[{"data":1,"prerenderedAt":729},["ShallowReactive",2],{"uc-claims-triage-and-straight-through-processing":3,"uc-regulations":525},{"useCase":4,"evidence":209,"blitsAiDeployments":405,"benchmarks":406,"indicative":419,"related":422,"indexability":523,"includeUnpublished":216},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":28,"audience":31,"autonomy":32,"adoptionStage":33,"segment":20,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":43,"indicativeValue":50,"macroEstimates":91,"feasibility":92,"implementation":106,"risk":152,"blitsAi":184,"faq":186,"related":196,"datePublished":203,"dateModified":203,"lastVerified":204,"changelog":205,"slug":208},"AI for claims triage and straight through processing","Claims triage and STP","AI claims triage and straight through processing","AI reads each new insurance claim, routes it to the right handler and settles simple claims automatically. Lemonade reports roughly 55% of claims fully automated.","published","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.",[12,13,14,15,16],"claims straight through processing","touchless claims","claims segmentation","automated claims handling","claims complexity scoring",[18],"insurance",[20,21],"claims","operations",[23,24,25,26,27],"classification-and-routing","prediction-and-scoring","document-processing","agentic-workflow","summarization",[29,30],"api","internal-tools","back-office","supervised-agent","early-adopters","Where new claims are routed with a handful of rules and a queue, a simple glass claim\nand a complex injury claim can wait in the same line, experienced handlers spend time on claims\nthat need no judgment, and claims can reach a handler without the authority to settle them. Every claim is read\nfrom scratch: emails, estimates, invoices, medical reports and photos, and complex claims can carry\nthousands of pages of expert evidence.\n\nThe cost shows up as slow settlement of easy claims, and as leakage\non hard claims that did not reach the right specialist early enough. Straight through processing\nhas been an ambition for years, but rules based automation stalls on unstructured documents, so\nmany simple claims still pass through a handler.",[],"1. **Read the claim.** The AI extracts the facts from the intake record and every attached\n   document (estimates, invoices, reports, photos) and writes a short claim summary.\n2. **Check cover and completeness.** It compares the loss with the policy wording and limits and\n   lists what is missing before anyone starts work.\n3. **Score and segment.** Models score complexity, expected severity, fraud risk and recovery\n   potential, and a rule table turns the scores into a handling path.\n4. **Settle the simple ones.** Claims inside strict limits (claim type, amount, clean fraud score,\n   confirmed cover) are approved and paid automatically, or declined only where a rule is\n   unambiguous and the decision is explained.\n5. **Route the rest.** Other claims go to the handler with the right skill, authority and capacity,\n   with the summary, open questions and suggested next steps attached, and the AI keeps checking\n   as new information arrives.",[38,39,40,41,42],"cost-to-serve","speed","customer-experience","employee-productivity","risk-reduction",[44,45,46,47,48,49],"automation-rate","processing-time-reduction","handling-time-reduction","productivity-gain","accuracy","cycle-time-days",{"referenceOrg":51,"inputs":52,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A personal lines insurer that settles 200,000 claims a year",[53,58,65,72,79],{"key":20,"label":54,"low":55,"high":55,"unit":56,"note":57},"Claims per year",200000,"claims per year","The reference insurer.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"stpShare","Share of claims newly settled straight through",0.1,0.3,"fraction of claims","Conservative against the evidence on this page (Lemonade reports roughly 55% of claims automated end to end), because an incumbent starts from legacy systems and a broader mix of claim types.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"costPerSimpleClaim","Internal handling cost of a simple claim today",40,100,"USD per claim","Editorial assumption covering handler time, checks and payment; replace with your own claims expense data.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"otherClaimsMinutesSaved","Handler minutes saved on each remaining claim by the summary and routing",5,15,"minutes per claim","Editorial assumption; replace with a time study.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"costPerHour","Fully loaded cost per handler hour",35,55,"USD per hour","Editorial assumption, replace with your own.","claims * stpShare * costPerSimpleClaim + claims * (1 - stpShare) * otherClaimsMinutesSaved / 60 * costPerHour","USD","per year","Claims handling expense avoided","Handling expense only. It leaves out leakage reduction from better routing, customer retention from faster settlement, the risk of paying claims that a person would have questioned, and the cost of the platform, models and integration.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"high","Routing on a summary is achievable quickly; paying claims without a person is not. Straight through settlement needs reliable document extraction, machine readable policy wording and limits, fraud screening in the same flow, payment integration and a governance model that claims, compliance and actuarial teams accept.",[96,97,98,99],"Historical claims with handling path, outcome, severity and leakage findings to train and test the scores","Policy wordings, limits and excesses in a form the system can check against","Handler skills, authority levels and capacity for routing","Labelled documents per claim type for extraction testing",[101,102,103,104,105],"Claims management system for claim data, reserves, tasks and payments","Policy administration system for cover, limits and excess","Document intake and extraction for estimates, invoices and reports","Fraud detection and subrogation models as inputs to the routing rules","Payment system for automated settlement",{"steps":107,"guardrails":126,"humanInTheLoop":132,"kpisToInstrument":133,"failureModes":139},[108,111,114,117,120,123],{"title":109,"detail":110},"Map today's handling paths and their cost","Take a year of claims and group them by type, severity and handling path. The simple, high volume groups are the straight through candidates; the complex ones are where routing and summaries pay off.",{"title":112,"detail":113},"Summaries and routing before settlement","Start with claim summaries and routing suggestions that handlers accept or correct. It builds the labelled data and trust you need before any claim is paid without a person.",{"title":115,"detail":116},"Define the straight through envelope","Write down per claim type the maximum amount, required documents, cover checks and fraud score threshold for automatic settlement, and have claims, compliance and actuarial sign it off.",{"title":118,"detail":119},"Put fraud and recovery checks in the same flow","Every claim on the automatic path must pass the fraud and subrogation checks first; speed must not open a door for fraud or leave recoveries on the table.",{"title":121,"detail":122},"Run in shadow mode, then widen","Let the system decide in parallel with handlers for a period, compare outcomes claim by claim, and switch on automatic settlement one claim type at a time.",{"title":124,"detail":125},"Audit a sample for ever","Keep reviewing a random sample of automatically settled claims, and watch leakage and complaint trends per claim type.",[127,128,129,130,131],"Automatic settlement only inside a signed off envelope per claim type (amount, documents, cover and fraud score)","Automatic declines only where a rule is unambiguous, with an explanation and a route to a person","Every automated decision logged with the inputs, scores, rules and model versions used","Fraud and recovery screening before any automatic payment","Handlers can override any routing or decision, and overrides feed back into testing","Handlers work every claim outside the envelope and can reopen any automated decision. A quality team reviews a random sample of straight through settlements every week, and claims leadership approves each change to the envelope, the rules or the models.",[134,135,136,137,138],"Share of claims settled straight through, per claim type","Time from first notice of loss to payment for straight through and routed claims","Share of routing suggestions handlers accept, and reasons for overrides","Leakage found in audits of automated settlements","Complaints and reopened claims after automated decisions",[140,143,146,149],{"title":141,"detail":142},"Speed opens a door for fraud","Automatic payment is exactly what fraudsters look for. Keep fraud scoring in the same flow and limit amounts per claim type.",{"title":144,"detail":145},"Extraction errors become payment errors","A misread invoice total is paid automatically. Cross check extracted amounts against estimates and limits, and hold claims with low extraction confidence.",{"title":147,"detail":148},"Automated declines without recourse","Customers receive a decline they cannot challenge. Explain every decline, avoid automatic declines where cover is a judgment, and give a route to a person.",{"title":150,"detail":151},"Routing that ignores capacity","The best handler for every complex claim ends up with a queue of hundreds. Route on skill, authority and current workload together.",{"euAiAct":153,"regulations":156,"guidance":165,"controls":177,"incidents":183},{"tier":154,"basis":155},"context-dependent","Claims handling as such is not listed in Annex III. The same system becomes high risk when it is also used for risk assessment and pricing of natural persons in life and health insurance (point 5(c)), or when it is used by or on behalf of a public authority to grant, reduce, revoke or reclaim essential public assistance benefits and services, including healthcare services (point 5(a)). Otherwise the tier is minimal, so the design and the operator decide. Decisions on claims based solely on automated processing are also subject to Article 22 of the GDPR and the UK GDPR.",[157,158,159,160,161,162,163,164],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","solvency-ii","dora","iso-42001","nist-ai-rmf",[166,172],{"title":167,"issuer":168,"region":169,"url":170,"note":171},"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","Addressed to national supervisors (August 2025); clarifies how existing insurance sector legislation applies to AI systems, with a risk based and proportionate approach to governance, fairness, explainability and human oversight.",{"title":173,"issuer":174,"region":169,"url":175,"note":176},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https://artificialintelligenceact.eu/annex/3/","Lists life and health insurance pricing and risk assessment and public benefit eligibility as high risk, which matters when triage is used for those lines or schemes.",[178,179,180,181,182],"Signed off straight through envelope per claim type, under change control","Decision log with inputs, scores, rules and model versions for every automated settlement","Weekly random audit of automated settlements with leakage and fairness checks","Model validation and monitoring for drift in complexity, fraud and severity scores","Customer route to a person for every automated decision",[],{"howToBuild":185},"On Blits.ai this is an **agentic workflow** triggered through the API when a claim is opened. An\n**AI agent** reads the claim and its documents through the **knowledge base** document ingestion\n(PDF, images and Outlook email files), writes a summary with **structured output**, and calls **custom\nfunctions** that fetch cover and limits from the policy system, pull fraud and severity scores, and\nwrite the routing decision back to the claims system.\n\n**Human in the loop approval** holds any payment above the configured threshold for a handler, and\nthe **tool execution policy** limits which systems the agent may change. Every run has a full\n**audit trail**, **test suites** grade summaries and routing against labelled historical claims on\neach change, and **monitors** run scheduled checks against the agent and alert by email or webhook\nwhen an expectation fails. The platform is model agnostic and can run in EU or UAE regions for\ndata residency.",[187,190,193],{"question":188,"answer":189},"What share of claims can be settled straight through?","It depends on the lines of business and the systems. Lemonade reports that roughly 55% of its claims were automated from start to finish at the end of 2025, and Travelers says more than half of its claims are eligible for straight through digital processing. Starting with a narrow set of simple claim types and widening from there limits the risk.",{"question":191,"answer":192},"Should an AI decline claims automatically?","Rarely. Lemonade already pays or declines simple claims automatically within seconds; automatic declines are safe only where a rule is unambiguous, and the customer must get an explanation and a route to a person. Where cover is a matter of judgment, the AI should prepare the file and a handler should decide.",{"question":194,"answer":195},"Where does triage pay off if straight through settlement is not yet possible?","In handler time and routing quality. Sedgwick gives examiners priorities, claim forecasts and next step guidance inside its claims systems. At Hiscox, a senior technical claims underwriter says that with Microsoft 365 Copilot, identifying and recording the key information of a new claim now takes him as little as 10 minutes instead of up to an hour.",[197,198,199,200,201,202],"claims-first-notice-of-loss-agent","claims-fraud-detection","photo-based-damage-assessment","subrogation-opportunity-detection","health-prior-authorization-and-claims-adjudication","outbound-notice-drafting","2026-09-27","2026-09-26",[206],{"date":203,"note":207},"First published","claims-triage-and-straight-through-processing",[210,254,275,308,329,351,376],{"title":211,"useCases":212,"organization":214,"vendors":219,"summary":220,"stage":221,"year":222,"channels":223,"languages":224,"metrics":225,"outcomeDisclosed":239,"sources":240,"verification":249,"grade":251,"id":252,"organizationSlug":253},"Allianz Partners: AI assisted travel claims turnaround",[213,208],"travel-insurance-claims-and-assistance-agent",{"name":215,"anonymized":216,"country":217,"region":218,"industry":18},"Allianz Partners",false,"US","north-america",[],"Allianz Partners' director of partnerships told a US travel advisor consortium in April 2026 that AI assistance now handles 65 to 70% of all its claims. She said this has cut claims turnaround time from about 14 days, the average for the consortium's member agencies, to three to four days, with some claims turned around in a matter of hours.","scaled",2026,[],[],[226,234],{"kpi":44,"value":227,"unit":228,"qualifier":229,"period":230,"claimant":231,"quote":232,"sourceUrl":233},70,"percent","up-to","Share of claims using AI assistance, stated at Signature Travel Network's Horizon Club, April 2026","organization","We are right now using AI assistance for 65 to 70% of all of our claims, which has brought our claims turnaround time down from about 14 days, which was the average for Signature partners, to three to four days,","https://latteluxurynews.com/2026/04/20/ai-cuts-down-allianz-travel-insurance-claim-time-by-more-than-half/",{"kpi":49,"value":235,"unit":236,"qualifier":229,"period":237,"baseline":238,"claimant":231,"quote":232,"sourceUrl":233},4,"days","Claims turnaround time with AI assistance, for Signature Travel Network member agencies","About 14 days before AI assistance, the average for Signature Travel Network partners",true,[241,245],{"url":233,"title":242,"publisher":243,"date":244},"AI cuts down Allianz travel insurance claim time by more than half","LATTE Australia","2026-04-20",{"url":246,"title":247,"publisher":248,"date":244},"https://completeaitraining.com/news/allianz-cuts-travel-insurance-claim-processing-time-from-14/","Allianz cuts travel insurance claim processing time from 14 days to 3 to 4 days using AI","Complete AI Training",{"level":250,"checkedAt":203},"source-verified","B","allianz-partners-ai-claims-turnaround",null,{"title":255,"useCases":256,"organization":257,"vendors":259,"summary":260,"stage":261,"year":222,"channels":262,"languages":264,"metrics":266,"outcomeDisclosed":216,"sources":267,"verification":273,"grade":251,"id":274,"organizationSlug":253},"Travelers: generative AI voice agent for first notice of loss and straight through claims",[197,208],{"name":258,"anonymized":216,"country":217,"region":218,"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.","production",[263],"voice",[265],"en",[],[268],{"url":269,"title":270,"publisher":271,"date":272},"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":250,"checkedAt":204},"travelers-generative-ai-fnol-voice-agent",{"title":276,"useCases":277,"organization":278,"vendors":280,"summary":283,"stage":221,"year":284,"channels":285,"languages":288,"metrics":289,"outcomeDisclosed":239,"sources":301,"verification":306,"grade":251,"id":307,"organizationSlug":253},"Lemonade: AI Jim claims bot for first notice of loss and automated settlement",[197,208,198],{"name":279,"anonymized":216,"country":217,"region":218,"industry":18},"Lemonade",[281],{"name":279,"role":282},"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.",2025,[286,287],"mobile-app","web-chat",[265],[290,297],{"kpi":291,"value":292,"unit":228,"qualifier":293,"period":294,"claimant":231,"quote":295,"sourceUrl":296},"containment-rate",96,"exact","First notice of loss taken without human intervention, as of December 31, 2025","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":44,"value":83,"unit":228,"qualifier":298,"period":299,"claimant":231,"quote":300,"sourceUrl":296},"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.",[302],{"url":296,"title":303,"publisher":304,"date":305},"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":250,"checkedAt":204},"lemonade-ai-jim-claims-automation",{"title":309,"useCases":310,"organization":311,"vendors":314,"summary":318,"stage":261,"year":284,"channels":319,"languages":320,"metrics":321,"outcomeDisclosed":216,"sources":322,"verification":327,"grade":251,"id":328,"organizationSlug":253},"Sedgwick: Sidekick Agent for claims examiner guidance",[208],{"name":312,"anonymized":216,"country":217,"region":313,"industry":18},"Sedgwick","global",[315],{"name":316,"role":317},"Microsoft","platform","Sedgwick, a global claims administrator, integrated Sidekick Agent into the workflows and screens of its own claims management systems. Built on Azure OpenAI Service and Azure AI Document Intelligence, it gives examiners claim insights, the day's top priorities, forecasts of anticipated claim trajectories, analytics on claim durations and reserves, and guidance on the next steps in the claim life cycle, and supports quality assurance for consistency and compliance. It follows an earlier version of Sidekick built on ChatGPT technology. No outcome figures were published.",[30],[265],[],[323],{"url":324,"title":325,"publisher":312,"date":326},"https://www.sedgwick.com/press-release/sedgwick-optimizes-claim-workflows-with-ai-application-sidekick-and-microsoft-integration/","Sedgwick optimizes claim workflows with AI application Sidekick and Microsoft integration","2025-04-29",{"level":250,"checkedAt":204},"sedgwick-sidekick-agent-claims-guidance",{"title":330,"useCases":331,"organization":332,"vendors":335,"summary":337,"stage":261,"year":284,"channels":338,"languages":340,"metrics":341,"outcomeDisclosed":239,"sources":342,"verification":348,"grade":349,"id":350,"organizationSlug":253},"Hiscox: Microsoft 365 Copilot in claims handling",[208,202],{"name":333,"anonymized":216,"country":334,"region":169,"industry":18},"Hiscox","GB",[336],{"name":316,"role":317},"Hiscox is rolling out Microsoft 365 Copilot to its more than 3,000 employees after a trial. A senior technical claims underwriter in the UK claims team uses it to identify and record the key information of a new claim, to summarise long expert medical evidence and legal advice, and to pull the progress of a claim from several emails and compose an update to a broker or customer. He says that recording a new claim now takes him as little as 10 minutes instead of up to an hour. This is one user's experience, not a measured program result.",[30,339],"email",[265],[],[343],{"url":344,"title":345,"publisher":346,"date":347},"https://ukstories.microsoft.com/features/how-ai-is-supercharging-hiscox-employees-to-do-what-theyre-great-at/","How AI is ‘supercharging’ Hiscox employees","Microsoft UK Stories","2025-06-25",{"level":250,"checkedAt":204},"C","hiscox-copilot-claims-handling",{"title":352,"useCases":353,"organization":354,"vendors":358,"summary":361,"stage":261,"year":284,"channels":362,"languages":363,"metrics":365,"outcomeDisclosed":216,"sources":366,"verification":374,"grade":349,"id":375,"organizationSlug":253},"Tokio Marine & Nichido Fire: AI review of damage photos, estimates and suspicious claims",[208,198,199],{"name":355,"anonymized":216,"country":356,"region":357,"industry":18},"Tokio Marine & Nichido Fire Insurance","JP","asia-pacific",[359],{"name":360,"role":317},"Shift Technology","Tokio Marine & Nichido Fire Insurance uses Shift Technology's claims intake and claims fraud detection solutions, extended with generative AI that extracts data from structured and unstructured sources such as images and documents. The system highlights the points handlers should check for consistency across estimates, damage photos and claim statements, which makes reviews more efficient and more standardized, including during the surge of claims after large disasters, and it helps detect suspicious claims, which tend to rise after such events. No figures were published.",[30],[364],"ja",[],[367,370],{"url":368,"title":369,"publisher":360},"https://www.shift-technology.com/resources/case-studies/tokiomarine_casestudy","Case Study: Tokyo Marine & Nichido Fire Insurance Co., Ltd.",{"url":371,"title":372,"publisher":360,"date":373},"https://www.shift-technology.com/resources/press/tokio-marine-deploys-shift-technologys-gen-ai-for-claims-fraud-detection","Tokio Marine Deploys Shift Technology’s Gen AI for Claims, Fraud Detection","2025-09-04",{"level":250,"checkedAt":204},"tokio-marine-nichido-shift-claims-review",{"title":377,"useCases":378,"organization":379,"vendors":382,"summary":385,"stage":261,"year":386,"channels":387,"languages":388,"metrics":390,"outcomeDisclosed":239,"sources":399,"verification":403,"grade":349,"id":404,"organizationSlug":253},"Admiral Seguros: touchless motor claims with AI damage estimates",[199,208],{"name":380,"anonymized":216,"country":381,"region":169,"industry":18},"Admiral Seguros","ES",[383],{"name":384,"role":317},"Tractable","Admiral Seguros, the Spanish business of Admiral Group, sends motor claimants a link to a web app in which they photograph the damage, and Tractable's AI produces the repair estimate within minutes. Tractable reports that Admiral Seguros processed 12,000 touchless claims this way in 2021, that 90% of claim estimates were processed without human appraisers and that 98% of claims were completed in less than 15 minutes.",2021,[],[389],"es",[391],{"kpi":392,"value":393,"unit":394,"qualifier":293,"period":395,"claimant":396,"quote":397,"sourceUrl":398},"interactions-handled",12000,"count","Touchless claims in 2021","vendor","In 2021, Admiral Seguros processed 12,000 touchless claims using Tractable AI.","https://tractable.ai/case-studies/admiral-seguros/",[400],{"url":398,"title":401,"publisher":384,"date":402},"Admiral Seguros: Tractable delivers outstanding customer services through touchless claims","2023-04-21",{"level":250,"checkedAt":204},"admiral-seguros-ai-vehicle-damage-estimates",1,[407,414],{"kpi":44,"label":408,"unit":228,"aggregate":239,"higherIsBetter":239,"n":405,"nUpTo":405,"median":83,"min":83,"max":83,"byClaimant":409,"vendorOnly":216,"points":411},"Automation rate",{"organization":405,"vendor":410,"regulator":410,"independent":410},0,[412,413],{"evidenceId":252,"organization":215,"value":227,"qualifier":229,"claimant":231,"grade":251,"pooled":216},{"evidenceId":307,"organization":279,"value":83,"qualifier":298,"claimant":231,"grade":251,"pooled":239},{"kpi":49,"label":415,"unit":236,"aggregate":216,"higherIsBetter":216,"n":410,"nUpTo":405,"median":253,"min":253,"max":253,"byClaimant":416,"vendorOnly":216,"points":417},"Cycle time",{"organization":410,"vendor":410,"regulator":410,"independent":410},[418],{"evidenceId":252,"organization":215,"value":235,"qualifier":229,"claimant":231,"grade":251,"pooled":216},{"low":420,"high":421},1325000,7925000,[423,442,458,469,481,503],{"slug":197,"title":424,"shortTitle":425,"definition":426,"status":9,"industries":427,"functions":428,"patterns":430,"audience":433,"autonomy":32,"adoptionStage":33,"segment":20,"evidenceCount":75,"publicEvidenceCount":75,"organizations":434,"bestGrade":251,"headline":438,"lastVerified":203,"indexable":239},"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.",[18],[20,429],"customer-service",[431,432,26,25],"conversational-agent","voice-agent","customer-facing",[435,436,279,437,258],"DOMCURA","Hippo","Progressive",{"kpi":48,"label":439,"unit":228,"n":405,"nUpTo":410,"kind":440,"value":441,"qualifier":293,"claimant":396,"organization":435,"vendorReported":239},"Accuracy","reported",90,{"slug":198,"title":443,"shortTitle":444,"definition":445,"status":9,"industries":446,"functions":447,"patterns":449,"audience":31,"autonomy":452,"adoptionStage":453,"segment":20,"evidenceCount":75,"publicEvidenceCount":75,"organizations":454,"bestGrade":251,"headline":253,"lastVerified":203,"indexable":239},"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],[20,448],"fraud-prevention",[450,24,25,451,23],"anomaly-detection","computer-vision","assist","mainstream",[455,456,457,279,355],"Assurant","AXA Switzerland","General Insurance Association of Singapore",{"slug":199,"title":459,"shortTitle":460,"definition":461,"status":9,"industries":462,"functions":463,"patterns":464,"audience":433,"autonomy":32,"adoptionStage":33,"segment":20,"evidenceCount":75,"publicEvidenceCount":75,"organizations":465,"bestGrade":349,"headline":253,"lastVerified":203,"indexable":239},"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],[451,24,26],[380,466,467,468,355],"Covéa","Foyer","PZU",{"slug":200,"title":470,"shortTitle":471,"definition":472,"status":9,"industries":473,"functions":474,"patterns":476,"audience":31,"autonomy":452,"adoptionStage":33,"segment":20,"evidenceCount":235,"publicEvidenceCount":477,"organizations":478,"bestGrade":251,"headline":253,"lastVerified":203,"indexable":239},"AI for subrogation opportunity detection","Subrogation detection","AI that reads open and closed claims to find cases where a third party is wholly or partly liable, estimates liability and the recoverable amount under the applicable negligence and recovery rules, and sends scored recovery opportunities with their reasons to the subrogation team.",[18],[20,475],"collections-and-recovery",[23,24,25,27],2,[479,480],"Central Insurance","Elephant Insurance",{"slug":201,"title":482,"shortTitle":483,"definition":484,"status":9,"industries":485,"functions":487,"patterns":489,"audience":492,"autonomy":493,"adoptionStage":33,"segment":20,"evidenceCount":75,"publicEvidenceCount":75,"organizations":494,"bestGrade":251,"headline":500,"lastVerified":203,"indexable":239},"AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI that reads prior authorization requests, medical claims and appeals with their clinical and billing documents, extracts diagnoses, treatments and costs, checks them against the policy and published clinical criteria, and prepares a summary and recommendation for a clinician or adjudicator, who makes every adverse decision.",[18,486],"healthcare",[20,488,21],"case-management",[25,27,490,23,491],"rag-knowledge-assistant","content-generation","employee-facing","copilot",[495,496,497,498,499],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","ICICI Lombard","Manulife",{"kpi":46,"label":501,"unit":228,"n":477,"nUpTo":410,"kind":440,"value":502,"qualifier":298,"claimant":396,"organization":495,"vendorReported":239},"Handling time reduction",50,{"slug":202,"title":504,"shortTitle":505,"definition":506,"status":9,"industries":507,"functions":512,"patterns":514,"audience":492,"autonomy":493,"adoptionStage":33,"segment":31,"evidenceCount":75,"publicEvidenceCount":75,"organizations":516,"bestGrade":251,"headline":519,"lastVerified":204,"indexable":239},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[508,509,18,510,486,511],"cross-industry","banking","government","wealth-and-asset-management",[21,429,475,513,20],"regulatory-compliance",[491,490,515],"translation",[495,333,517,518],"Health Resources and Services Administration","SS&C Technologies",{"kpi":45,"label":520,"unit":228,"n":405,"nUpTo":410,"kind":440,"value":521,"qualifier":293,"claimant":396,"organization":522,"vendorReported":239},"Cycle time reduction",25,"SS&C GIDS and RS",{"indexable":239,"reasons":524},[],[526,531,536,542,548,553,559,565,572,578,585,591,598,604,610,615,622,628,634,640,646,652,658,663,668,675,682,686,692,699,705,711,718,723],{"id":157,"label":527,"issuer":174,"region":169,"url":528,"description":529,"useCases":530,"indexable":239},"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":158,"label":532,"issuer":174,"region":169,"url":533,"description":534,"useCases":535,"indexable":239},"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":163,"label":537,"issuer":538,"region":313,"url":539,"description":540,"useCases":541,"indexable":239},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":164,"label":543,"issuer":544,"region":218,"url":545,"description":546,"useCases":547,"indexable":239},"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":162,"label":549,"issuer":174,"region":169,"url":550,"description":551,"useCases":552,"indexable":239},"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":159,"label":554,"issuer":555,"region":169,"url":556,"description":557,"useCases":558,"indexable":239},"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":160,"label":560,"issuer":561,"region":169,"url":562,"description":563,"useCases":564,"indexable":239},"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":566,"label":567,"issuer":568,"region":357,"url":569,"description":570,"useCases":571,"indexable":239},"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":573,"label":574,"issuer":575,"region":357,"url":576,"description":577,"useCases":521,"indexable":239},"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":579,"label":580,"issuer":581,"region":313,"url":582,"description":583,"useCases":584,"indexable":239},"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":586,"label":587,"issuer":588,"region":218,"url":589,"description":590,"useCases":584,"indexable":239},"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":592,"label":593,"issuer":594,"region":169,"url":595,"description":596,"useCases":597,"indexable":239},"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":599,"label":600,"issuer":601,"region":313,"url":602,"description":603,"useCases":76,"indexable":239},"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":605,"label":606,"issuer":174,"region":169,"url":607,"description":608,"useCases":609,"indexable":239},"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":611,"label":612,"issuer":174,"region":169,"url":613,"description":614,"useCases":609,"indexable":239},"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":616,"label":617,"issuer":618,"region":218,"url":619,"description":620,"useCases":621,"indexable":239},"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":623,"label":624,"issuer":174,"region":169,"url":625,"description":626,"useCases":627,"indexable":239},"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":629,"label":630,"issuer":631,"region":218,"url":632,"description":633,"useCases":627,"indexable":239},"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":635,"label":636,"issuer":637,"region":313,"url":638,"description":639,"useCases":627,"indexable":239},"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":641,"label":642,"issuer":174,"region":169,"url":643,"description":644,"useCases":645,"indexable":239},"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":647,"label":648,"issuer":649,"region":218,"url":650,"description":651,"useCases":645,"indexable":239},"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":653,"label":654,"issuer":568,"region":357,"url":655,"description":656,"useCases":657,"indexable":239},"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":659,"label":660,"issuer":174,"region":169,"url":661,"description":662,"useCases":657,"indexable":239},"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":664,"label":665,"issuer":174,"region":169,"url":666,"description":667,"useCases":657,"indexable":239},"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":669,"label":670,"issuer":671,"region":169,"url":672,"description":673,"useCases":674,"indexable":239},"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":676,"label":677,"issuer":678,"region":218,"url":679,"description":680,"useCases":681,"indexable":239},"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":161,"label":683,"issuer":174,"region":169,"url":684,"description":685,"useCases":681,"indexable":239},"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":687,"label":688,"issuer":174,"region":169,"url":689,"description":690,"useCases":691,"indexable":239},"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.",6,{"id":693,"label":694,"issuer":695,"region":696,"url":697,"description":698,"useCases":75,"indexable":239},"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":700,"label":701,"issuer":702,"region":169,"url":703,"description":704,"useCases":235,"indexable":239},"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":706,"label":707,"issuer":708,"region":169,"url":709,"description":710,"useCases":235,"indexable":239},"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":712,"label":713,"issuer":714,"region":357,"url":715,"description":716,"useCases":717,"indexable":239},"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.",3,{"id":719,"label":720,"issuer":174,"region":169,"url":721,"description":722,"useCases":717,"indexable":239},"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":724,"label":725,"issuer":726,"region":218,"url":727,"description":728,"useCases":717,"indexable":239},"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.",1790598299469]