[{"data":1,"prerenderedAt":542},["ShallowReactive",2],{"uc-public-transit-passenger-information-agent":3,"uc-regulations":331},{"useCase":4,"evidence":187,"blitsAiDeployments":242,"benchmarks":243,"indicative":244,"related":247,"indexability":329,"includeUnpublished":193},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":29,"autonomy":30,"adoptionStage":31,"problem":32,"problemStats":33,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":51,"macroEstimates":86,"feasibility":87,"implementation":100,"risk":146,"blitsAi":166,"faq":168,"related":181,"datePublished":182,"dateModified":182,"lastVerified":182,"changelog":183,"slug":186},"AI agent for public transit passenger information and disruption reporting","Transit passenger information agent","AI chatbot for transit passenger information","An AI agent answers transit riders from live service data and logs their reports. Live at Chicago Transit Authority; NJ Transit pilots Navvie for trip information.","published","An AI agent on a public transport operator's website, app or messaging channel that answers riders' real time questions (\"when is my bus coming\", \"why is my train delayed\") from live service data, takes a structured report when something is wrong on board or at a station, and flags urgent or safety related reports for fast human follow up, in the rider's own language.",[12,13,14,15,16],"transit chatbot","public transport virtual assistant","rider service disruption assistant","where is my bus/train chatbot","transit agency AI assistant",[18],"logistics-and-transportation",[20,21],"customer-service","operations",[23,24],"conversational-agent","classification-and-routing",[26,27,28],"web-chat","mobile-app","sms","customer-facing","supervised-agent","emerging","A public transport operator runs a large, always moving network and has to tell a diverse rider\nbase what is happening on it right now: is my bus coming, why is my train delayed, is the\nelevator at this station working. The Chicago Transit Authority (CTA), an independent government\nagency, operates one of the largest transit systems in the US, more than a million rides on buses\nand trains on an average weekday, 24 hours a day, across the City of Chicago and 35 surrounding\nsuburbs, serving a diverse community that includes many multilingual commuters.\n\nTraditional channels can struggle to keep up: a call centre works through a queue, station\nsignage and a schedule app show the plan more than the live reality, and a rider who wants to\nreport a problem, a dirty train, a broken air conditioner, a safety concern, often has to call\nor wait to flag someone down. NJ Transit, for example, currently reaches riders through station\nand onboard digital signage, its DepartureVision and MyBus systems, mobile apps, websites, SMS\nand push notifications, email alerts, social media, real time and third party data feeds, and\npublic address systems, and the agency says it wants to unify these into one authoritative\nsource of information.",[34],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"The Chicago Transit Authority operates one of the nation's largest public transportation systems covering the City of Chicago and 35 surrounding suburbs, operating 24 hours a day with over a million rides on buses and trains on an average weekday.","Chicago Transit Authority Connects with City: AI Chatbot Bridges Language Barriers and Empowers Riders","https://publicsector.google/ai/chicago-transit-authority-launches-a-multi-lingual-chatbot-for-more-a-more-seamless-commute/",2025,"1. **Understand the request.** The agent classifies whether the rider is asking a schedule or\n   trip question, asking about a known disruption, or reporting a problem on a vehicle, at a\n   station or with staff.\n2. **Answer from live data.** Trip and delay questions are answered from the operator's real\n   time vehicle location, schedule adherence and service alert feeds, not a static timetable.\n3. **Turn a report into a case.** A free text report (\"the AC is broken on the Red Line\") is\n   structured into a category, a location and an urgency, and logged with a reference number the\n   rider can follow up on.\n4. **Flag what is urgent.** Safety concerns and other urgent situations are flagged for fast\n   human follow up, on a stated time target, rather than sitting in the same queue as a routine\n   cleanliness report.\n5. **Hand over what needs a person.** Safety incidents, complaints, unsupported languages and\n   complex itinerary questions go to a human agent with the conversation already captured.",[41,42,43,44],"customer-experience","cost-to-serve","inclusion-and-access","speed",[46,47,48,49,50],"contact-deflection","productivity-gain","containment-rate","response-time-reduction","customer-satisfaction",{"referenceOrg":52,"inputs":53,"formula":81,"currency":82,"period":83,"resultLabel":84,"caveat":85},"A transit agency carrying 150 million passenger trips a year",[54,60,67,74],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"trips","Passenger trips per year",150000000,"trips per year","The reference agency.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"contactsPerTrip","Assisted contacts (calls, chats, social posts, in station reports) per trip",0.0005,0.0015,"contacts per trip","Editorial assumption, replace with your own contact volume.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"automationShare","Share of contacts the agent resolves or logs without a person reaching them first",0.15,0.35,"fraction of contacts","Editorial assumption, replace with your own. No evidence on this page reports a containment, automation or deflection share: the customer service reach and conversation completion figures Google Public Sector reports for CTA measure different things, and NJ Transit's Navvie is still a pilot with no outcome disclosed yet. For scale only, CTA staff review over 250 incidents a week across a system with over a million weekday rides (Google Public Sector), which does not by itself imply a share of contacts.",{"key":75,"label":76,"low":77,"high":78,"unit":79,"note":80},"costPerContact","Cost of a human handled contact",3,7,"USD per contact","Editorial assumption for a blended phone, chat and social contact. Replace with your own fully loaded cost.","trips * contactsPerTrip * automationShare * costPerContact","USD","per year","Human handled contact cost avoided","Gross avoided contact cost only. It leaves out the cost of running the AI and its integrations, the value of faster and more accurate disruption information to riders, and any change in the number or quality of maintenance and safety reports the agency actually receives.",[],{"complexity":88,"complexityNote":89,"dataPrerequisites":90,"integrations":95},"medium","Answering from a static timetable is easy; the work is integrating live vehicle and service alert feeds so answers reflect reality during a disruption, and wiring reports into a system that maintenance and operations teams actually work from, in more than one language.",[91,92,93,94],"Real time vehicle location, schedule adherence and service alert feeds","A taxonomy of report types (cleanliness, mechanical, safety, lost property, staff conduct) mapped to the right team","Current fare, accessibility and policy content","Rider language data, to prioritise which languages to support first",[96,97,98,99],"Real time transit operations feed (vehicle location, schedule adherence, service alerts)","Incident or work order system for maintenance and operations teams","Notification channels (SMS, app push, website and station alerts)","Contact centre or social media platform for handover",{"steps":101,"guardrails":120,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":133},[102,105,108,111,114,117],{"title":103,"detail":104},"Start with schedule and disruption questions","Answer \"when is my bus or train coming\" and active service alerts first: the highest volume, lowest risk questions, and the ones a static timetable answers worst during a disruption.",{"title":106,"detail":107},"Add structured issue reporting once answers are trusted","Turn free text reports into a category, a location and an urgency, and give every report a reference number the rider can check back on.",{"title":109,"detail":110},"Define what counts as urgent, in writing","Agree with operations and safety teams which report categories and keywords trigger fast human follow up, and the time target for that follow up.",{"title":112,"detail":113},"Support the languages your riders actually speak","Prioritise languages by rider population data, not by what is easiest to add first, the way CTA supports English, Spanish, Polish, Simplified Chinese and Filipino/Tagalog.",{"title":115,"detail":116},"Test before riders do","Build a test set of real questions and reports per category and per supported language, including ambiguous and urgent ones, and run it on every change.",{"title":118,"detail":119},"Widen channels once the first one is proven","Launch on the website or app first, measure containment and report routing accuracy, then add messaging channels and voice.",[121,122,123,124,125],"Trip and disruption answers only from live operational data, with a refusal when the feed is stale or unavailable","Every report gets a case reference and a routed owner, tracked to closure","Urgent or safety related reports flagged for human follow up within a stated time target","Input and output guardrails against abuse and off topic prompts, tested after every change","Personal data in reports (names, contact details, photos) masked in logs and model prompts","Operations and safety staff review every flagged urgent report and decide the follow up. A team monitors handover reasons and unmatched report categories weekly, and approves any new report category or supported language before it goes live.",[128,129,130,131,132],"Share of contacts resolved or logged without a person, per question and report type","Time from an urgent report to a confirmed human follow up","Reports confirmed as real issues by maintenance or operations teams, versus all reports logged","Customer satisfaction on chatbot interactions versus human handled ones","Language coverage of the assistant against the rider population it serves",[134,137,140,143],{"title":135,"detail":136},"Confident but stale disruption information","The agent repeats a schedule that a live disruption has already overtaken. Ground trip and delay answers only in live feeds, and say clearly when live data is unavailable.",{"title":138,"detail":139},"Reports that go nowhere","A report is logged but never reaches a team that acts on it. Wire every report category to an owning team and track reports to closure, not just to intake.",{"title":141,"detail":142},"An urgent situation misclassified as routine","A safety relevant report is filed as a routine cleanliness complaint. Err toward escalation on ambiguous language and review missed urgent cases weekly.",{"title":144,"detail":145},"Coverage gaps that exclude riders","The agent supports only the languages that were easiest to add, leaving other riders no better off than before. Prioritise languages by rider population, not convenience.",{"euAiAct":147,"regulations":150,"guidance":153,"controls":160,"incidents":165},{"tier":148,"basis":149},"limited","Article 50(1): riders must be told they are dealing with an AI system, unless that is obvious from the context. Answering trip questions and logging reports is not a listed Annex III use; it would need a fresh assessment if the same agent decided eligibility for a reduced fare, a concession or paratransit access, which touches access to an essential public service.",[151,152],"eu-ai-act","gdpr",[154],{"title":155,"issuer":156,"region":157,"url":158,"note":159},"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","Riders must be informed that they are interacting with an AI system unless this is obvious from the context.",[161,162,163,164],"AI disclosure at the start of every conversation","Case reference and an owning team for every report, tracked to closure","Escalation rules for urgent and safety related reports, with a monitored time target","Guardrail and regression tests rerun after every model or prompt change",[],{"howToBuild":167},"On Blits.ai this is an **AI agent** with **custom functions** that call the operator's real\ntime vehicle and service alert feed, plus a **knowledge base** with fare, accessibility and\npolicy content, retrieved with hybrid search. Issue reporting runs as a **flow** with\ndeterministic steps: category, location and urgency, then a **custom function** creates the\ncase in the maintenance or operations system. The urgent path routes through **agent\nhandover** or a **send email** action, or a **custom function** that calls the on call\nsystem's REST API, so a flagged safety report reaches a person fast.\n\nThe same agent serves **web chat, WhatsApp and SMS**, and, through the **API channel**, the\noperator's own mobile app, replying in the rider's own language. **Guardrails** check input\nand output for abuse and off topic prompts, **PII masking** protects names and contact\ndetails in reports, and **human handover** passes an escalated case to the contact centre or\noperations team with the full conversation. **Test suites** run per question and report type\non every change, **monitors** run on a schedule, and **custom dashboards** break down\nconversation outcomes and report categories. The platform is model agnostic.",[169,172,175,178],{"question":170,"answer":171},"What results have transit agencies reported from this kind of chatbot?","Google Public Sector reports that the Chicago Transit Authority's Chat with CTA chatbot, built with Google and Quantiphi, grew CTA's customer service reach by over 63% and lifted conversation completion by 16% since launch, and that it helps intercept urgent situations within five minutes of a rider's first message. NJ Transit's Navvie is a narrower assistant for trip information only, launched as a pilot by early September 2026 according to Mass Transit, and the agency says results are still being analysed.",{"question":173,"answer":174},"Does this replace real time apps like a trip planner or a map app?","No. It answers from the same kind of live vehicle and service alert data those apps use, but inside a conversation, and it adds a way to report a problem and get a case reference, which a trip planner does not do.",{"question":176,"answer":177},"How does the agent decide a report is urgent?","Operations and safety teams agree in advance which report categories and language (for example anything describing a safety threat) trigger fast human follow up, with a stated time target, rather than joining the same queue as a routine cleanliness report.",{"question":179,"answer":180},"What should stay with a person?","Safety incidents, complaints, lost property claims above a set value, languages the assistant does not yet support, and any itinerary question complex enough that a scripted answer would mislead the rider.",[],"2026-09-28",[184],{"date":182,"note":185},"First published","public-transit-passenger-information-agent",[188,214],{"title":189,"useCases":190,"organization":191,"vendors":196,"summary":197,"stage":198,"year":199,"channels":200,"languages":201,"metrics":202,"outcomeDisclosed":193,"sources":203,"verification":209,"grade":211,"id":212,"organizationSlug":213},"NJ Transit: Navvie trip planning chatbot",[186],{"name":192,"anonymized":193,"country":194,"region":195,"industry":18},"NJ Transit",false,"US","north-america",[],"NJ Transit launched Navvie, its first AI powered chatbot, alongside a redesigned website; Mass Transit reported this by early September 2026. Navvie is available around the clock to help riders plan trips and get schedules, alerts and transfer information. NJ Transit describes it as a pilot: results are being analysed before a decision on integrating it into the mobile app, and the agency separately issued a request for information for a larger, unified real time customer communications platform.","pilot",2026,[26],[],[],[204],{"url":205,"title":206,"publisher":207,"date":208},"https://www.masstransitmag.com/management/news/55402724/new-jersey-transit-nj-transit-nj-transit-launches-new-website-ai-chatbot-rfi-to-improve-customer-communications","NJ Transit launches new website, AI chatbot, RFI to improve customer communications","Mass Transit","2026-09-03",{"level":210,"checkedAt":182},"source-verified","C","nj-transit-navvie-chatbot",null,{"title":215,"useCases":216,"organization":217,"vendors":219,"summary":226,"stage":227,"year":38,"channels":228,"languages":229,"metrics":235,"outcomeDisclosed":236,"sources":237,"verification":240,"grade":211,"id":241,"organizationSlug":213},"Chicago Transit Authority: Chat with CTA",[186],{"name":218,"anonymized":193,"country":194,"region":195,"industry":18},"Chicago Transit Authority",[220,223],{"name":221,"role":222},"Google Public Sector","platform",{"name":224,"role":225},"Quantiphi","integrator","The Chicago Transit Authority (CTA), the independent government agency that runs Chicago's buses and trains, worked with Google Public Sector and Quantiphi to build Chat with CTA, a multilingual virtual assistant on its website. Riders ask when their bus is coming and report issues; the chatbot answers in five languages (English, Spanish, Polish, Simplified Chinese and Filipino/Tagalog) and delivers detailed and timely reports to maintenance crews, including flagging urgent situations for fast follow up. CTA staff review over 250 incidents a week spanning buses, trains and train stations. Google Public Sector says the chatbot \"has grown CTA's customer service reach by over 63%\" and that \"since launch, there's been a 16% improvement in conversation completion\".","production",[26],[230,231,232,233,234],"en","es","pl","zh","tl",[],true,[238],{"url":37,"title":36,"publisher":221,"archivedUrl":239},"https://web.archive.org/web/20250317123845/https://publicsector.google/ai/chicago-transit-authority-launches-a-multi-lingual-chatbot-for-more-a-more-seamless-commute/",{"level":210,"checkedAt":182},"chicago-transit-authority-chat-with-cta",0,[],{"low":245,"high":246},33750,551250,[248,277,295,309],{"slug":249,"title":250,"shortTitle":251,"definition":252,"status":9,"industries":253,"functions":254,"patterns":255,"audience":29,"autonomy":30,"adoptionStage":258,"evidenceCount":259,"publicEvidenceCount":260,"organizations":261,"bestGrade":267,"headline":268,"lastVerified":276,"indexable":236},"parcel-tracking-and-delivery-exception-agent","AI agent for parcel tracking and delivery exceptions","Parcel tracking and delivery exceptions","An AI agent that answers \"where is my parcel\" and resolves delivery exceptions for parcel carriers and postal operators, such as missed deliveries, redelivery or a change of address or pickup point, delays, customs holds and lost or damaged parcel claims, on chat, messaging and phone, and hands disputes and claims above set limits to a human with the tracking history attached.",[18],[20,21],[23,256,257,24],"voice-agent","agentic-workflow","early-adopters",6,5,[262,263,264,265,266],"Chronopost","DPD Deutschland","DPD UK","Evri","PostNL","B",{"kpi":46,"label":269,"unit":270,"n":271,"nUpTo":242,"kind":272,"value":273,"qualifier":274,"claimant":275,"organization":265,"vendorReported":193},"Contact deflection","percent",1,"reported",50,"exact","organization","2026-09-27",{"slug":278,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":285,"patterns":287,"audience":29,"autonomy":30,"adoptionStage":258,"segment":289,"evidenceCount":290,"publicEvidenceCount":77,"organizations":291,"bestGrade":267,"headline":213,"lastVerified":276,"indexable":236},"card-dispute-and-chargeback-intake","AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[283,284],"banking","payments",[20,286,21],"fraud-prevention",[23,256,24,288,257],"document-processing","front-office",4,[292,293,294],"Commonwealth Bank of Australia","Klarna","Visa",{"slug":296,"title":297,"shortTitle":298,"definition":299,"status":9,"industries":300,"functions":302,"patterns":303,"audience":29,"autonomy":30,"adoptionStage":258,"evidenceCount":77,"publicEvidenceCount":77,"organizations":305,"bestGrade":267,"headline":213,"lastVerified":276,"indexable":236},"student-enrollment-and-services-assistant","AI assistant for student enrollment and student services","Student enrollment assistant","An AI assistant that answers admitted and current students' questions about admissions, financial aid, registration, housing and deadlines by text message and web chat, sends timely reminders for the tasks each student still has to complete, and hands personal or complex cases to staff.",[301],"education",[20,21],[23,304,24],"rag-knowledge-assistant",[306,307,308],"Adelphi University","Austin Peay State University","Georgia State University",{"slug":310,"title":311,"shortTitle":312,"definition":313,"status":9,"industries":314,"functions":316,"patterns":319,"audience":29,"autonomy":30,"adoptionStage":31,"segment":289,"evidenceCount":77,"publicEvidenceCount":77,"organizations":320,"bestGrade":267,"headline":324,"lastVerified":328,"indexable":236},"order-to-activation-and-esim-onboarding-assistant","AI assistant for telecom order to activation and eSIM onboarding","Order to activation and eSIM onboarding","An AI assistant that takes a new or existing customer from order to a working service: it collects and checks the order details, guides number porting, eSIM download or SIM activation and installation appointments, tracks the order and fixes or escalates the step that is stuck, on messaging, app, web or phone.",[315],"telecommunications",[317,318,20,21],"sales","onboarding-and-kyc",[23,257,24,288],[321,322,323],"Reliance Jio","Singtel","Verizon",{"kpi":325,"label":326,"unit":270,"n":271,"nUpTo":242,"kind":272,"value":327,"qualifier":274,"claimant":275,"organization":322,"vendorReported":193},"automation-rate","Automation rate",76,"2026-09-26",{"indexable":236,"reasons":330},[],[332,336,341,349,356,362,369,376,384,391,398,404,411,418,424,429,436,442,448,454,460,466,472,477,482,489,496,501,506,513,519,525,531,536],{"id":151,"label":333,"issuer":156,"region":157,"url":158,"description":334,"useCases":335,"indexable":236},"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.",197,{"id":152,"label":337,"issuer":156,"region":157,"url":338,"description":339,"useCases":340,"indexable":236},"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":342,"label":343,"issuer":344,"region":345,"url":346,"description":347,"useCases":348,"indexable":236},"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":350,"label":351,"issuer":352,"region":195,"url":353,"description":354,"useCases":355,"indexable":236},"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":357,"label":358,"issuer":156,"region":157,"url":359,"description":360,"useCases":361,"indexable":236},"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":363,"label":364,"issuer":365,"region":157,"url":366,"description":367,"useCases":368,"indexable":236},"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":370,"label":371,"issuer":372,"region":157,"url":373,"description":374,"useCases":375,"indexable":236},"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":377,"label":378,"issuer":379,"region":380,"url":381,"description":382,"useCases":383,"indexable":236},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":385,"label":386,"issuer":387,"region":380,"url":388,"description":389,"useCases":390,"indexable":236},"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":392,"label":393,"issuer":394,"region":345,"url":395,"description":396,"useCases":397,"indexable":236},"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":399,"label":400,"issuer":401,"region":195,"url":402,"description":403,"useCases":397,"indexable":236},"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":405,"label":406,"issuer":407,"region":157,"url":408,"description":409,"useCases":410,"indexable":236},"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":412,"label":413,"issuer":414,"region":345,"url":415,"description":416,"useCases":417,"indexable":236},"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":419,"label":420,"issuer":156,"region":157,"url":421,"description":422,"useCases":423,"indexable":236},"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":425,"label":426,"issuer":156,"region":157,"url":427,"description":428,"useCases":423,"indexable":236},"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":430,"label":431,"issuer":432,"region":195,"url":433,"description":434,"useCases":435,"indexable":236},"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":437,"label":438,"issuer":156,"region":157,"url":439,"description":440,"useCases":441,"indexable":236},"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":443,"label":444,"issuer":445,"region":195,"url":446,"description":447,"useCases":441,"indexable":236},"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":449,"label":450,"issuer":451,"region":345,"url":452,"description":453,"useCases":441,"indexable":236},"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":455,"label":456,"issuer":156,"region":157,"url":457,"description":458,"useCases":459,"indexable":236},"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":461,"label":462,"issuer":463,"region":195,"url":464,"description":465,"useCases":459,"indexable":236},"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":467,"label":468,"issuer":379,"region":380,"url":469,"description":470,"useCases":471,"indexable":236},"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":473,"label":474,"issuer":156,"region":157,"url":475,"description":476,"useCases":471,"indexable":236},"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":478,"label":479,"issuer":156,"region":157,"url":480,"description":481,"useCases":471,"indexable":236},"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":483,"label":484,"issuer":485,"region":157,"url":486,"description":487,"useCases":488,"indexable":236},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":490,"label":491,"issuer":492,"region":195,"url":493,"description":494,"useCases":495,"indexable":236},"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.",8,{"id":497,"label":498,"issuer":156,"region":157,"url":499,"description":500,"useCases":495,"indexable":236},"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":502,"label":503,"issuer":156,"region":157,"url":504,"description":505,"useCases":259,"indexable":236},"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":507,"label":508,"issuer":509,"region":510,"url":511,"description":512,"useCases":260,"indexable":236},"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":514,"label":515,"issuer":516,"region":157,"url":517,"description":518,"useCases":290,"indexable":236},"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":520,"label":521,"issuer":522,"region":157,"url":523,"description":524,"useCases":290,"indexable":236},"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":526,"label":527,"issuer":528,"region":380,"url":529,"description":530,"useCases":77,"indexable":236},"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":532,"label":533,"issuer":156,"region":157,"url":534,"description":535,"useCases":77,"indexable":236},"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":537,"label":538,"issuer":539,"region":195,"url":540,"description":541,"useCases":77,"indexable":236},"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.",1790598295934]