[{"data":1,"prerenderedAt":695},["ShallowReactive",2],{"uc-insurance-policy-servicing-agent":3,"uc-regulations":491},{"useCase":4,"evidence":193,"blitsAiDeployments":368,"benchmarks":369,"indicative":383,"related":386,"indexability":489,"includeUnpublished":199},{"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":81,"feasibility":82,"implementation":96,"risk":139,"blitsAi":170,"faq":172,"related":182,"datePublished":188,"dateModified":188,"lastVerified":188,"changelog":189,"slug":192},"AI agent for insurance policy servicing","Policy servicing agent","AI agents for insurance policy servicing","AI agents answer coverage questions and make routine policy changes. Lemonade's 10-K says its bot handles over half of customer inquiries without human help.","published","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.",[12,13,14],"policyholder service chatbot","insurance customer service agent","coverage question assistant",[16],"insurance",[18,19],"customer-service","operations",[21,22,23,24],"conversational-agent","voice-agent","rag-knowledge-assistant","agentic-workflow",[26,27,28,29],"web-chat","mobile-app","whatsapp","voice","customer-facing","supervised-agent","early-adopters","policy-administration","Most contact with an insurer between purchase and claim is routine: am I covered for this, send me\nmy certificate, I changed car, add my partner, update my card. Each request is simple but depends on\nthe specific policy wording, endorsements and product version, so front line staff spend time\nlooking things up and customers wait on hold at renewal peaks.\n\nMany first generation chatbots answered generic FAQs and could not see the customer's policy or change\nanything, so the conversation ended in a queue anyway. Coverage answers are also regulated: a wrong\n\"yes, you're covered\" becomes a complaint or a dispute when a claim is declined.",[],"1. **Identify and authenticate.** The agent verifies the policyholder in proportion to the request:\n   a logged in session for questions, step up checks before changes.\n2. **Answer from the customer's own policy.** Coverage questions are answered by retrieving the\n   policy schedule, wording and endorsements that apply to this customer, with the clause cited.\n3. **Complete routine changes.** Through an allow list of policy system actions the agent updates\n   details, adds drivers or items within set limits, issues documents and takes payments, and\n   shows any premium change before the customer confirms.\n4. **Know when to stop.** Claims, complaints, cancellations with refunds above a threshold,\n   vulnerability signals and ambiguous coverage questions go to a human with the conversation\n   summary.\n5. **Learn from the gaps.** Unanswered questions and handovers are reviewed to fix content and add\n   new intents.",[38,39,40,41],"cost-to-serve","customer-experience","inclusion-and-access","employee-productivity",[43,44,45,46,47],"containment-rate","first-contact-resolution","interactions-handled","customer-satisfaction","handling-time-reduction",{"referenceOrg":49,"inputs":50,"formula":76,"currency":77,"period":78,"resultLabel":79,"caveat":80},"A personal lines insurer with 1 million policyholders",[51,56,63,69],{"key":52,"label":53,"low":54,"high":54,"unit":52,"note":55},"policyholders","Policyholders",1000000,"The reference insurer.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"contactsPerPolicyholder","Servicing contacts per policyholder per year",0.5,1,"contacts per policyholder per year","Editorial assumption, excluding claims contacts. Replace with your own contact volume.",{"key":64,"label":65,"low":66,"high":59,"unit":67,"note":68},"containment","Share of servicing contacts the agent resolves",0.25,"fraction of contacts","In line with the evidence on this page (Lemonade's 10-K says over half of inquiries are handled without human intervention).",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"costPerContact","Cost of a human handled contact",4,8,"USD per contact","Editorial assumption for a blended phone, chat and email contact. Replace with your own fully loaded cost.","policyholders * contactsPerPolicyholder * containment * costPerContact","USD","per year","Human handled servicing cost avoided","Gross avoided contact cost only. It leaves out the cost of running the agent, integration with the policy system, and effects on retention and complaints, which can go either way depending on answer quality.",[],{"complexity":83,"complexityNote":84,"dataPrerequisites":85,"integrations":90},"medium","Answering from generic content is easy; answering from the customer's own wording and changing the policy is the work. Legacy policy administration systems may lack APIs for mid term adjustments, and product wordings exist in many versions.",[86,87,88,89],"Policy wordings, schedules and endorsements by product version, linked to each policy","A catalog of servicing intents with volumes from the contact centre","Rules for which changes can be made without underwriting review, and their limits","Approved answers for regulated topics (cancellation rights, complaints, claims)",[91,92,93,94,95],"Policy administration system (read and mid term adjustment APIs)","Document generation for certificates and proof of cover","Payment provider for premium changes","Contact centre platform for handover with context","Identity verification and customer portal login",{"steps":97,"guardrails":113,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[98,101,104,107,110],{"title":99,"detail":100},"Rank intents by volume and risk","Start with document requests, payment updates and simple coverage questions; leave cancellations with refunds and anything touching claims for a later wave.",{"title":102,"detail":103},"Link answers to the right wording","Index wordings by product and version and retrieve by the customer's policy, not by keyword. If the customer is not identified, answer only in general terms and say so.",{"title":105,"detail":106},"Define the change allow list","For every change write the API call, the authentication level, the underwriting limits (for example which vehicles or sums insured may be changed without review) and the confirmation the customer sees.",{"title":108,"detail":109},"Design handover and vulnerability rules","Hand over on complaints, claims, bereavement, financial difficulty and repeated failure, with the summary and verified identity passed to the human.",{"title":111,"detail":112},"Test with real conversations","Build a test set from transcripts, including tricky coverage questions and attempts to push the agent into confirming cover it cannot confirm, and run it on every change.",[114,115,116,117,118],"Coverage answers only from the customer's own policy documents, with the clause cited","Explicit wording that the agent does not decide claims, with handover for any claim question","Premium changes shown and confirmed by the customer before they apply","Step up authentication before changes to payment details or named persons","AI disclosure at the start of the conversation and an easy route to a human","Humans handle claims, complaints, vulnerable customers and any change outside the allow list. A service quality team reviews a weekly sample of contained conversations, with extra focus on coverage answers, and approves every new intent before release.",[121,122,123,124,125],"Containment per intent, counting repeat contacts within seven days as not contained","Accuracy of coverage answers on a weekly audited sample","Handover rate and reasons","Customer satisfaction for AI handled versus human handled contacts","Complaints that mention the assistant",[127,130,133,136],{"title":128,"detail":129},"Confident wrong coverage answers","The agent answers from the current product wording when the customer holds an older version. Retrieve by policy and version, and refuse when unsure.",{"title":131,"detail":132},"Changes that should have gone to underwriting","A mid term change alters the risk (a new driver, a higher sum insured) without review. Encode underwriting limits in the allow list.",{"title":134,"detail":135},"Containment that is really abandonment","Customers give up and call instead. Measure repeat contacts and satisfaction per intent.",{"title":137,"detail":138},"Claims conversations handled as service","A customer describes a loss while asking about cover. Detect claim signals and hand over to claims.",{"euAiAct":140,"regulations":143,"guidance":151,"controls":163,"incidents":169},{"tier":141,"basis":142},"limited","A customer facing assistant must be designed so that people know they are interacting with AI (Article 50(1), applicable from 2 August 2026). It is not high risk as long as it does not carry out risk assessment and pricing in relation to natural persons in life and health insurance (Annex III point 5(c)).",[144,145,146,147,148,149,150],"eu-ai-act","gdpr","uk-gdpr","dora","uk-consumer-duty","pci-dss","eu-idd",[152,158],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":159,"issuer":160,"region":155,"url":161,"note":162},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Published 6 August 2025 and addressed to national supervisors. Sets out risk based, proportionate expectations for insurers using AI systems, including fairness, transparency and explainability, and human oversight, and mentions chatbots as an example use.",[164,165,166,167,168],"AI disclosure and a visible route to a human in every channel","Inventory entry with an accountable owner and a documented action allow list","Audit trail of every policy change the agent made","Regression tests on coverage questions for each wording release","Monitoring of complaints and outcomes for vulnerable customers",[],{"howToBuild":171},"On Blits.ai this is an **AI agent** with a **knowledge base** of policy wordings and endorsements,\nsearched with **hybrid retrieval**, plus **custom functions** that call the policy administration\nsystem's REST APIs to fetch the customer's own schedule and wording version and to make mid term\nchanges. Regulated journeys, such as changing payment details, run as a **flow** with an\nauthentication step, and **payment links** handle any premium due.\n\nThe same agent serves **web chat, WhatsApp and voice**, and the insurer's own mobile app through the\n**REST or WebSocket API channel**, with streaming speech on the phone and **multi language** support. **Guardrails** check inputs and outputs, **PII masking** and\ncard number tokenization happen at the gateway, and **human handover** passes the summary to the\ncontact centre, including live takeover. **Test suites** replay real coverage questions on every\nwording release, **analytics** show interactions, satisfaction and top intents, and **conversation\nlogs** show each handover in full.",[173,176,179],{"question":174,"answer":175},"What share of customer questions can an AI agent handle?","Published figures sit between about 30% and 60%, though each covers a different scope. Infobip reports that LAQO's assistant, which covers claims and general information about LAQO rather than policy servicing, handles 30% of customer queries. Nsure.com says its copilot handles around 60% of customer questions. Lemonade's 10-K says over half of its customer inquiries are handled by its bot platform without human intervention. Waterdrop reported in its second quarter 2025 results that its AI Customer Service Agent resolved 60% of inquiries on first contact.",{"question":177,"answer":178},"Can the agent tell a customer whether they are covered?","It can explain what the customer's own policy wording says, with the clause cited, and should hand over when the answer depends on the facts of a loss. Claims decisions stay with the claims team.",{"question":180,"answer":181},"Is a policy servicing chatbot high risk under the EU AI Act?","Usually not; it carries the Article 50 duty to make clear that customers are talking to AI. It would become high risk under Annex III point 5(c) if it were used for risk assessment and pricing in relation to individuals in life and health insurance.",[183,184,185,186,187],"claims-first-notice-of-loss-agent","conversational-insurance-quote-and-buy","insurance-renewal-and-retention","first-line-contact-centre-agent","account-servicing-execution","2026-09-27",[190],{"date":188,"note":191},"First published","insurance-policy-servicing-agent",[194,231,260,291,318,339],{"title":195,"useCases":196,"organization":197,"vendors":202,"summary":205,"stage":206,"year":207,"channels":208,"languages":209,"metrics":211,"outcomeDisclosed":220,"sources":221,"verification":226,"grade":228,"id":229,"organizationSlug":230},"Lemonade: AI Maya for quote and buy, CX.AI for policy service requests",[184,192],{"name":198,"anonymized":199,"country":200,"region":201,"industry":16},"Lemonade",false,"US","north-america",[203],{"name":198,"role":204},"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],[210],"en",[212],{"kpi":43,"value":213,"unit":214,"qualifier":215,"period":216,"claimant":217,"quote":218,"sourceUrl":219},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,[222],{"url":219,"title":223,"publisher":224,"date":225},"Lemonade, Inc. Form 10-K for 2025","Lemonade via SEC EDGAR","2026-02-25",{"level":227,"checkedAt":188},"source-verified","B","lemonade-ai-maya-and-cx-ai",null,{"title":232,"useCases":233,"organization":235,"vendors":238,"summary":240,"stage":241,"year":207,"channels":242,"languages":244,"metrics":245,"outcomeDisclosed":220,"sources":252,"verification":257,"grade":228,"id":259,"organizationSlug":230},"Sun Life: AI in individual insurance underwriting, contact centre chat and advisor tools",[192,234],"insurance-broker-and-agent-assistant",{"name":236,"anonymized":199,"country":237,"region":201,"industry":16},"Sun Life","CA",[239],{"name":236,"role":204},"Sun Life's 2025 annual report says AI tools cut median response times for individual insurance applications in a target segment by close to half, including a 50% increase in straight through underwriting. In its Client Contact Centre, generative AI raised chatbot containment by 16 percentage points year over year, and advisors use a generative AI Notes Assistant Tool; in Hong Kong it launched Advisory Buddy, a GenAI chatbot inside its Advisor Workbench. In Malaysia almost two thirds of clients received automated underwriting decisions within two hours.","production",[243,26],"internal-tools",[210],[246],{"kpi":247,"value":213,"unit":214,"qualifier":248,"period":249,"claimant":217,"quote":250,"sourceUrl":251},"processing-time-reduction","approximately","median response time for individual insurance applications, target segment, 2025","Leveraged AI tools to do more for our Clients including reduced median response times for individual insurance applications for a target segment by close to half (including increasing straight-through underwriting by 50%), increased chatbot containment rate (up 16 percentage points year-over-year) with generative AI tools in the Client Contact Centre, and enhanced advisor productivity using a generative AI-powered Notes Assistant Tool.","https://www.sec.gov/Archives/edgar/data/1097362/000109736226000010/a2025q4slfmdalive.htm",[253],{"url":251,"title":254,"publisher":255,"date":256},"Sun Life Financial Inc. Management's Discussion and Analysis for 2025 (Form 40-F, Exhibit 99.1)","Sun Life via SEC EDGAR","2026-02-12",{"level":227,"checkedAt":258},"2026-09-26","sun-life-ai-underwriting-and-client-service",{"title":261,"useCases":262,"organization":263,"vendors":267,"summary":269,"stage":206,"year":207,"channels":270,"languages":271,"metrics":273,"outcomeDisclosed":220,"sources":280,"verification":289,"grade":228,"id":290,"organizationSlug":230},"Waterdrop: Waterdrop Guardian AI suite for insurance sales, service, consultants and underwriting",[192,234],{"name":264,"anonymized":199,"country":265,"region":266,"industry":16},"Waterdrop","CN","asia-pacific",[268],{"name":264,"role":204},"Waterdrop, a Chinese online insurance distribution and health services platform, runs a suite of AI applications called Waterdrop Guardian that either talk to users directly or support its online insurance consultants. In its second quarter 2025 results it reported that its AI Customer Service Agent resolved 60% of inquiries on first contact, that its Life Planner Copilot handled 300,000 product consultations for consultants, and that premiums facilitated by its AI Medical Insurance Expert rose 155% from the previous quarter (the release does not say whether this tool talks to users directly or supports consultants). It also launched KEYI.AI, a real time AI underwriting assistant for consultants. Its 2023 annual report describes an earlier LLM powered AI Insurance Consultant that was tested internally in medical insurance scenarios, an internal test rather than a sales deployment.",[27,243],[272],"zh",[274],{"kpi":44,"value":275,"unit":214,"qualifier":276,"period":277,"claimant":217,"quote":278,"sourceUrl":279},60,"exact","second quarter 2025, AI Customer Service Agent","‘AI Customer Service Agent’ resolved 60% of inquiries on first contact, enhancing user experience.","https://www.sec.gov/Archives/edgar/data/1823986/000110465925087282/tm2524976d1_ex99-1.htm",[281,285],{"url":279,"title":282,"publisher":283,"date":284},"Waterdrop Inc. second quarter 2025 unaudited financial results (Form 6-K, Exhibit 99.1)","Waterdrop via SEC EDGAR","2025-09-04",{"url":286,"title":287,"publisher":283,"date":288},"https://www.sec.gov/Archives/edgar/data/1823986/000110465924051464/wdh-20231231x20f.htm","Waterdrop Inc. annual report for 2023 (Form 20-F)","2024-04-25",{"level":227,"checkedAt":258},"waterdrop-guardian-ai-insurance-assistants",{"title":292,"useCases":293,"organization":294,"vendors":296,"summary":303,"stage":241,"year":304,"channels":305,"languages":308,"metrics":309,"outcomeDisclosed":220,"sources":310,"verification":315,"grade":316,"id":317,"organizationSlug":230},"Nsure.com: Friendly John copilot for payments, renewal offers and discount requests",[192,185],{"name":295,"anonymized":199,"country":200,"region":201,"industry":16},"Nsure.com",[297,300],{"name":298,"role":299},"Microsoft","platform",{"name":301,"role":302},"Netwise","integrator","Nsure.com is a Florida based digital insurance agency that lets consumers compare home and auto quotes from more than 50 insurers and buy online. Generative AI in Power Automate cut its service representatives' manual processing time by more than 60%, for example by triaging the shared inboxes and either preparing an automated response or routing each email to an agent. It replaced a third party chatbot with a Copilot Studio copilot, Friendly John, that helps customers submit payments, review renewal offers and request discounts, with an interactive voice response option and after hours support. Its VP of AI and Automation says it handles around 60% of customer questions, and the company plans to use copilots for new policy sales and cross selling.",2024,[26,29,306,307],"email","sms",[210],[],[311],{"url":312,"title":313,"publisher":314},"https://www.microsoft.com/en/customers/story/1728829430186194098-nsure-power-platform-insurance-usa","Digital insurance agency, Nsure.com, reduces manual processing time by 60% using generative AI and Power Automate","Microsoft Customer Stories",{"level":227,"checkedAt":258},"C","nsure-friendly-john-copilot",{"title":319,"useCases":320,"organization":321,"vendors":324,"summary":328,"stage":241,"year":304,"channels":329,"languages":331,"metrics":332,"outcomeDisclosed":199,"sources":333,"verification":337,"grade":316,"id":338,"organizationSlug":230},"Zurich Insurance (Hong Kong): WhatsApp service agent that triages policy and claim questions",[192],{"name":322,"anonymized":199,"country":323,"region":266,"industry":16},"Zurich Insurance (Hong Kong)","HK",[325,326],{"name":298,"role":299},{"name":327,"role":299},"Twilio","Zurich Insurance (Hong Kong) added WhatsApp to its contact centre with Dynamics 365 Contact Center and an agent built in Copilot Studio that captures preliminary information such as policy numbers before escalating to live staff, who then already know what the customer needs. Its head of customer services management says the bot might be able to answer simple questions. A second agent automates motor claim status updates from external surveyors to customers by SMS or email. The company reports lower call and email volumes and staff handling up to two chats at once, and was piloting Copilot to search the knowledge base during live chats. No containment figure is published for the AI agent itself.",[28,307,306,330],"agent-desktop",[],[],[334],{"url":335,"title":336,"publisher":314},"https://www.microsoft.com/en/customers/story/24082-zurich-dynamics-365-contact-center","Zurich Insurance (Hong Kong) transforms customer service and communications with Dynamics 365",{"level":227,"checkedAt":258},"zurich-hong-kong-whatsapp-service-agent",{"title":340,"useCases":341,"organization":342,"vendors":345,"summary":350,"stage":241,"year":351,"channels":352,"languages":353,"metrics":355,"outcomeDisclosed":220,"sources":362,"verification":366,"grade":316,"id":367,"organizationSlug":230},"LAQO: Pavle, a generative AI digital assistant for policy and claims questions on WhatsApp",[192],{"name":343,"anonymized":199,"country":344,"region":155,"industry":16},"LAQO","HR",[346,348],{"name":347,"role":299},"Infobip",{"name":298,"role":349},"model-provider","LAQO, Croatia's first fully digital insurance provider (part of Croatian Insurance), built Pavle with Infobip on Azure OpenAI Service to answer customers 24/7 on WhatsApp in Croatian. The assistant is limited to insurance claims and general information about LAQO to reduce the risk of misleading answers, guides customers through reporting an accident after confirming cover, and transfers complex queries to a live agent. The vendor reports that Pavle handles 30% of customer queries and that 90% of queries are resolved within three to five messages; LAQO's head of digital sales and customer support says the contact centre now spends 10 percent less effort.",2023,[28],[354],"hr",[356],{"kpi":43,"value":357,"unit":214,"qualifier":276,"period":358,"claimant":359,"quote":360,"sourceUrl":361},30,"share of customer queries handled by the assistant","vendor","Today, LAQOs digital assistant is handling 30 percent of customer queries, freeing LAQO’s agents to focus on complex cases and customer acquisition.","https://www.infobip.com/customer/laqo",[363],{"url":361,"title":364,"publisher":347,"date":365},"LAQO Insurance elevates support with Infobip's Gen-AI and Azure OpenAI partnership","2023-11-30",{"level":227,"checkedAt":188},"laqo-pavle-digital-assistant",0,[370,378],{"kpi":43,"label":371,"unit":214,"aggregate":220,"higherIsBetter":220,"n":372,"nUpTo":368,"median":373,"min":357,"max":213,"byClaimant":374,"vendorOnly":199,"points":375},"Containment rate",2,40,{"organization":60,"vendor":60,"regulator":368,"independent":368},[376,377],{"evidenceId":229,"organization":198,"value":213,"qualifier":215,"claimant":217,"grade":228,"pooled":220},{"evidenceId":367,"organization":343,"value":357,"qualifier":276,"claimant":359,"grade":316,"pooled":220},{"kpi":44,"label":379,"unit":214,"aggregate":220,"higherIsBetter":220,"n":60,"nUpTo":368,"median":275,"min":275,"max":275,"byClaimant":380,"vendorOnly":199,"points":381},"First contact resolution",{"organization":60,"vendor":368,"regulator":368,"independent":368},[382],{"evidenceId":290,"organization":264,"value":275,"qualifier":276,"claimant":217,"grade":228,"pooled":220},{"low":384,"high":385},500000,4000000,[387,407,418,434,475],{"slug":183,"title":388,"shortTitle":389,"definition":390,"status":9,"industries":391,"functions":392,"patterns":394,"audience":30,"autonomy":31,"adoptionStage":32,"segment":393,"evidenceCount":396,"publicEvidenceCount":396,"organizations":397,"bestGrade":228,"headline":402,"lastVerified":188,"indexable":220},"AI agent for first notice of loss claims intake","First notice of loss agent","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.",[16],[393,18],"claims",[21,22,24,395],"document-processing",5,[398,399,198,400,401],"DOMCURA","Hippo","Progressive","Travelers",{"kpi":403,"label":404,"unit":214,"n":60,"nUpTo":368,"kind":405,"value":406,"qualifier":276,"claimant":359,"organization":398,"vendorReported":220},"accuracy","Accuracy","reported",90,{"slug":184,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":412,"patterns":414,"audience":30,"autonomy":31,"adoptionStage":32,"segment":416,"evidenceCount":60,"publicEvidenceCount":60,"organizations":417,"bestGrade":228,"headline":230,"lastVerified":188,"indexable":220},"Conversational AI for insurance quote and buy","Conversational quote and buy","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.",[16],[413,18],"sales",[21,24,415,22],"recommendation-and-personalization","distribution",[198],{"slug":185,"title":419,"shortTitle":420,"definition":421,"status":9,"industries":422,"functions":423,"patterns":425,"audience":427,"autonomy":428,"adoptionStage":429,"segment":416,"evidenceCount":430,"publicEvidenceCount":430,"organizations":431,"bestGrade":228,"headline":230,"lastVerified":258,"indexable":220},"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],[424,18,413],"underwriting",[426,395,21,415],"prediction-and-scoring","back-office","copilot","emerging",3,[432,295,433],"Hiscox","Zurich Insurance Group",{"slug":186,"title":435,"shortTitle":436,"definition":437,"status":9,"industries":438,"functions":446,"patterns":447,"audience":30,"autonomy":31,"adoptionStage":449,"segment":450,"evidenceCount":451,"publicEvidenceCount":452,"organizations":453,"bestGrade":228,"headline":471,"lastVerified":188,"indexable":220},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[439,440,441,442,443,444,445],"cross-industry","banking","payments","telecommunications","travel-and-hospitality","retail-and-ecommerce","wealth-and-asset-management",[18],[21,22,23,448],"classification-and-routing","mainstream","front-office",25,18,[454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470],"Air India","Airbnb","Bank of America","Bank of the Philippine Islands","BT Group","Commonwealth Bank of Australia","Ingka Group","JetBlue","Klarna","Lufthansa Group","Mobily","NatWest Group","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":43,"label":371,"unit":214,"n":472,"nUpTo":368,"kind":473,"value":474,"qualifier":276,"claimant":230,"organization":230,"vendorReported":199},7,"median",47,{"slug":187,"title":476,"shortTitle":477,"definition":478,"status":9,"industries":479,"functions":480,"patterns":482,"audience":427,"autonomy":31,"adoptionStage":32,"segment":427,"evidenceCount":430,"publicEvidenceCount":372,"organizations":483,"bestGrade":316,"headline":486,"lastVerified":188,"indexable":220},"AI for back office account servicing execution","Account servicing execution","AI that executes the servicing requests that land in operations queues, such as address and mandate changes, standing instructions, beneficiary updates, reissues, payoff and reference letters and loan maintenance, by reading the request, checking it against policy and entitlements, and preparing or making the change in core systems under dual control.",[440,16,445],[19,481],"lending-and-credit",[24,395,448],[484,485],"Banco Supervielle","SS&C Technologies",{"kpi":247,"label":487,"unit":214,"n":372,"nUpTo":368,"kind":405,"value":488,"qualifier":276,"claimant":359,"organization":485,"vendorReported":220},"Cycle time reduction",95,{"indexable":220,"reasons":490},[],[492,497,502,510,517,522,528,533,540,546,552,558,565,572,578,583,590,596,602,608,614,620,626,631,636,643,649,654,659,666,672,678,684,689],{"id":144,"label":493,"issuer":154,"region":155,"url":494,"description":495,"useCases":496,"indexable":220},"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":145,"label":498,"issuer":154,"region":155,"url":499,"description":500,"useCases":501,"indexable":220},"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":503,"label":504,"issuer":505,"region":506,"url":507,"description":508,"useCases":509,"indexable":220},"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":511,"label":512,"issuer":513,"region":201,"url":514,"description":515,"useCases":516,"indexable":220},"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":147,"label":518,"issuer":154,"region":155,"url":519,"description":520,"useCases":521,"indexable":220},"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":146,"label":523,"issuer":524,"region":155,"url":525,"description":526,"useCases":527,"indexable":220},"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":148,"label":529,"issuer":530,"region":155,"url":531,"description":532,"useCases":474,"indexable":220},"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.",{"id":534,"label":535,"issuer":536,"region":266,"url":537,"description":538,"useCases":539,"indexable":220},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":541,"label":542,"issuer":543,"region":266,"url":544,"description":545,"useCases":451,"indexable":220},"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":149,"label":547,"issuer":548,"region":506,"url":549,"description":550,"useCases":551,"indexable":220},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":553,"label":554,"issuer":555,"region":201,"url":556,"description":557,"useCases":551,"indexable":220},"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":559,"label":560,"issuer":561,"region":155,"url":562,"description":563,"useCases":564,"indexable":220},"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":566,"label":567,"issuer":568,"region":506,"url":569,"description":570,"useCases":571,"indexable":220},"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":573,"label":574,"issuer":154,"region":155,"url":575,"description":576,"useCases":577,"indexable":220},"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":579,"label":580,"issuer":154,"region":155,"url":581,"description":582,"useCases":577,"indexable":220},"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":584,"label":585,"issuer":586,"region":201,"url":587,"description":588,"useCases":589,"indexable":220},"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":591,"label":592,"issuer":154,"region":155,"url":593,"description":594,"useCases":595,"indexable":220},"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":597,"label":598,"issuer":599,"region":201,"url":600,"description":601,"useCases":595,"indexable":220},"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":603,"label":604,"issuer":605,"region":506,"url":606,"description":607,"useCases":595,"indexable":220},"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":609,"label":610,"issuer":154,"region":155,"url":611,"description":612,"useCases":613,"indexable":220},"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":615,"label":616,"issuer":617,"region":201,"url":618,"description":619,"useCases":613,"indexable":220},"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":621,"label":622,"issuer":536,"region":266,"url":623,"description":624,"useCases":625,"indexable":220},"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":627,"label":628,"issuer":154,"region":155,"url":629,"description":630,"useCases":625,"indexable":220},"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":632,"label":633,"issuer":154,"region":155,"url":634,"description":635,"useCases":625,"indexable":220},"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":637,"label":638,"issuer":639,"region":155,"url":640,"description":641,"useCases":642,"indexable":220},"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":644,"label":645,"issuer":646,"region":201,"url":647,"description":648,"useCases":73,"indexable":220},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":650,"label":651,"issuer":154,"region":155,"url":652,"description":653,"useCases":73,"indexable":220},"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":150,"label":655,"issuer":154,"region":155,"url":656,"description":657,"useCases":658,"indexable":220},"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.",6,{"id":660,"label":661,"issuer":662,"region":663,"url":664,"description":665,"useCases":396,"indexable":220},"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":667,"label":668,"issuer":669,"region":155,"url":670,"description":671,"useCases":72,"indexable":220},"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":673,"label":674,"issuer":675,"region":155,"url":676,"description":677,"useCases":72,"indexable":220},"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":679,"label":680,"issuer":681,"region":266,"url":682,"description":683,"useCases":430,"indexable":220},"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":685,"label":686,"issuer":154,"region":155,"url":687,"description":688,"useCases":430,"indexable":220},"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":690,"label":691,"issuer":692,"region":201,"url":693,"description":694,"useCases":430,"indexable":220},"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.",1790598295222]