[{"data":1,"prerenderedAt":668},["ShallowReactive",2],{"uc-aerial-imagery-property-risk-scoring":3,"uc-regulations":447},{"useCase":4,"evidence":215,"blitsAiDeployments":321,"benchmarks":322,"indicative":329,"related":332,"indexability":445,"includeUnpublished":221},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":42,"valueDrivers":43,"kpis":48,"indicativeValue":53,"macroEstimates":91,"feasibility":92,"implementation":106,"risk":152,"blitsAi":189,"faq":191,"related":204,"datePublished":210,"dateModified":210,"lastVerified":210,"changelog":211,"slug":214},"AI aerial imagery risk scoring for property insurance underwriting","Aerial imagery property risk scoring","AI aerial imagery for property underwriting","AI scores roof condition and wildfire risk from aerial imagery at quote. Florida Peninsula says good CAPE roof ratings stayed within its tolerance 95% of the time.","published","AI property risk scoring reads aerial imagery to rate roof condition, wildfire exposure and other building features at quote, so underwriters inspect fewer homes. It turns an address into a score and a set of flags that feed the insurer's eligibility, inspection and pricing rules, and the underwriter or the rules engine keeps the decision.",[12,13,14,15],"roof condition scoring","aerial underwriting","geospatial property intelligence","AI property inspection",[17],"insurance",[19,20],"underwriting","risk-management",[22,23],"computer-vision","prediction-and-scoring",[25,26],"api","internal-tools","back-office","assist","early-adopters","property-underwriting","A homeowners or commercial property insurer has to know the condition of the building it insures.\nThe cheapest source is the application, which the applicant fills in from memory, and the usual\ncheck is a physical or drive by inspection ordered after the policy is bound or at renewal.\nInspections cost money and time, they can leave customers with a poor experience, and they cover only part of the book.\nFlorida Peninsula's Director of Underwriting describes the budget pressure: a third of its in force\nbook is inspected again every year on a rotating basis, at around $35 a physical inspection.\n\nThe roof is the usual proxy for the rest of the structure, yet the standard rating input is roof\nage, which CAPE Analytics says is not an accurate measure of how a roof performs. Wildfire has a\nsimilar gap, because legacy wildfire models paint large regions as high risk, according to\nZestyAI. AI that reads high resolution aerial imagery addresses both gaps: it rates each property\nfrom a recent image, without a visit, at the moment of the quote.",[33,38],{"statement":34,"sourceTitle":35,"sourceUrl":36,"year":37},"Florida Peninsula's Director of Underwriting says a third of its in force book is inspected again every year on a rotating basis, and that the cost per physical inspection is around $35.","AI-based Property Intelligence from CAPE Analytics Fuels A New Level of Underwriting Quality and Efficiency at Florida Peninsula","https:\u002F\u002Fcapeanalytics.com\u002Fflorida-peninsula-qa\u002F",2021,{"statement":39,"sourceTitle":40,"sourceUrl":41,"year":37},"ZestyAI says legacy wildfire models mark regions equal to 15% of all properties in California as high risk, while its Z-FIRE model estimates that 7% of properties in the state have a true high wildfire risk exposure.","California Department of Insurance Approves Filings Using ZestyAI Z-FIRE Model for Both Underwriting and Rating","https:\u002F\u002Fzesty.ai\u002Fnews\u002Fcalifornia-department-of-insurance-approves-filings-using-zesty-ai-z-fire-tm-model-for-both-underwriting-and-rating","1. **Match the address to a building.** At quote, renewal or on a portfolio refresh, the system\n   sends the address to the imagery provider, which resolves it to a building footprint and the most\n   recent aerial capture available for it.\n2. **Extract property features.** Computer vision models read the image for roof condition (missing\n   shingles, tarps, discoloration), roof geometry and covering material, and in wildfire models for\n   building materials and the vegetation around the structure.\n3. **Score the risk.** A model turns the features into a condition rating or a property level risk\n   score. Wildfire scores also account for slope, climate history and mitigation work the owner has\n   done, such as clearing vegetation or fitting fire resistant materials.\n4. **Apply the insurer's rules.** The underwriting system receives the score by API and compares it\n   with the guideline. A good roof can skip an inspection, a poor one can send the file to an\n   underwriter or an inspection order, and a severe one can make the property ineligible.\n5. **Check for recent changes.** Imagery lags reality. Florida Peninsula's Director of Underwriting\n   says that if the score is poor but a permit for a new roof was pulled the day before, the home is\n   inspected anyway.\n6. **Explain and monitor.** The score and the image are stored with the file so that the decision\n   can be explained to the broker, the policyholder and the regulator. Some insurers rescan the\n   portfolio to warn customers about changing conditions and to decide which renewals need a visit.",[44,45,46,47],"cost-to-serve","risk-reduction","speed","customer-experience",[49,50,51,52],"accuracy","cost-reduction","automation-rate","cycle-time-days",{"referenceOrg":54,"inputs":55,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A homeowners insurer with 300,000 policies in force",[56,61,67,73,80],{"key":57,"label":58,"low":59,"high":59,"unit":57,"note":60},"policies","Policies in force",300000,"The reference insurer.",{"key":62,"label":63,"low":64,"high":64,"unit":65,"note":66,"sourceUrl":36},"inspectedShare","Share of the book that is physically inspected each year",0.33,"fraction of policies","Florida Peninsula says a third of its book is inspected again every year (https:\u002F\u002Fcapeanalytics.com\u002Fflorida-peninsula-qa\u002F). Replace with your own inspection schedule.",{"key":68,"label":69,"low":70,"high":70,"unit":71,"note":72,"sourceUrl":36},"costPerInspection","Cost of one physical inspection",35,"USD per inspection","The cost Florida Peninsula quotes for a physical inspection (around $35, https:\u002F\u002Fcapeanalytics.com\u002Fflorida-peninsula-qa\u002F). Replace with your vendor's price; a fuller inspection costs more, which raises the saving.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"avoidedShare","Share of those inspections the imagery score makes unnecessary",0.2,0.3,"fraction of inspections","Editorial range, not a measured result. The only quantified pointer is a forecast. Florida Peninsula insures about 150,000 homes (2020 release) and inspects a third again of them each year, so about 50,000. Its Director of Underwriting says the roof ratings could easily lead to \"another 10,000 properties we don't need to inspect each year\", which implies roughly 20% of those inspections. The range starts at that forecast and allows for more; below about 17% the imagery fee outweighs the saving. Measure the share in a pilot on your own book.",{"key":81,"label":82,"low":83,"high":83,"unit":84,"note":85},"dataCost","Imagery and scoring fee per policy and year",2,"USD per policy","Editorial assumption. Replace with the price in your contract.","policies * (inspectedShare * costPerInspection * avoidedShare - dataCost)","USD","per year","Net annual inspection spend avoided","Leaves out the loss prevention effect of finding bad roofs earlier, premium changes from better pricing, the cost of integration and of handling disputed scores, and the inspections the imagery adds when it flags a property that would not have been visited.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"medium","The scoring comes from a specialist provider by API, so the build is the integration. It needs address matching, a write back into the quote and underwriting system, inspection and eligibility rules the underwriting committee signs off, and a way to keep the image and score with each file. The two reported rollouts describe an initial limited rollout (Zurich North America) and 12 months from integration to a review (Florida Peninsula).",[96,97,98,99],"Clean property addresses and geocodes for the in force book and for new quotes","Written inspection and eligibility guidelines that can be encoded as rules, by roof condition and hazard class","Historical inspection results for a sample of properties, to compare against the imagery score","Records of permits, claims and prior repairs that can override a stale image",[101,102,103,104,105],"Imagery and property intelligence provider, by API","Quote, rating and underwriting workbench, where the score is shown and used","Inspection ordering and vendor management system","Policy administration system, for renewal and portfolio rescans","Document storage for the image, the score and the rule version used",{"steps":107,"guardrails":126,"humanInTheLoop":132,"kpisToInstrument":133,"failureModes":139},[108,111,114,117,120,123],{"title":109,"detail":110},"Benchmark providers on your own properties","Send the same sample of properties to each provider and compare the ratings with your own physical inspections, as Florida Peninsula did. Decide the pass mark before you look at results, for example the share of good rated roofs that your inspectors also judge acceptable.",{"title":112,"detail":113},"Start with inspection triage, not decisions","Use the score first to decide which properties need an inspection, in new business and at renewal. Keep declines and price changes with underwriters until the error rate on your book is known.",{"title":115,"detail":116},"Encode the guideline as visible rules","Write what a good, fair, poor and severe rating does in each line and territory as rules that underwriting management can read and version. Add the override cases, such as a recent permit or a recent claim.",{"title":118,"detail":119},"Fix the image age problem","Record the capture date next to every score and set a maximum age per use. A score from an image taken before a storm or a roof replacement should send the file to a human.",{"title":121,"detail":122},"Run a controlled comparison before widening","Hold out a sample that is inspected regardless of the score, and compare claims and inspection findings for scored and unscored properties over a renewal cycle.",{"title":124,"detail":125},"Add the portfolio view last","Once quote time scoring works, rescan the renewal book to rank properties for inspection and to warn policyholders about conditions they can fix.",[127,128,129,130,131],"A policyholder or broker can ask for the image, the score and the rule that applied, and can dispute it","No automatic non renewal or decline from an imagery score alone","Every score stores its image capture date, provider and model version","Wildfire and condition models tested for unfair discrimination, including proxy effects through geography, before use in rating","Imagery limited to the insured address and its surroundings, with no use for other purposes","Underwriters decide on referrals, ineligible ratings and disputed scores, and can order an inspection that overrides the score. Underwriting management owns the rating thresholds and reviews a monthly sample of properties that skipped inspection, compared with later inspection or claim findings.",[134,135,136,137,138],"Share of quotes and renewals that skip an inspection, and the claims experience of that group","Agreement between imagery rating and physical inspection on a held out sample","Inspection spend per policy, before and after","Quote to bind time for properties scored at quote","Disputes and overrides per thousand scored properties",[140,143,146,149],{"title":141,"detail":142},"Stale or wrong imagery","A roof replaced last month still scores as poor, and a storm last week is not yet in the image. Show the capture date, check permits and claims, and send contradictions to a person.",{"title":144,"detail":145},"Rating drives non renewal without explanation","An adverse decision that the customer cannot see or contest damages trust and invites regulatory attention. Keep the image and rule behind each decision and give a route to challenge it.",{"title":147,"detail":148},"Wildfire score used as a blunt exclusion","A property level score is meant to separate good risks from bad ones inside a hazard zone. Applying it as a cut off for the whole zone brings back the problem of regional models.",{"title":150,"detail":151},"Provider lock in on the only copy of the data","If the scores are not stored with the policy, changing provider restarts your inspection programme. Store the features and the version, and keep a held out sample for benchmarking.",{"euAiAct":153,"regulations":156,"guidance":163,"controls":181,"incidents":188},{"tier":154,"basis":155},"minimal","Annex III point 5(c) lists risk assessment and pricing of natural persons in life and health insurance. Property condition and wildfire scoring for homeowners or commercial property is not listed. It would move up to high risk if the same scores were used to assess or price life or health cover for individuals.",[157,158,159,160,161,162],"eu-ai-act","gdpr","solvency-ii","dora","nist-ai-rmf","iso-42001",[164,170,175],{"title":165,"issuer":166,"region":167,"url":168,"note":169},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","europe","https:\u002F\u002Fwww.eiopa.europa.eu\u002Fpublications\u002Fopinion-artificial-intelligence-governance-and-risk-management_en","Sets out how existing insurance rules on governance, data quality, fairness and human oversight apply to AI systems that are not high risk under the AI Act, such as property risk scoring.",{"title":171,"issuer":172,"region":167,"url":173,"note":174},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 5(c) names life and health insurance risk assessment and pricing of natural persons; property insurance is outside it.",{"title":176,"issuer":177,"region":178,"url":179,"note":180},"SB21-169, Protecting Consumers from Unfair Discrimination in Insurance Practices","Colorado Division of Insurance","north-america","https:\u002F\u002Fdoi.colorado.gov\u002Ffor-consumers\u002Fsb21-169-protecting-consumers-from-unfair-discrimination-in-insurance-practices","Requires insurers to test external data, algorithms and predictive models for unfair discrimination. Its work so far focuses on life, health and private passenger auto insurance, and shows the testing that regulators ask for from models that use external data.",[182,183,184,185,186,187],"AI inventory entry per provider and model, with an accountable underwriting owner","Documented rating thresholds and override rules with version history and sign off","Stored image, capture date, score and rule version for every decision","Periodic bias and proxy testing of condition and wildfire scores on the insurer's own book","Complaint and dispute log reviewed with underwriting and compliance","Third party risk assessment of the imagery provider under DORA",[],{"howToBuild":190},"The image analysis itself comes from a specialist provider's API, and Blits.ai does not replace\nit. On Blits.ai the decision around it runs as an **agentic workflow**, started from the quote or\nrenewal system through an **API token**. **Custom functions** call the imagery provider,\nthe permit and claims lookups and the inspection ordering system, and a **SQL knowledge base**\ngives the agent read access to inspection history and prior scores. The agent applies the\ninspection and eligibility guideline, which sits in the **knowledge base** with **hybrid\nretrieval**, and returns a decision ready note with the rule that applied.\n\nReferrals and ineligible ratings go through **human in the loop approval** to an underwriter, and\nevery run keeps a full **audit trail and trace**. **Test suites** replay a labelled sample of past\nproperties before a guideline or provider changes, and **monitors** run scheduled checks\nagainst the workflow and alert on failures, while run history with **analytics** shows the share\nof properties that skipped inspection. The platform is **model agnostic** and can run in **EU or\nUAE data residency** regions.",[192,195,198,201],{"question":193,"answer":194},"How accurate is roof condition scoring from aerial imagery?","Florida Peninsula compared several providers with its own physical inspections and reports that every property CAPE Analytics rated as having a good roof was within its risk tolerance more than 95% of the time. That is one insurer's evaluation reported on the vendor's site, so test it on your own book and your own definition of tolerance.",{"question":196,"answer":197},"Does imagery replace physical inspections?","Not in the cases on this page. CAPE Analytics and Florida Peninsula describe imagery as a way to reduce unnecessary inspections, with targeted in person checks where the score or other data calls for one. Zurich North America describes the imagery as information underwriters see alongside their other data while they assess a risk.",{"question":199,"answer":200},"Is wildfire scoring from imagery allowed for underwriting?","In California the department of insurance approved filings that use ZestyAI's Z-FIRE model for underwriting and rating in 2021, first for Metropolitan Direct and then for Farmers Insurance (underwriting only), according to ZestyAI. The California FAIR Plan later selected the same model for every new quote. Check the filing rules of each state or country before use.",{"question":202,"answer":203},"Is aerial imagery scoring high risk under the EU AI Act?","Property and commercial lines scoring is not on the Annex III list, which names risk assessment and pricing of natural persons in life and health insurance. Existing insurance rules on governance, data quality and fair treatment still apply, as EIOPA's 2025 opinion explains.",[205,206,207,208,209],"catastrophe-exposure-assessment","underwriting-risk-assessment-copilot","commercial-underwriting-submission-triage","property-valuation-support","insurance-renewal-and-retention","2026-10-13",[212],{"date":210,"note":213},"First published","aerial-imagery-property-risk-scoring",[216,247,267,295],{"title":217,"useCases":218,"organization":219,"vendors":223,"summary":229,"stage":230,"year":231,"channels":232,"languages":233,"metrics":235,"outcomeDisclosed":221,"sources":236,"verification":242,"grade":244,"id":245,"organizationSlug":246},"Zurich North America: aerial imagery and AI roof scores in the Middle Market underwriting platform",[214],{"name":220,"anonymized":221,"country":222,"region":178,"industry":17},"Zurich North America",false,"US",[224,227],{"name":225,"role":226},"Nearmap","platform",{"name":228,"role":226},"Betterview","In October 2025 Zurich North America and Nearmap announced that Nearmap's AI analyzed aerial imagery and roof condition scores are delivered through the Betterview platform directly into Zurich's U.S. Middle Market underwriting platform. Underwriters see regularly updated imagery, roof condition scores and signs of deferred maintenance while they assess a risk. In an initial limited rollout, underwriters found risks that conventional methods may have missed, and the results led Zurich to roll the integration out across its U.S. Middle Market underwriting offices. The release gives no figures.","production",2025,[25,26],[234],"en",[],[237],{"url":238,"title":239,"publisher":240,"date":241},"https:\u002F\u002Finsurance-canada.ca\u002F?p=84141","Nearmap and Zurich advance property underwriting with AI integration","Insurance-Canada.ca (release issued by Zurich North America and Nearmap)","2025-10-13",{"level":243,"checkedAt":210},"source-verified","C","zurich-north-america-nearmap-roof-scoring",null,{"title":248,"useCases":249,"organization":250,"vendors":252,"summary":255,"stage":256,"year":37,"channels":257,"languages":258,"metrics":259,"outcomeDisclosed":221,"sources":260,"verification":265,"grade":244,"id":266,"organizationSlug":246},"California FAIR Plan: property level wildfire risk scores for every quote",[214],{"name":251,"anonymized":221,"country":222,"region":178,"industry":17},"California FAIR Plan Association",[253],{"name":254,"role":226},"ZestyAI","The California FAIR Plan, the state's insurer of last resort for property, selected ZestyAI's Z-FIRE wildfire risk model in 2021 after evaluating several models. Z-FIRE scores individual properties from features such as building materials, vegetation density and ease of fire suppression, with aerial imagery as one input. Under a five year agreement the FAIR Plan uses it to assess wildfire risk for every new quote and every property in its portfolio, to price more accurately and to reward homeowners who harden their homes. The plan's president says Z-FIRE outperformed the other models evaluated; no result figures were published.","announced",[25,26],[234],[],[261],{"url":262,"title":263,"publisher":254,"date":264},"https:\u002F\u002Fwww.zesty.ai\u002Fnews\u002Fthe-california-fair-plan-association-enters-new-partnership-with-zesty-ai-to-better-assess-wildfire-risk-leveraging-aerial-imagery-and-artificial-intelligence","The California FAIR Plan Association Enters New Partnership with ZestyAI to Better Assess Wildfire Risk Leveraging Aerial Imagery and Artificial Intelligence","2021-08-11",{"level":243,"checkedAt":210},"california-fair-plan-zestyai-wildfire-risk",{"title":268,"useCases":269,"organization":270,"vendors":272,"summary":275,"stage":230,"year":37,"channels":276,"languages":277,"metrics":278,"outcomeDisclosed":286,"sources":287,"verification":293,"grade":244,"id":294,"organizationSlug":246},"Florida Peninsula Insurance: roof condition ratings from aerial imagery in underwriting",[214],{"name":271,"anonymized":221,"country":222,"region":178,"industry":17},"Florida Peninsula Insurance Company",[273],{"name":274,"role":226},"CAPE Analytics","Florida Peninsula, a Florida homeowners insurer, chose CAPE Analytics in 2020 to rate roof condition, roof geometry and roof covering from geospatial imagery at quote, so it could validate rating data and cut unnecessary physical inspections. Its Director of Underwriting describes a vendor evaluation in which each provider analyzed the same properties and the team compared the results with its own physical inspections. About 12 months after integration he calls the result \"phenomenal\": the underwriting system receives the roof score instantly, some homes are screened out on condition before an inspection is ordered, and the insurer planned to extend the scores to its renewal book.",[25,26],[234],[279],{"kpi":49,"value":280,"unit":281,"qualifier":282,"baseline":283,"claimant":284,"quote":285,"sourceUrl":36},95,"percent","at-least","The insurer's own physical inspections of the same properties. The figure covers only properties CAPE rated as having a good roof, so it measures one class, not overall accuracy.","organization","Every property with a good roof condition rating from CAPE was within our risk tolerance more than 95% of the time.",true,[288,289],{"url":36,"title":35,"publisher":274},{"url":290,"title":291,"publisher":274,"date":292},"https:\u002F\u002Fcapeanalytics.com\u002Fresources\u002Fflorida-peninsula-insurance-selects-cape","Florida Peninsula Insurance Selects Cape Analytics for Aerial Underwriting and Portfolio Monitoring Intelligence","2020-09-09",{"level":243,"checkedAt":210},"florida-peninsula-cape-analytics-roof-condition",{"title":296,"useCases":297,"organization":298,"vendors":300,"summary":302,"stage":230,"year":303,"channels":304,"languages":305,"metrics":306,"outcomeDisclosed":221,"sources":307,"verification":319,"grade":244,"id":320,"organizationSlug":246},"CSAA Insurance Group: roof condition scoring from imagery for home underwriting",[214],{"name":299,"anonymized":221,"country":222,"region":178,"industry":17},"CSAA Insurance Group",[301],{"name":274,"role":226},"CSAA Insurance Group, a AAA insurer, partnered with CAPE Analytics in 2019 to assess the condition of member homes from geospatial imagery instead of ordering an inspection for each one. The insurer used the roof condition rating to help members repair damaged roofs, to price home policies more accurately at renewal and for new business, and to skip inspections of good roofs. No numbers were published; CSAA's managing principal of core innovation describes the aim as consistent and objective underwriting decisions.",2019,[25,26],[234],[],[308,311,314],{"url":309,"title":310,"publisher":274},"https:\u002F\u002Fcapeanalytics.com\u002Fcsaa\u002F","Insurance Case Study: CSAA Insurance Group",{"url":312,"title":313,"publisher":274},"https:\u002F\u002Fcapeanalytics.com\u002Fresources\u002Fcsaa-insurance-group-partners-with-cape-analytics-to-improve-home-inspections\u002F","CSAA Insurance Group Partners with Cape Analytics to Improve Home Inspections",{"url":315,"title":316,"publisher":317,"date":318},"https:\u002F\u002Fcarriermanagement.com\u002Fnews\u002F2019\u002F07\u002F26\u002F195886.htm","The Latest Launches From CSAA Insurance Group and Aon","Carrier Management","2019-07-26",{"level":243,"checkedAt":210},"csaa-cape-analytics-roof-condition",0,[323],{"kpi":49,"label":324,"unit":281,"aggregate":286,"higherIsBetter":286,"n":325,"nUpTo":321,"median":280,"min":280,"max":280,"byClaimant":326,"vendorOnly":221,"points":327},"Accuracy",1,{"organization":325,"vendor":321,"regulator":321,"independent":321},[328],{"evidenceId":294,"organization":271,"value":280,"qualifier":282,"claimant":284,"grade":244,"pooled":286},{"low":330,"high":331},93000.00000000001,439500.0000000001,[333,354,383,404,429],{"slug":205,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":338,"patterns":341,"audience":27,"autonomy":28,"adoptionStage":344,"segment":345,"evidenceCount":346,"publicEvidenceCount":346,"organizations":347,"bestGrade":352,"headline":246,"lastVerified":353,"indexable":286},"AI for catastrophe and exposure assessment in insurance","Catastrophe exposure assessment","AI catastrophe exposure assessment is AI that turns satellite and geospatial data into a fast, portfolio wide view of which policies and properties are exposed to a catastrophe. It covers before and immediately after the event, so exposure managers and claims teams can quantify the loss, prioritize response and reach affected customers first, without waiting for ground surveys.",[17],[339,20,340],"claims","operations",[22,342,23,343],"anomaly-detection","agentic-workflow","emerging","catastrophe-and-exposure",4,[348,349,350,351],"Brit","Security First Insurance","Sompo Japan Insurance","Suncorp Group","B","2026-09-30",{"slug":206,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":359,"patterns":360,"audience":364,"autonomy":365,"adoptionStage":29,"segment":19,"evidenceCount":366,"publicEvidenceCount":366,"organizations":367,"bestGrade":352,"headline":375,"lastVerified":382,"indexable":286},"AI copilot for underwriting risk assessment","Underwriting risk assessment copilot","An underwriting risk assessment copilot is an AI tool that assembles everything relevant to a risk, such as the submission, loss history and underwriting guidelines, for an insurance underwriter. It also gathers third party data and public information, highlights exposures and gaps against the insurer's underwriting guidelines, and drafts the underwriting narrative or referral note, while the underwriter makes and signs every decision.",[17],[19,20],[361,362,363,343],"rag-knowledge-assistant","summarization","content-generation","employee-facing","copilot",8,[368,369,370,371,372,373,374,220],"Accelerant Holdings","American International Group","Arch Capital Group","Bowhead Specialty","Generali Global Corporate & Commercial","Hiscox","Skyward Specialty Insurance Group",{"kpi":376,"label":377,"unit":281,"n":325,"nUpTo":321,"kind":378,"value":379,"qualifier":380,"claimant":381,"organization":372,"vendorReported":286},"processing-time-reduction","Cycle time reduction","reported",50,"exact","vendor","2026-09-26",{"slug":207,"title":384,"shortTitle":385,"definition":386,"status":9,"industries":387,"functions":388,"patterns":389,"audience":27,"autonomy":392,"adoptionStage":29,"segment":19,"evidenceCount":393,"publicEvidenceCount":393,"organizations":394,"bestGrade":352,"headline":400,"lastVerified":403,"indexable":286},"AI for commercial underwriting submission intake and triage","Underwriting submission triage","AI that reads incoming broker submissions for commercial insurance so underwriters open a complete, prioritized file instead of an inbox. It reads emails, applications, schedules of values, loss runs and supplements, extracts the risk data into a structured record, checks clearance and appetite, and enriches and ranks the risk with internal and third party data.",[17],[19,340],[390,391,23,343],"document-processing","classification-and-routing","supervised-agent",9,[369,395,396,372,373,397,398,399,374],"AXIS Capital","CNA Financial","Kinsale Capital Group","Markel","Paragon Insurance Group",{"kpi":49,"label":324,"unit":281,"n":325,"nUpTo":321,"kind":378,"value":401,"qualifier":402,"claimant":284,"organization":399,"vendorReported":221},98,"approximately","2026-09-27",{"slug":208,"title":405,"shortTitle":406,"definition":407,"status":9,"industries":408,"functions":412,"patterns":415,"audience":364,"autonomy":392,"adoptionStage":416,"segment":417,"evidenceCount":366,"publicEvidenceCount":366,"organizations":418,"bestGrade":427,"headline":246,"lastVerified":428,"indexable":286},"AI support for property valuation and appraisal","Property valuation support","AI property valuation support, most often an automated valuation model, estimates a property's market value from comparable sales, characteristics and location data. It either offers to replace a full appraisal within set limits, or gives a professional valuer a first pass estimate, the closest comparable sales and a reliability score, so the valuer's time goes to properties that need a person's judgment.",[409,410,411],"real-estate","banking","government",[413,414,340],"lending-and-credit","case-management",[23,391],"mainstream","lending",[419,420,421,422,423,424,425,426],"Fannie Mae","Freddie Mac","Redfin","Riverside County Assessor-County Clerk-Recorder","Rocket Companies","Valuation Office Agency","Vurderingsstyrelsen","Zillow Group","A","2026-09-28",{"slug":209,"title":430,"shortTitle":431,"definition":432,"status":9,"industries":433,"functions":434,"patterns":437,"audience":27,"autonomy":365,"adoptionStage":344,"segment":440,"evidenceCount":441,"publicEvidenceCount":441,"organizations":442,"bestGrade":352,"headline":246,"lastVerified":382,"indexable":286},"AI for insurance renewal processing and customer retention","Renewal and retention","Renewal and retention AI prepares and runs the insurance renewal cycle, digitizing renewal submissions and changes in risk for underwriters and flagging policies at risk of lapsing or leaving. It also prepares the renewal conversation and answers customers' renewal questions, while renewal prices stay governed by the insurer's pricing rules and fair value obligations.",[17],[19,435,436],"customer-service","sales",[23,390,438,439],"conversational-agent","recommendation-and-personalization","distribution",3,[373,443,444],"Nsure.com","Zurich Insurance Group",{"indexable":286,"reasons":446},[],[448,453,458,465,471,478,483,490,498,505,512,518,524,531,538,545,551,557,564,569,573,580,586,593,598,603,609,614,620,626,634,639,645,651,657,662],{"id":157,"label":449,"issuer":172,"region":167,"url":450,"description":451,"useCases":452,"indexable":286},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":158,"label":454,"issuer":172,"region":167,"url":455,"description":456,"useCases":457,"indexable":286},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":162,"label":459,"issuer":460,"region":461,"url":462,"description":463,"useCases":464,"indexable":286},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":161,"label":466,"issuer":467,"region":178,"url":468,"description":469,"useCases":470,"indexable":286},"NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":472,"label":473,"issuer":474,"region":167,"url":475,"description":476,"useCases":477,"indexable":286},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":160,"label":479,"issuer":172,"region":167,"url":480,"description":481,"useCases":482,"indexable":286},"DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":484,"label":485,"issuer":486,"region":167,"url":487,"description":488,"useCases":489,"indexable":286},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":491,"label":492,"issuer":493,"region":494,"url":495,"description":496,"useCases":497,"indexable":286},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":499,"label":500,"issuer":501,"region":494,"url":502,"description":503,"useCases":504,"indexable":286},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":506,"label":507,"issuer":508,"region":178,"url":509,"description":510,"useCases":511,"indexable":286},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":513,"label":514,"issuer":172,"region":167,"url":515,"description":516,"useCases":517,"indexable":286},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":519,"label":520,"issuer":521,"region":461,"url":522,"description":523,"useCases":517,"indexable":286},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":525,"label":526,"issuer":527,"region":167,"url":528,"description":529,"useCases":530,"indexable":286},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":532,"label":533,"issuer":534,"region":178,"url":535,"description":536,"useCases":537,"indexable":286},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":539,"label":540,"issuer":541,"region":461,"url":542,"description":543,"useCases":544,"indexable":286},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":546,"label":547,"issuer":172,"region":167,"url":548,"description":549,"useCases":550,"indexable":286},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":552,"label":553,"issuer":554,"region":178,"url":555,"description":556,"useCases":550,"indexable":286},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":558,"label":559,"issuer":560,"region":178,"url":561,"description":562,"useCases":563,"indexable":286},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":565,"label":566,"issuer":172,"region":167,"url":567,"description":568,"useCases":563,"indexable":286},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":159,"label":570,"issuer":172,"region":167,"url":571,"description":572,"useCases":563,"indexable":286},"Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":574,"label":575,"issuer":576,"region":178,"url":577,"description":578,"useCases":579,"indexable":286},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":581,"label":582,"issuer":583,"region":461,"url":584,"description":585,"useCases":579,"indexable":286},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":587,"label":588,"issuer":589,"region":167,"url":590,"description":591,"useCases":592,"indexable":286},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":594,"label":595,"issuer":172,"region":167,"url":596,"description":597,"useCases":592,"indexable":286},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":599,"label":600,"issuer":172,"region":167,"url":601,"description":602,"useCases":592,"indexable":286},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":604,"label":605,"issuer":493,"region":494,"url":606,"description":607,"useCases":608,"indexable":286},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":610,"label":611,"issuer":172,"region":167,"url":612,"description":613,"useCases":608,"indexable":286},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":615,"label":616,"issuer":617,"region":178,"url":618,"description":619,"useCases":366,"indexable":286},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":621,"label":622,"issuer":172,"region":167,"url":623,"description":624,"useCases":625,"indexable":286},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":627,"label":628,"issuer":629,"region":630,"url":631,"description":632,"useCases":633,"indexable":286},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":635,"label":636,"issuer":172,"region":167,"url":637,"description":638,"useCases":346,"indexable":286},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":640,"label":641,"issuer":642,"region":167,"url":643,"description":644,"useCases":346,"indexable":286},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":646,"label":647,"issuer":648,"region":167,"url":649,"description":650,"useCases":346,"indexable":286},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":652,"label":653,"issuer":654,"region":494,"url":655,"description":656,"useCases":441,"indexable":286},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":658,"label":659,"issuer":172,"region":167,"url":660,"description":661,"useCases":441,"indexable":286},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":663,"label":664,"issuer":665,"region":178,"url":666,"description":667,"useCases":441,"indexable":286},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904353150]