[{"data":1,"prerenderedAt":558},["ShallowReactive",2],{"uc-conversational-insurance-quote-and-buy":3,"uc-regulations":350},{"useCase":4,"evidence":203,"blitsAiDeployments":242,"benchmarks":243,"indicative":244,"related":247,"indexability":348,"includeUnpublished":209},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":25,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":48,"macroEstimates":83,"feasibility":89,"implementation":103,"risk":146,"blitsAi":180,"faq":182,"related":192,"datePublished":198,"dateModified":198,"lastVerified":198,"changelog":199,"slug":202},"Conversational AI for insurance quote and buy","Conversational quote and buy","AI agents for insurance quote and buy","AI agents that quote, explain cover and bind insurance policies in a chat. Lemonade says its bot AI Maya and its APIs sell 98% of its policies.","published","A customer facing AI agent that sells insurance directly in a conversation: it asks the rating questions in plain language, explains cover options, returns a price from the insurer's rating engine, handles objections and takes payment to bind the policy, with a licensed human available for advice and anything outside its limits.",[12,13,14],"insurance sales chatbot","conversational insurance onboarding","digital insurance sales agent",[16],"insurance",[18,19],"sales","customer-service",[21,22,23,24],"conversational-agent","agentic-workflow","recommendation-and-personalization","voice-agent",[26,27,28,29],"web-chat","mobile-app","whatsapp","voice","customer-facing","supervised-agent","early-adopters","distribution","Buying insurance online often means long forms with questions customers do not understand (\"what\nis your property's construction type?\"), and each confusing question can make a customer leave\nbefore the price. A form can show a price, but it cannot answer a question about what is and is not\ncovered. Intermediaries still dominate some lines: Lemonade's 2025 annual report notes that\nhomeowners insurance in the United States is sold primarily via agents.\n\nA conversation can ask fewer, better questions, explain terms in plain language and answer\nquestions about cover at the moment of purchase. The hard part is doing that while staying inside\ndistribution rules: demands and needs testing, product information disclosure, suitability for\ninvestment based products and honest explanations of exclusions.",[],"1. **Understand the need.** The agent asks what the customer wants to protect and captures the\n   demands and needs the law requires before recommending a product.\n2. **Collect rating data conversationally.** It asks only the rating questions the product needs,\n   prefills what it can from approved data sources and explains why each question matters.\n3. **Price through the rating engine.** The agent calls the insurer's own rating and underwriting\n   rules; it never calculates or negotiates price itself.\n4. **Explain and compare cover.** Retrieval over the product documents answers questions on\n   limits, excesses and exclusions, and the required product information is shown before purchase.\n5. **Bind and pay.** The customer confirms the details, accepts the documents and pays through a\n   secure payment link; the policy is issued and documents are sent.\n6. **Hand over when needed.** Advice requests, referrals from underwriting rules, vulnerable\n   customers and complex needs go to a licensed human with the conversation so far.",[38,39,40,41],"revenue-growth","customer-experience","cost-to-serve","inclusion-and-access",[43,44,45,46,47],"conversion-rate-uplift","automation-rate","revenue-uplift","customer-satisfaction","users-served",{"referenceOrg":49,"inputs":50,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A direct personal lines insurer with 200,000 online quote journeys a year",[51,57,64,71],{"key":52,"label":53,"low":54,"high":54,"unit":55,"note":56},"quoteJourneys","Online quote journeys started per year",200000,"journeys per year","The reference insurer.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"baseConversion","Baseline conversion from quote start to purchase",0.08,0.12,"fraction of journeys","Editorial assumption for a direct channel. Replace with your own funnel data.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"relativeUplift","Relative conversion uplift from the conversational journey",0.05,0.15,"fraction of baseline conversion","Editorial assumption; no insurer on this page publishes a controlled uplift. Measure it with an A/B test.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"averagePremium","Average annual premium per new policy",400,600,"USD per policy","Editorial assumption. Replace with your own average premium.","quoteJourneys * baseConversion * relativeUplift * averagePremium","USD","per year","Additional gross written premium","Premium, not profit. It leaves out loss ratio effects of the new business, acquisition costs saved or added, the cost of running the agent and regulatory work, and the risk that a poorly designed flow lowers conversion.",[84],{"statement":85,"sourceTitle":86,"sourceUrl":87,"year":88},"Evident reports that sales and distribution matched underwriting and pricing with nine new AI use cases among the 30 insurers it tracked in the second quarter of 2026, and notes that Aviva, Liberty Mutual Insurance and Allianz now generate live quotes directly inside ChatGPT.","Evident: Insurance Use Case Trends Q2 2026","https://evidentinsights.com/insights/insurance-use-case-trends-q2-2026",2026,{"complexity":90,"complexityNote":91,"dataPrerequisites":92,"integrations":97},"high","The conversation is the easy part. Binding real policies needs the rating engine, underwriting rules, document generation, payments and policy issuance behind APIs, plus distribution compliance (demands and needs, product information, record keeping) built into the flow.",[93,94,95,96],"Rating and underwriting rules exposed through an API","Product documents (terms, product information documents) per product version","A mapping of every rating question to plain language explanations and allowed answers","Approved prefill data sources and their use conditions",[98,99,100,101,102],"Rating engine and underwriting rules","Policy administration for issuance","Payment service provider","Document generation and delivery","CRM and handover to licensed sales staff",{"steps":104,"guardrails":120,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":133},[105,108,111,114,117],{"title":106,"detail":107},"Start with a simple product","Renters, travel, pet or simple home cover with few rating factors and low advice needs are the right first products. Leave life and investment based products for later.",{"title":109,"detail":110},"Put compliance in the flow, not the prompt","Demands and needs capture, required disclosures and document acceptance should be deterministic steps that cannot be skipped, with a record of each.",{"title":112,"detail":113},"Let the rating engine own the price","The agent passes answers to the rating API and presents the result; it must not estimate, discount or negotiate prices.",{"title":115,"detail":116},"Test for misselling","Build test conversations where customers ask leading questions (\"so I'm covered for flood?\") and check that exclusions are explained correctly.",{"title":118,"detail":119},"Run an A/B test","Compare the conversational journey with the existing form on conversion, cancellations in the cooling off period and complaints before switching traffic.",[121,122,123,124,125],"Price only from the rating engine; no free text price statements","Required disclosures and demands and needs steps enforced by the flow","Coverage answers only from the current product documents, with refusal when unsure","Clear AI disclosure and a route to a licensed human","Payment by secure link or tokenized card, never card numbers in the chat transcript","Licensed sales staff take advice requests, underwriting referrals and vulnerable customers. Compliance reviews a sample of completed sales every month, and product owners approve every change to questions, explanations and flows.",[128,129,130,131,132],"Conversion from quote start to purchase versus the form journey, by product","Cancellations within the cooling off period","Complaints and misselling indicators per 1,000 sales","Handover rate to licensed staff and reasons","Satisfaction at purchase",[134,137,140,143],{"title":135,"detail":136},"The agent talks about price","A model rounds, estimates or promises a discount. Keep all price statements tied to the rating engine output.",{"title":138,"detail":139},"Exclusions glossed over","The agent reassures instead of explaining, which surfaces later as a declined claim. Test leading questions and cite the wording.",{"title":141,"detail":142},"Unsuitable sales","The agent recommends a product without capturing needs. Enforce the demands and needs step.",{"title":144,"detail":145},"Invisible fairness issues","Conversational data (language, typing style) leaks into underwriting or pricing. Keep the conversation layer separate from rating inputs.",{"euAiAct":147,"regulations":150,"guidance":157,"controls":173,"incidents":179},{"tier":148,"basis":149},"context-dependent","The conversational layer carries the Article 50 transparency duty. If the system assesses risk or sets prices for life or health insurance of natural persons, that part is high risk under Annex III point 5(c); pricing for property and casualty products is not listed.",[151,152,153,154,155,156],"eu-ai-act","gdpr","uk-consumer-duty","pci-dss","dora","eu-idd",[158,164,169],{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Insurance Distribution Directive (IDD)","European Insurance and Occupational Pensions Authority","europe","https://www.eiopa.europa.eu/browse/regulation-and-policy/insurance-distribution-directive-idd_en","Sets the rules for how insurance is distributed in the EU, including demands and needs, product information and conduct, which also apply to AI sales journeys.",{"title":165,"issuer":166,"region":161,"url":167,"note":168},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system, unless that is obvious from the context.",{"title":170,"issuer":166,"region":161,"url":171,"note":172},"Annex III, high risk AI systems referred to in Article 6(2)","https://artificialintelligenceact.eu/annex/3/","Point 5(c) makes AI for life and health insurance risk assessment and pricing of natural persons high risk.",[174,175,176,177,178],"Record of demands and needs, disclosures shown and documents accepted for every sale","Rating engine version logged with each quote","Monthly compliance sample of AI completed sales","Complaints and cancellation monitoring by journey","PCI DSS scope kept outside the conversational platform through tokenization",[],{"howToBuild":181},"On Blits.ai the sales journey is a **flow** for the regulated steps (demands and needs,\ndisclosures, confirmation) with an **AI agent** block for the open conversation, and a **knowledge\nbase** of product documents for coverage questions. **Custom functions** call the rating engine and\npolicy system over REST, so the price always comes from the insurer's own rules, and **payment\nlinks** through Stripe, Mollie or Adyen take payment with card data tokenized at the payment\nprovider.\n\nThe same journey runs on **web chat, WhatsApp and voice**, and inside the insurer's own app through\nthe API channel, with rich cards for options and product recommendations and **multi language** support. Output **guardrails** with admin authored\npolicies catch price or cover statements the agent should not make, **human handover** routes\nadvice requests to licensed staff, and\n**test suites** replay misselling scenarios on every change. **Analytics** and flow statistics show\nhow the journey performs, and **human in the loop approval** can be required for agent actions\nabove a set threshold.",[183,186,189],{"question":184,"answer":185},"Do insurers really sell policies through chatbots?","Some do. Lemonade's 2025 annual report says its bot AI Maya and its APIs sell 98% of its policies, with Maya collecting information, personalizing coverage, quoting and taking payment in a chat; the filing does not split that share between Maya and the APIs. Evident also notes that Aviva, Liberty Mutual Insurance and Allianz now generate live quotes directly inside ChatGPT.",{"question":187,"answer":188},"Can the AI give advice?","Only within the distribution rules that apply. The cautious design keeps the agent to information and non advised sales on simple products, captures demands and needs in the flow, and hands advice requests to licensed staff.",{"question":190,"answer":191},"Is a sales chatbot high risk under the EU AI Act?","The chat itself needs AI disclosure under Article 50. Any component that assesses risk or sets prices for individual life or health insurance is high risk under Annex III; for home, motor or travel pricing the Act does not list it as high risk.",[193,194,195,196,197],"insurance-policy-servicing-agent","insurance-broker-and-agent-assistant","insurance-renewal-and-retention","insurance-pricing-and-actuarial-copilot","conversational-shopping-assistant","2026-09-27",[200],{"date":198,"note":201},"First published","conversational-insurance-quote-and-buy",[204],{"title":205,"useCases":206,"organization":207,"vendors":212,"summary":215,"stage":216,"year":217,"channels":218,"languages":219,"metrics":221,"outcomeDisclosed":231,"sources":232,"verification":237,"grade":239,"id":240,"organizationSlug":241},"Lemonade: AI Maya for quote and buy, CX.AI for policy service requests",[202,193],{"name":208,"anonymized":209,"country":210,"region":211,"industry":16},"Lemonade",false,"US","north-america",[213],{"name":208,"role":214},"in-house","Lemonade sells renters, homeowners, pet, car and life insurance through a chat with its bot AI Maya, which collects information, personalizes coverage, creates the quote and takes payment by asking a limited number of high impact questions. Its 2025 annual report says AI Maya and its APIs sell 98% of its policies. A second bot platform, CX.AI, resolves pre and post purchase requests such as coverage questions, adding a spouse, changing coverage amounts or payment methods and adding newly bought items, and handles over half of customer inquiries without human intervention.","scaled",2025,[27,26],[220],"en",[222],{"kpi":223,"value":224,"unit":225,"qualifier":226,"period":227,"claimant":228,"quote":229,"sourceUrl":230},"containment-rate",50,"percent","at-least","customer inquiries handled by CX.AI without human intervention, as reported in the 10-K for 2025","organization","Currently, over half of Lemonade’s customer inquiries are handled this way.","https://www.sec.gov/Archives/edgar/data/1691421/000169142126000016/lmnd-20251231.htm",true,[233],{"url":230,"title":234,"publisher":235,"date":236},"Lemonade, Inc. Form 10-K for 2025","Lemonade via SEC EDGAR","2026-02-25",{"level":238,"checkedAt":198},"source-verified","B","lemonade-ai-maya-and-cx-ai",null,0,[],{"low":245,"high":246},320000,2160000,[248,269,286,303,327],{"slug":193,"title":249,"shortTitle":250,"definition":251,"status":9,"industries":252,"functions":253,"patterns":255,"audience":30,"autonomy":31,"adoptionStage":32,"segment":257,"evidenceCount":258,"publicEvidenceCount":258,"organizations":259,"bestGrade":239,"headline":265,"lastVerified":198,"indexable":231},"AI agent for insurance policy servicing","Policy servicing agent","An AI agent that answers policyholders' coverage questions from their own policy documents and completes routine policy changes and document requests (address and vehicle changes, adding a named driver or item, payment method updates, certificates and proof of cover) across chat, messaging and phone, and hands anything complex or sensitive to a human with the context.",[16],[19,254],"operations",[21,24,256,22],"rag-knowledge-assistant","policy-administration",6,[260,208,261,262,263,264],"LAQO","Nsure.com","Sun Life","Waterdrop","Zurich Insurance (Hong Kong)",{"kpi":223,"label":266,"unit":225,"n":267,"nUpTo":242,"kind":268,"value":224,"qualifier":226,"claimant":228,"organization":208,"vendorReported":209},"Containment rate",2,"reported",{"slug":194,"title":270,"shortTitle":271,"definition":272,"status":9,"industries":273,"functions":274,"patterns":276,"audience":279,"autonomy":280,"adoptionStage":32,"segment":33,"evidenceCount":281,"publicEvidenceCount":281,"organizations":282,"bestGrade":239,"headline":241,"lastVerified":198,"indexable":231},"AI assistant for insurance brokers and agents","Broker and agent assistant","An AI assistant for tied agents, independent brokers, advisors and the insurer's own distribution staff that answers product, underwriting and process questions from approved sources, prepares personalized customer engagement and follow ups, validates and prioritizes leads, and drafts meeting notes and emails, so producers spend more time with customers.",[16],[18,275],"knowledge-management",[256,23,277,278],"content-generation","summarization","employee-facing","assist",5,[283,284,262,263,285],"Manulife","Prudential plc","Zurich Insurance Group",{"slug":195,"title":287,"shortTitle":288,"definition":289,"status":9,"industries":290,"functions":291,"patterns":293,"audience":296,"autonomy":297,"adoptionStage":298,"segment":33,"evidenceCount":299,"publicEvidenceCount":299,"organizations":300,"bestGrade":239,"headline":241,"lastVerified":302,"indexable":231},"AI for insurance renewal processing and customer retention","Renewal and retention","AI that prepares and runs the renewal cycle: it digitizes renewal submissions and changes in risk for underwriters, flags policies at risk of lapsing or leaving, prepares the renewal conversation and answers customers' renewal questions, while renewal prices stay governed by the insurer's pricing rules and fair value obligations.",[16],[292,19,18],"underwriting",[294,295,21,23],"prediction-and-scoring","document-processing","back-office","copilot","emerging",3,[301,261,285],"Hiscox","2026-09-26",{"slug":196,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":308,"patterns":312,"audience":279,"autonomy":297,"adoptionStage":32,"segment":314,"evidenceCount":281,"publicEvidenceCount":281,"organizations":315,"bestGrade":239,"headline":321,"lastVerified":302,"indexable":231},"AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[16],[309,310,311],"product-and-pricing","risk-management","analytics-and-reporting",[294,313,22,278],"code-generation","pricing",[316,317,318,319,320],"Accelerant Holdings","Europ Assistance","Generali France","Kinsale Capital Group","MAIF",{"kpi":322,"label":323,"unit":324,"n":325,"nUpTo":242,"kind":268,"value":281,"qualifier":326,"claimant":228,"organization":318,"vendorReported":209},"productivity-gain","Productivity gain","multiplier",1,"exact",{"slug":197,"title":328,"shortTitle":329,"definition":330,"status":9,"industries":331,"functions":334,"patterns":336,"audience":30,"autonomy":337,"adoptionStage":32,"evidenceCount":338,"publicEvidenceCount":281,"organizations":339,"bestGrade":239,"headline":345,"lastVerified":198,"indexable":231},"AI shopping assistant for product discovery and recommendations","Conversational shopping assistant","A conversational assistant on a retailer's site or app that answers product questions, compares items and recommends products from the retailer's own catalog for a need, project or occasion described in the shopper's own words, grounded in product data, reviews and stock, and hands the shopper to a basket, a store or a human expert.",[332,333],"cross-industry","retail-and-ecommerce",[18,335,19],"marketing",[21,23,256,22],"autonomous",10,[340,341,342,343,344],"Amazon","Lowe's","Sun & Ski Sports","Walmart","Zalando",{"kpi":43,"label":346,"unit":324,"n":325,"nUpTo":242,"kind":268,"value":299,"qualifier":326,"claimant":347,"organization":342,"vendorReported":231},"Conversion uplift","vendor",{"indexable":231,"reasons":349},[],[351,356,361,369,376,381,388,394,402,409,415,421,428,435,441,446,453,459,465,471,477,483,488,493,498,505,512,517,521,528,535,541,547,552],{"id":151,"label":352,"issuer":166,"region":161,"url":353,"description":354,"useCases":355,"indexable":231},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":152,"label":357,"issuer":166,"region":161,"url":358,"description":359,"useCases":360,"indexable":231},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":362,"label":363,"issuer":364,"region":365,"url":366,"description":367,"useCases":368,"indexable":231},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":370,"label":371,"issuer":372,"region":211,"url":373,"description":374,"useCases":375,"indexable":231},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":155,"label":377,"issuer":166,"region":161,"url":378,"description":379,"useCases":380,"indexable":231},"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":382,"label":383,"issuer":384,"region":161,"url":385,"description":386,"useCases":387,"indexable":231},"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":153,"label":389,"issuer":390,"region":161,"url":391,"description":392,"useCases":393,"indexable":231},"FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":395,"label":396,"issuer":397,"region":398,"url":399,"description":400,"useCases":401,"indexable":231},"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":403,"label":404,"issuer":405,"region":398,"url":406,"description":407,"useCases":408,"indexable":231},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":154,"label":410,"issuer":411,"region":365,"url":412,"description":413,"useCases":414,"indexable":231},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":416,"label":417,"issuer":418,"region":211,"url":419,"description":420,"useCases":414,"indexable":231},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":422,"label":423,"issuer":424,"region":161,"url":425,"description":426,"useCases":427,"indexable":231},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":429,"label":430,"issuer":431,"region":365,"url":432,"description":433,"useCases":434,"indexable":231},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":436,"label":437,"issuer":166,"region":161,"url":438,"description":439,"useCases":440,"indexable":231},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":442,"label":443,"issuer":166,"region":161,"url":444,"description":445,"useCases":440,"indexable":231},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":447,"label":448,"issuer":449,"region":211,"url":450,"description":451,"useCases":452,"indexable":231},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":454,"label":455,"issuer":166,"region":161,"url":456,"description":457,"useCases":458,"indexable":231},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":460,"label":461,"issuer":462,"region":211,"url":463,"description":464,"useCases":458,"indexable":231},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":466,"label":467,"issuer":468,"region":365,"url":469,"description":470,"useCases":458,"indexable":231},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":472,"label":473,"issuer":166,"region":161,"url":474,"description":475,"useCases":476,"indexable":231},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":478,"label":479,"issuer":480,"region":211,"url":481,"description":482,"useCases":476,"indexable":231},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":484,"label":485,"issuer":397,"region":398,"url":486,"description":487,"useCases":338,"indexable":231},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":489,"label":490,"issuer":166,"region":161,"url":491,"description":492,"useCases":338,"indexable":231},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":494,"label":495,"issuer":166,"region":161,"url":496,"description":497,"useCases":338,"indexable":231},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":499,"label":500,"issuer":501,"region":161,"url":502,"description":503,"useCases":504,"indexable":231},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":506,"label":507,"issuer":508,"region":211,"url":509,"description":510,"useCases":511,"indexable":231},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":513,"label":514,"issuer":166,"region":161,"url":515,"description":516,"useCases":511,"indexable":231},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":156,"label":518,"issuer":166,"region":161,"url":519,"description":520,"useCases":258,"indexable":231},"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":522,"label":523,"issuer":524,"region":525,"url":526,"description":527,"useCases":281,"indexable":231},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":529,"label":530,"issuer":531,"region":161,"url":532,"description":533,"useCases":534,"indexable":231},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":536,"label":537,"issuer":538,"region":161,"url":539,"description":540,"useCases":534,"indexable":231},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":542,"label":543,"issuer":544,"region":398,"url":545,"description":546,"useCases":299,"indexable":231},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":548,"label":549,"issuer":166,"region":161,"url":550,"description":551,"useCases":299,"indexable":231},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":553,"label":554,"issuer":555,"region":211,"url":556,"description":557,"useCases":299,"indexable":231},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598307111]