[{"data":1,"prerenderedAt":655},["ShallowReactive",2],{"uc-claims-first-notice-of-loss-agent":3,"uc-regulations":450},{"useCase":4,"evidence":204,"blitsAiDeployments":341,"benchmarks":342,"indicative":359,"related":362,"indexability":448,"includeUnpublished":210},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":27,"audience":32,"autonomy":33,"adoptionStage":34,"segment":20,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":50,"macroEstimates":90,"feasibility":91,"implementation":105,"risk":151,"blitsAi":181,"faq":183,"related":193,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI agent for first notice of loss claims intake","First notice of loss agent","AI agents for first notice of loss (FNOL) intake","An AI FNOL agent takes the claim report by phone, chat or app and opens the claim. Lemonade says its bot takes 96% of FNOL unaided; Travelers added a voice agent.","published","An AI agent that takes the first notice of loss from a policyholder by phone, chat or app, identifies the policy, collects the facts of the incident and the evidence the claim type needs, opens the claim in the claims system and tells the customer what happens next, handing complex, injured or vulnerable claimants to a human handler.",[12,13,14,15,16],"FNOL agent","AI claims intake","claims reporting bot","voice agent for claims reporting","digital first notice of loss",[18],"insurance",[20,21],"claims","customer-service",[23,24,25,26],"conversational-agent","voice-agent","agentic-workflow","document-processing",[28,29,30,31],"voice","web-chat","mobile-app","whatsapp","customer-facing","supervised-agent","early-adopters","The first notice of loss is the moment the insurer's promise is tested. A customer who has just\nhad a car accident, a burst pipe or a stolen bag calls or logs in to report it, often upset and\noften at night or at the weekend. Today much of that intake is a scripted conversation with a\nhuman handler who types answers into the claims system: policy number, date and place, what\nhappened, who was involved, photos and documents to send later.\n\nThe work is repetitive but it drives everything downstream. Missing or inconsistent facts at\nintake cause call backs, wrong triage, slower settlement and missed fraud or recovery signals.\nAfter a storm, many customers report losses at the same time, and queues grow exactly when\ncustomers need the insurer most. Intake by web form alone does not solve it: forms can be long,\nand many customers still prefer to call (Travelers launched its voice agent for exactly those\ncustomers).",[],"1. **Identify the customer and the policy.** The agent recognises the caller or logged in user\n   and matches the policy, for example by reading back a spoken policy number or registration.\n2. **Take the story in the customer's words.** It asks what happened and extracts the structured\n   facts the claim type needs (date, place, cause, parties, damage, injuries), asking follow up\n   questions only for what is missing.\n3. **Collect evidence while the customer is there.** It asks for photos, receipts or reports\n   through a link or upload and checks that they are readable and relevant.\n4. **Open the claim and set expectations.** It creates the claim in the claims system through an\n   API, returns the claim number and explains the next steps and timelines for this claim type.\n5. **Hand over when it should.** Injuries, vulnerability signals, disputes about cover, complex\n   commercial losses and anything the customer asks a person for go to a handler with the\n   transcript and extracted facts attached.",[39,40,41,42],"customer-experience","cost-to-serve","speed","inclusion-and-access",[44,45,46,47,48,49],"containment-rate","automation-rate","interactions-handled","handling-time-reduction","customer-satisfaction","accuracy",{"referenceOrg":51,"inputs":52,"formula":85,"currency":86,"period":87,"resultLabel":88,"caveat":89},"A motor and home insurer that receives 300,000 claims a year",[53,58,65,71,78],{"key":20,"label":54,"low":55,"high":55,"unit":56,"note":57},"Claims reported per year",300000,"claims per year","The reference insurer.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"assistedShare","Share of claims reported through a human handler today",0.4,0.6,"fraction of claims","Editorial assumption; replace with your own channel mix.",{"key":66,"label":67,"low":68,"high":62,"unit":69,"note":70},"containment","Share of those intakes the agent completes without a handler",0.3,"fraction of assisted intakes","Conservative against the evidence on this page (Lemonade reports that its claims bot takes the first notice of loss without human intervention 96% of the time), because that figure comes from an insurer whose customers already file claims by chatting with its bot in the app.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"minutesPerIntake","Handler minutes per first notice of loss, including wrap up",15,25,"minutes per claim","Editorial assumption, replace with your own handling time.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"costPerHour","Fully loaded cost per handler hour",35,55,"USD per hour","Editorial assumption, replace with your own.","claims * assistedShare * containment * minutesPerIntake / 60 * costPerHour","USD","per year","Handler time avoided at first notice of loss","Intake effort only. It leaves out the effect of better captured facts on triage, leakage and fraud detection, surge capacity after catastrophes, the cost of running the agent and the integration work with the claims and policy systems.",[],{"complexity":92,"complexityNote":93,"dataPrerequisites":94,"integrations":99},"medium","The conversation is the easy part. The work is in policy lookup and identity checks, writing a complete claim into the claims system through APIs, handling many claim types with different questions, and a clean handover for injured or vulnerable claimants.",[95,96,97,98],"The question set and required evidence per claim type and line of business","Policy and customer data reachable through APIs","Recorded or transcribed historical intake calls to design and test against","Approved wording for next steps, timelines and cover explanations",[100,101,102,103,104],"Policy administration system for policy and cover lookup","Claims management system to create and update the claim","Telephony or contact centre platform for voice intake and handover","Document and photo upload with storage linked to the claim","Identity verification and customer authentication",{"steps":106,"guardrails":125,"humanInTheLoop":131,"kpisToInstrument":132,"failureModes":138},[107,110,113,116,119,122],{"title":108,"detail":109},"Start with one line and simple claim types","Pick high volume, low complexity intakes such as glass, minor motor damage or personal possessions, where the facts are predictable and injuries are rare.",{"title":111,"detail":112},"Write the intake schema per claim type","List the mandatory facts, the optional ones and the evidence per claim type, and let the agent fill that schema instead of following a fixed script.",{"title":114,"detail":115},"Connect the systems before going live","The agent must create a real claim with a real claim number. An agent that only emails a transcript to a queue moves work around without removing it.",{"title":117,"detail":118},"Design the handover rules","Decide which signals in what the caller says send them to a person (injury, vulnerability, anger, disputes about cover, commercial losses) and pass the extracted facts so nobody asks twice.",{"title":120,"detail":121},"Test with real recordings and surge scenarios","Replay historical calls, noisy lines, accents, partial policy numbers and catastrophe volumes, and track field level accuracy against what a handler recorded.",{"title":123,"detail":124},"Measure claim quality, not only containment","Follow each agent intake downstream: call backs for missing facts, triage corrections and customer complaints tell you more than the share of calls without a handler.",[126,127,128,129,130],"The agent never tells a customer that a loss is covered or declined; cover decisions stay with the claims process","Automatic handover on injury, vulnerability, distress, disputes about cover and any request for a person","Every extracted field is confirmed back to the customer before the claim is created","Identity and policy match checks before any claim is opened or personal data is disclosed","Personal and health data masked in logs, minimised in model prompts, with retention aligned to claims files","Handlers own every claim the agent opens and review a daily sample of agent intakes against the recording. They take over complex, injured and vulnerable claimants, and claims leadership approves each new claim type before the agent handles it.",[133,134,135,136,137],"Share of intakes completed without a handler, per claim type","Share of agent opened claims that need a call back for missing or wrong facts","Field level accuracy of extracted facts on a weekly sample","Time from first contact to claim number, and customer satisfaction after intake","Handover rate and handover reasons",[139,142,145,148],{"title":140,"detail":141},"Fast intake, poor claim file","The agent completes the call but misses facts handlers need, so the work moves downstream. Measure call backs and triage corrections per claim type.",{"title":143,"detail":144},"Implied promises about cover","A friendly answer such as \"that will be covered\" creates an expectation the insurer may not meet. Block cover statements in the prompt and in output guardrails.",{"title":146,"detail":147},"Vulnerable claimants kept in automation","Bereaved, injured or distressed customers are pushed through a script. Detect the signals in what the customer says and hand over early.",{"title":149,"detail":150},"Collapse under catastrophe volume","The agent is sized for normal days and fails during a storm. Load test for catastrophe peaks and keep a simple fallback that still captures the essentials.",{"euAiAct":152,"regulations":155,"guidance":162,"controls":174,"incidents":180},{"tier":153,"basis":154},"limited","A customer facing intake agent must be designed so that people know they are interacting with AI (Article 50(1)). Claims intake and claims handling are not listed in Annex III: point 5(c) covers risk assessment and pricing in life and health insurance, not claims. One design choice changes this: detecting distress by inferring emotions from the caller's voice is emotion recognition based on biometric data, which is high risk under Annex III point 1(c) and needs disclosure under Article 50(3). Detecting vulnerability from what the caller says does not. The limited tier assumes that design: every handover signal on this page (injury, distress, anger, vulnerability) is detected from the words of the conversation, and inferring emotions from the voice itself is out of scope.",[156,157,158,159,160,161],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","iso-42001","nist-ai-rmf",[163,169],{"title":164,"issuer":165,"region":166,"url":167,"note":168},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","europe","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Published in August 2025 and addressed to national supervisors, it clarifies how the principles and requirements of existing insurance legislation apply to AI systems, following a risk based and proportionate approach.",{"title":170,"issuer":171,"region":166,"url":172,"note":173},"Regulation (EU) 2024/1689 (AI Act), Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","People must be informed that they are interacting with an AI system unless this is obvious from the context.",[175,176,177,178,179],"AI disclosure at the start of every intake conversation","Inventory entry with an accountable claims owner and the list of claim types in scope","Transcript and extracted fields stored with the claim for audit and dispute handling","Regression tests on recorded calls for every change in prompts, models or claim types","Monitoring of outcomes for vulnerable customers and complaints that mention the agent",[],{"howToBuild":182},"On Blits.ai this is an **AI agent** on the **voice** channel (real time streaming speech\nrecognition and synthesis over telephony, with DTMF input) and on **web chat and WhatsApp**, with\nthe insurer's own app connected through the **API channel**. A **flow** holds the intake schema\nper claim type, using **ask question**, **slot filling** and **receive attachment** blocks for the\nfacts, photos and documents. **Custom functions** call the policy system to match the policy and\nthe claims system to open the claim and return the claim number.\n\n**Guardrails** block statements about cover, **PII masking** with configurable patterns keeps\nidentifiers such as policy numbers, contact and payment details out of model prompts and logs,\nand **human handover** escalates to a claims handler through automatic rules,\nwith live takeover also on voice calls. **Test suites** run sets of multi turn intake\nconversations for every claim type on each change, **monitors** run\nscheduled checks against the agent, and **analytics** show interactions and satisfaction per\nbot. The platform is model agnostic and runs\nin EU or UAE regions for data residency.",[184,187,190],{"question":185,"answer":186},"How much of first notice of loss can an AI agent take without a human?","For simple, digital first claim types it can be most of it: Lemonade's annual report states that, as of December 31, 2025, its claims bot took the first notice of loss without human intervention 96% of the time. Incumbent insurers with phone heavy, complex lines should expect lower shares and start with simple claim types such as glass or minor motor damage.",{"question":188,"answer":189},"Does a voice agent make sense when most insurers push digital claims?","Yes, because many customers still call. Travelers says it launched a generative AI voice agent for first notice of loss by phone to serve customers who prefer to call, starting with auto damage claims, next to its straight through digital journey.",{"question":191,"answer":192},"Should the intake agent tell customers whether they are covered?","No. The agent records the loss and explains the process; cover decisions belong to the claims process with its rules and handlers. Statements that imply cover create expectations and complaints, and should be blocked by guardrails.",[194,195,196,197,198],"claims-triage-and-straight-through-processing","photo-based-damage-assessment","claims-fraud-detection","travel-insurance-claims-and-assistance-agent","insurance-policy-servicing-agent","2026-09-27",[201],{"date":199,"note":202},"First published","claims-first-notice-of-loss-agent",[205,233,251,285,305],{"title":206,"useCases":207,"organization":208,"vendors":213,"summary":214,"stage":215,"year":216,"channels":217,"languages":218,"metrics":220,"outcomeDisclosed":210,"sources":221,"verification":227,"grade":230,"id":231,"organizationSlug":232},"Hippo: Clara, an AI assistant for first notice of loss and claims processing",[203],{"name":209,"anonymized":210,"country":211,"region":212,"industry":18},"Hippo",false,"US","north-america",[],"Hippo, a US insurance platform that describes itself as a multi line carrier, reported in its first quarter 2026 investor update that it launched Clara in that quarter, an AI assistant for first notice of loss and end to end claims processing, as part of a wider move to agentic AI in claims, including capacity for catastrophe events. The presentation gives expectations, such as the share of homeowners claims it expects to be filed digitally, but no measured results yet.","production",2026,[],[219],"en",[],[222],{"url":223,"title":224,"publisher":225,"date":226},"https://www.sec.gov/Archives/edgar/data/1828105/000182810526000023/q126quarterlyinvestorupd.htm","Hippo Holdings Inc. first quarter 2026 quarterly investor update (Form 8-K exhibit)","Hippo Holdings Inc. (via SEC EDGAR)","2026-04-30",{"level":228,"checkedAt":229},"source-verified","2026-09-26","B","hippo-clara-ai-claims-assistant",null,{"title":234,"useCases":235,"organization":236,"vendors":238,"summary":239,"stage":215,"year":216,"channels":240,"languages":241,"metrics":242,"outcomeDisclosed":210,"sources":243,"verification":249,"grade":230,"id":250,"organizationSlug":232},"Travelers: generative AI voice agent for first notice of loss and straight through claims",[203,194],{"name":237,"anonymized":210,"country":211,"region":212,"industry":18},"Travelers",[],"In its 2025 annual report to shareholders, Travelers says it launched a natural language generative AI voice agent that takes first notice of loss by phone, used first for auto damage claims and planned to expand to more lines of business and claim interactions. The same letter reports that more than half of all claims are eligible for straight through digital processing, which customers choose about two thirds of the time, and that another 15% of claims are handled with advanced digital tools. No outcome figures for the voice agent itself were published; the company describes early adoption and feedback as positive.",[28],[219],[],[244],{"url":245,"title":246,"publisher":247,"date":248},"https://www.sec.gov/Archives/edgar/data/86312/000110465926040152/tm2611214d1_ars.pdf","The Travelers Companies, Inc. 2025 Annual Report to Shareholders","The Travelers Companies, Inc. (via SEC EDGAR)","2026-04-07",{"level":228,"checkedAt":229},"travelers-generative-ai-fnol-voice-agent",{"title":252,"useCases":253,"organization":254,"vendors":256,"summary":259,"stage":260,"year":261,"channels":262,"languages":263,"metrics":264,"outcomeDisclosed":277,"sources":278,"verification":283,"grade":230,"id":284,"organizationSlug":232},"Lemonade: AI Jim claims bot for first notice of loss and automated settlement",[203,194,196],{"name":255,"anonymized":210,"country":211,"region":212,"industry":18},"Lemonade",[257],{"name":255,"role":258},"in-house","Lemonade's claims bot AI Jim takes the first notice of loss in a chat with the customer, pays or declines simple claims within seconds and assigns the claims it may not settle, or has concerns about, to human claims experts based on their specialty, workload and schedule. A separate system, Forensic Graph, uses machine learning to predict, detect and block fraud across the customer engagement. The annual report states that AI Jim took the first notice of loss without human intervention 96% of the time and that roughly 55% of claims were automated from start to finish, both as of December 31, 2025.","scaled",2025,[30,29],[219],[265,273],{"kpi":44,"value":266,"unit":267,"qualifier":268,"period":269,"claimant":270,"quote":271,"sourceUrl":272},96,"percent","exact","First notice of loss taken without human intervention, as of December 31, 2025","organization","AI Jim is our claims bot, and, as of December 31, 2025, 96% of the time, it is AI Jim that will take the first notice of loss from a Lemonade customer without human intervention","https://www.sec.gov/Archives/edgar/data/1691421/000169142126000016/lmnd-20251231.htm",{"kpi":45,"value":82,"unit":267,"qualifier":274,"period":275,"claimant":270,"quote":276,"sourceUrl":272},"approximately","Share of claims automated end to end, as of December 31, 2025","As of December 31, 2025, roughly 55% of our claims were automated, resulting in instant or near-instant processing from start to finish.",true,[279],{"url":272,"title":280,"publisher":281,"date":282},"Lemonade, Inc. Annual Report on Form 10-K for the fiscal year ended December 31, 2025","U.S. Securities and Exchange Commission (EDGAR)","2026-02-25",{"level":228,"checkedAt":229},"lemonade-ai-jim-claims-automation",{"title":286,"useCases":287,"organization":288,"vendors":290,"summary":291,"stage":215,"year":261,"channels":292,"languages":295,"metrics":296,"outcomeDisclosed":210,"sources":297,"verification":303,"grade":230,"id":304,"organizationSlug":232},"Progressive: digital claims journey with a generative AI assistant in claims messaging",[203],{"name":289,"anonymized":210,"country":211,"region":212,"industry":18},"Progressive",[],"In its 2025 letter to shareholders, Progressive says it implemented digital claims capabilities in 2025 that let customers interact from the first notice of loss through investigation, damage assessment and repair. The same program gave claims employees a new text and email communication platform that includes a customer facing generative AI assistant for automated tasks, information retrieval and tailored follow up actions. No outcome figures were published.",[293,294],"sms","email",[219],[],[298],{"url":299,"title":300,"publisher":301,"date":302},"https://www.sec.gov/Archives/edgar/data/80661/000008066126000086/pgr-20251231exhibit99.htm","The Progressive Corporation 2025 Annual Report, Letter to Shareholders (Exhibit 99 to Form 10-K)","The Progressive Corporation (via SEC EDGAR)","2026-03-02",{"level":228,"checkedAt":229},"progressive-digital-claims-and-generative-ai-assistant",{"title":306,"useCases":307,"organization":308,"vendors":311,"summary":317,"stage":215,"year":318,"channels":319,"languages":320,"metrics":322,"outcomeDisclosed":277,"sources":329,"verification":338,"grade":339,"id":340,"organizationSlug":232},"DOMCURA: Claimens voice agent for claim reporting",[203],{"name":309,"anonymized":210,"country":310,"region":166,"industry":18},"DOMCURA","DE",[312,315],{"name":313,"role":314},"Parloa","platform",{"name":316,"role":314},"Microsoft","DOMCURA, a German underwriting agent, turned its claims chatbot Claimens into a phone based AI agent with Parloa that guides callers through the recurring steps of reporting a claim, matches the caller to the policy by recognising the policy number, and covers more than 20 types of damage claims that the DOMCURA team configured itself. It went from kickoff to live launch in three months. Parloa reports a 90% recognition rate for caller requests.",2023,[28],[321],"de",[323],{"kpi":49,"value":324,"unit":267,"qualifier":268,"period":325,"claimant":326,"quote":327,"sourceUrl":328},90,"Recognition of caller requests","vendor","90% recognition rate for requests","https://www.parloa.com/customers/the-evolution-from-chatbot-to-agent-claimens-makes-filling-claim-reports-easier-than-ever/",[330,333],{"url":328,"title":331,"publisher":313,"date":332},"The evolution from chatbot to voice AI: “Claimens” makes filling claim reports easier than ever","2025-07-16",{"url":334,"title":335,"publisher":336,"date":337},"https://web.archive.org/web/20230205195152/https://www.parloa.com/customers/domcura/","The evolution from chatbot to phone bot: “Claimens” makes filling claim reports easier than ever (archived February 2023)","Parloa (via Internet Archive)","2023-02-05",{"level":228,"checkedAt":229},"C","domcura-claimens-voice-claims-reporting",0,[343,349,354],{"kpi":49,"label":344,"unit":267,"aggregate":277,"higherIsBetter":277,"n":345,"nUpTo":341,"median":324,"min":324,"max":324,"byClaimant":346,"vendorOnly":277,"points":347},"Accuracy",1,{"organization":341,"vendor":345,"regulator":341,"independent":341},[348],{"evidenceId":340,"organization":309,"value":324,"qualifier":268,"claimant":326,"grade":339,"pooled":277},{"kpi":45,"label":350,"unit":267,"aggregate":277,"higherIsBetter":277,"n":345,"nUpTo":341,"median":82,"min":82,"max":82,"byClaimant":351,"vendorOnly":210,"points":352},"Automation rate",{"organization":345,"vendor":341,"regulator":341,"independent":341},[353],{"evidenceId":284,"organization":255,"value":82,"qualifier":274,"claimant":270,"grade":230,"pooled":277},{"kpi":44,"label":355,"unit":267,"aggregate":277,"higherIsBetter":277,"n":345,"nUpTo":341,"median":266,"min":266,"max":266,"byClaimant":356,"vendorOnly":210,"points":357},"Containment rate",{"organization":345,"vendor":341,"regulator":341,"independent":341},[358],{"evidenceId":284,"organization":255,"value":266,"qualifier":268,"claimant":270,"grade":230,"pooled":277},{"low":360,"high":361},315000,2475000,[363,385,398,413,429],{"slug":194,"title":364,"shortTitle":365,"definition":366,"status":9,"industries":367,"functions":368,"patterns":370,"audience":374,"autonomy":33,"adoptionStage":34,"segment":20,"evidenceCount":375,"publicEvidenceCount":376,"organizations":377,"bestGrade":230,"headline":383,"lastVerified":229,"indexable":277},"AI for claims triage and straight through processing","Claims triage and STP","AI that reads each new insurance claim and its documents, scores its complexity, cover questions, fraud and recovery signals, sends it to the right handling path and handler, and settles simple, low risk claims end to end within set limits without a person touching them.",[18],[20,369],"operations",[371,372,26,25,373],"classification-and-routing","prediction-and-scoring","summarization","back-office",9,7,[378,379,380,255,381,382,237],"Admiral Seguros","Allianz Partners","Hiscox","Sedgwick","Tokio Marine & Nichido Fire Insurance",{"kpi":45,"label":350,"unit":267,"n":345,"nUpTo":345,"kind":384,"value":82,"qualifier":274,"claimant":270,"organization":255,"vendorReported":210},"reported",{"slug":195,"title":386,"shortTitle":387,"definition":388,"status":9,"industries":389,"functions":390,"patterns":391,"audience":32,"autonomy":33,"adoptionStage":34,"segment":20,"evidenceCount":393,"publicEvidenceCount":393,"organizations":394,"bestGrade":339,"headline":232,"lastVerified":199,"indexable":277},"AI for photo based damage assessment in insurance claims","Photo damage assessment","Computer vision that assesses damage from photos or video of a vehicle or property taken by the policyholder, a repairer or an adjuster, identifies the damaged parts and the repair or replace decision, produces or checks the repair estimate, and flags total losses and inconsistencies for a person to review.",[18],[20],[392,372,25],"computer-vision",5,[378,395,396,397,382],"Covéa","Foyer","PZU",{"slug":196,"title":399,"shortTitle":400,"definition":401,"status":9,"industries":402,"functions":403,"patterns":405,"audience":374,"autonomy":407,"adoptionStage":408,"segment":20,"evidenceCount":393,"publicEvidenceCount":393,"organizations":409,"bestGrade":230,"headline":232,"lastVerified":199,"indexable":277},"AI for insurance claims fraud detection","Claims fraud detection","AI that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data to find suspicious claims, organised rings and inflated losses, and sends each alert with its reasons to a claims handler or special investigations unit for review.",[18],[20,404],"fraud-prevention",[406,372,26,392,371],"anomaly-detection","assist","mainstream",[410,411,412,255,382],"Assurant","AXA Switzerland","General Insurance Association of Singapore",{"slug":197,"title":414,"shortTitle":415,"definition":416,"status":9,"industries":417,"functions":419,"patterns":420,"audience":32,"autonomy":33,"adoptionStage":422,"segment":20,"evidenceCount":423,"publicEvidenceCount":424,"organizations":425,"bestGrade":230,"headline":426,"lastVerified":199,"indexable":277},"AI agent for travel insurance claims and assistance","Travel insurance claims and assistance","An AI agent that helps insured travellers around the clock and in their own language: it answers cover questions, takes claims for delays, cancellations, lost baggage and medical costs, reads the receipts and certificates they upload, settles simple claims within set limits, and connects medical emergencies and complex cases to the assistance team at once.",[18,418],"travel-and-hospitality",[20,21],[23,24,26,25,421],"translation","emerging",3,2,[379,412],{"kpi":45,"label":350,"unit":267,"n":341,"nUpTo":345,"kind":384,"value":427,"qualifier":428,"claimant":270,"organization":379,"vendorReported":210},70,"up-to",{"slug":198,"title":430,"shortTitle":431,"definition":432,"status":9,"industries":433,"functions":434,"patterns":435,"audience":32,"autonomy":33,"adoptionStage":34,"segment":437,"evidenceCount":438,"publicEvidenceCount":438,"organizations":439,"bestGrade":230,"headline":445,"lastVerified":199,"indexable":277},"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.",[18],[21,369],[23,24,436,25],"rag-knowledge-assistant","policy-administration",6,[440,255,441,442,443,444],"LAQO","Nsure.com","Sun Life","Waterdrop","Zurich Insurance (Hong Kong)",{"kpi":44,"label":355,"unit":267,"n":424,"nUpTo":341,"kind":384,"value":446,"qualifier":447,"claimant":270,"organization":255,"vendorReported":210},50,"at-least",{"indexable":277,"reasons":449},[],[451,455,460,467,473,479,485,491,499,505,512,518,525,531,537,542,549,555,561,567,573,579,585,590,595,601,608,613,618,625,632,638,644,649],{"id":156,"label":452,"issuer":171,"region":166,"url":172,"description":453,"useCases":454,"indexable":277},"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":157,"label":456,"issuer":171,"region":166,"url":457,"description":458,"useCases":459,"indexable":277},"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":160,"label":461,"issuer":462,"region":463,"url":464,"description":465,"useCases":466,"indexable":277},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":161,"label":468,"issuer":469,"region":212,"url":470,"description":471,"useCases":472,"indexable":277},"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":474,"label":475,"issuer":171,"region":166,"url":476,"description":477,"useCases":478,"indexable":277},"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":158,"label":480,"issuer":481,"region":166,"url":482,"description":483,"useCases":484,"indexable":277},"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":159,"label":486,"issuer":487,"region":166,"url":488,"description":489,"useCases":490,"indexable":277},"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":492,"label":493,"issuer":494,"region":495,"url":496,"description":497,"useCases":498,"indexable":277},"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":500,"label":501,"issuer":502,"region":495,"url":503,"description":504,"useCases":75,"indexable":277},"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.",{"id":506,"label":507,"issuer":508,"region":463,"url":509,"description":510,"useCases":511,"indexable":277},"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":513,"label":514,"issuer":515,"region":212,"url":516,"description":517,"useCases":511,"indexable":277},"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":519,"label":520,"issuer":521,"region":166,"url":522,"description":523,"useCases":524,"indexable":277},"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":526,"label":527,"issuer":528,"region":463,"url":529,"description":530,"useCases":74,"indexable":277},"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.",{"id":532,"label":533,"issuer":171,"region":166,"url":534,"description":535,"useCases":536,"indexable":277},"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":538,"label":539,"issuer":171,"region":166,"url":540,"description":541,"useCases":536,"indexable":277},"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":543,"label":544,"issuer":545,"region":212,"url":546,"description":547,"useCases":548,"indexable":277},"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":550,"label":551,"issuer":171,"region":166,"url":552,"description":553,"useCases":554,"indexable":277},"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":556,"label":557,"issuer":558,"region":212,"url":559,"description":560,"useCases":554,"indexable":277},"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":562,"label":563,"issuer":564,"region":463,"url":565,"description":566,"useCases":554,"indexable":277},"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":568,"label":569,"issuer":171,"region":166,"url":570,"description":571,"useCases":572,"indexable":277},"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":574,"label":575,"issuer":576,"region":212,"url":577,"description":578,"useCases":572,"indexable":277},"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":580,"label":581,"issuer":494,"region":495,"url":582,"description":583,"useCases":584,"indexable":277},"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":586,"label":587,"issuer":171,"region":166,"url":588,"description":589,"useCases":584,"indexable":277},"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":591,"label":592,"issuer":171,"region":166,"url":593,"description":594,"useCases":584,"indexable":277},"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":596,"label":597,"issuer":598,"region":166,"url":599,"description":600,"useCases":375,"indexable":277},"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":602,"label":603,"issuer":604,"region":212,"url":605,"description":606,"useCases":607,"indexable":277},"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":609,"label":610,"issuer":171,"region":166,"url":611,"description":612,"useCases":607,"indexable":277},"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":614,"label":615,"issuer":171,"region":166,"url":616,"description":617,"useCases":438,"indexable":277},"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":619,"label":620,"issuer":621,"region":622,"url":623,"description":624,"useCases":393,"indexable":277},"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":626,"label":627,"issuer":628,"region":166,"url":629,"description":630,"useCases":631,"indexable":277},"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":633,"label":634,"issuer":635,"region":166,"url":636,"description":637,"useCases":631,"indexable":277},"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":639,"label":640,"issuer":641,"region":495,"url":642,"description":643,"useCases":423,"indexable":277},"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":645,"label":646,"issuer":171,"region":166,"url":647,"description":648,"useCases":423,"indexable":277},"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":650,"label":651,"issuer":652,"region":212,"url":653,"description":654,"useCases":423,"indexable":277},"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.",1790598294877]