,"issuer":594,"region":272,"url":595,"description":596,"useCases":365,"indexable":221},"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":598,"label":599,"issuer":151,"region":152,"url":600,"description":601,"useCases":365,"indexable":221},"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":603,"label":604,"issuer":605,"region":196,"url":606,"description":607,"useCases":365,"indexable":221},"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.",1790598294247]