[{"data":1,"prerenderedAt":798},["ShallowReactive",2],{"uc-commercial-underwriting-submission-triage":3,"uc-regulations":592},{"useCase":4,"evidence":214,"blitsAiDeployments":460,"benchmarks":461,"indicative":492,"related":495,"indexability":590,"includeUnpublished":220},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":25,"audience":29,"autonomy":30,"adoptionStage":31,"segment":18,"problem":32,"problemStats":33,"howItWorks":44,"valueDrivers":45,"kpis":50,"indicativeValue":58,"macroEstimates":93,"feasibility":98,"implementation":112,"risk":158,"blitsAi":188,"faq":190,"related":203,"datePublished":209,"dateModified":209,"lastVerified":209,"changelog":210,"slug":213},"AI for commercial underwriting submission intake and triage","Underwriting submission triage","AI for commercial underwriting submission triage","AI reads commercial broker submissions, extracts the data, checks appetite and ranks each risk. AIG prepares submissions for underwriter review within one day.","published","AI that reads incoming broker submissions for commercial insurance (emails, applications, schedules of values, loss runs and supplements), extracts the risk data into a structured record, checks clearance and appetite, enriches the risk with internal and third party data and ranks it, so underwriters open a complete, prioritized file instead of an inbox.",[12,13,14],"submission ingestion and clearance","broker submission triage","underwriting intake automation",[16],"insurance",[18,19],"underwriting","operations",[21,22,23,24],"document-processing","classification-and-routing","prediction-and-scoring","agentic-workflow",[26,27,28],"email","api","internal-tools","back-office","supervised-agent","early-adopters","Commercial and specialty insurers receive far more broker submissions than their underwriters can\nread. Each one arrives as an email with attachments in different formats: an application, a\nschedule of locations, several years of loss runs, financials and supplementals. Before anyone can\njudge the risk, someone has to clear it (is it a duplicate, is another broker already on it), check\nit against appetite, rekey the data into the rating and policy systems and pull third party data.\n\nThat work is slow and falls on expensive people. Senior underwriters triage their own inboxes, so\nin appetite business waits behind business the insurer will decline, broker turnaround slips and\nsome submissions are never looked at. Insurers treat speed as part of what they sell to brokers:\nKinsale presents service as a competitive advantage to investors and reports an average\nsubmission clearance time of 9 minutes.",[34,39],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"Hiscox says extracting key data from email submissions is a manual process that can typically take up to three days in today's insurance operating model.","Hiscox and Google Cloud Collaborate on AI in lead underwriting for the London Market","https://www.hiscoxgroup.com/news/press-releases/2023/12-12-23",2023,{"statement":40,"sourceTitle":41,"sourceUrl":42,"year":43},"Paragon Insurance Group's CTO told Kalepa that one of its programs, receiving around 50,000 submissions a year, was only looking at about 30% of its submissions.","How Paragon Doubled Its Quote-to-Bind Rate and Achieved 99% Submission Accuracy with Kalepa","https://www.kalepa.com/case-studies/paragon-doubled-quote-to-bind-rate",2026,"1. **Ingest.** Submissions arrive in a shared mailbox, a broker portal or a placing platform. The\n   system splits the email and attachments, classifies each document (application, loss run,\n   schedule, financials) and reads scanned and native files.\n2. **Extract to a schema.** A model extracts the fields the underwriting workbench needs (insured,\n   address, class of business, revenue, limits, loss history) into a fixed schema, with a\n   confidence score and a pointer to the page each value came from.\n3. **Clear and classify.** The record is matched against existing accounts and open submissions to\n   catch duplicates and broker conflicts, and the business is classified into the insurer's\n   industry codes.\n4. **Check appetite and enrich.** Rules and models compare the risk to the written appetite and\n   add internal history and approved third party data (firmographics, hazard scores, news).\n5. **Rank and route.** Each submission gets a priority (fit, likelihood to bind, broker\n   importance) and goes to the right underwriter or team; clear declines are drafted for a human\n   to confirm.\n6. **Underwriter decides.** The underwriter opens a decision ready file, corrects any extracted\n   field (the correction is logged and fed back) and makes every quote or decline decision.",[46,47,48,49],"speed","employee-productivity","revenue-growth","cost-to-serve",[51,52,53,54,55,56,57],"processing-time-reduction","productivity-gain","accuracy","conversion-rate-uplift","interactions-handled","cycle-time-days","automation-rate",{"referenceOrg":59,"inputs":60,"formula":88,"currency":89,"period":90,"resultLabel":91,"caveat":92},"A commercial insurer receiving 40,000 broker submissions a year",[61,67,74,81],{"key":62,"label":63,"low":64,"high":64,"unit":65,"note":66},"submissions","Broker submissions per year",40000,"submissions per year","The reference insurer.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"minutesPerSubmission","Manual intake and triage time per submission",20,40,"minutes per submission","Editorial assumption for reading, clearance, appetite check and rekeying. Replace with a time study of your own intake.",{"key":75,"label":76,"low":77,"high":78,"unit":79,"note":80},"timeSavedShare","Share of intake time the AI removes",0.4,0.7,"fraction of intake time","Editorial assumption. The evidence on this page measures elapsed time rather than effort (Sixfold reports a 50% cut in turnaround for Generali GC&C cyber submissions; AIG says submissions are prepared for underwriter review within one day, against a submission to quote process of about three to four weeks), so replace this with a time study from your own pilot.",{"key":82,"label":83,"low":84,"high":85,"unit":86,"note":87},"costPerHour","Fully loaded cost of an underwriting or assistant hour",60,100,"USD per hour","Editorial assumption. Replace with your own fully loaded cost.","submissions * minutesPerSubmission / 60 * timeSavedShare * costPerHour","USD","per year","Underwriting intake capacity released","Capacity released, not cash saved, unless headcount or outsourcing changes. It leaves out the usually larger effect of quoting more in appetite business faster (higher bind ratios), the cost of the platform and integration, and data licences for enrichment.",[94],{"statement":95,"sourceTitle":96,"sourceUrl":97,"year":43},"Evident reports that the 30 insurers in its AI Index for Insurance announced 37 new AI use cases in the second quarter of 2026, that underwriting and pricing was the fastest growing application area, and that insurers prioritized tasks that structure and route information, such as submission intake and triage, over pricing and portfolio management.","Evident: Insurance Use Case Trends Q2 2026","https://evidentinsights.com/insights/insurance-use-case-trends-q2-2026",{"complexity":99,"complexityNote":100,"dataPrerequisites":101,"integrations":106},"medium","Extraction from messy broker documents now works well enough to use; the work is in the schema, the clearance logic, the appetite rules and the write back into the underwriting workbench and policy system. Reported rollouts are measured in weeks per team: Sixfold's case study gives about six weeks for AXIS, Skyward Specialty reports an average deployment timeline of 8 to 10 weeks, and Paragon had its full submission inbox running through Kalepa within 90 days.",[102,103,104,105],"A written appetite per line of business, precise enough to encode as rules","A target data schema for each line (the fields the rating and policy systems need)","A labelled sample of past submissions with the correct extracted values and outcomes (quoted, declined, bound)","Account and broker master data for clearance and duplicate checks",[107,108,109,110,111],"Submission mailboxes, broker portals or placing platforms","Underwriting workbench or CRM where the file is opened","Rating engine and policy administration system","Approved third party data providers (firmographics, hazard and property data)","Document storage for the original submission and extraction audit trail",{"steps":113,"guardrails":132,"humanInTheLoop":138,"kpisToInstrument":139,"failureModes":145},[114,117,120,123,126,129],{"title":115,"detail":116},"Start with one line and its inbox","Pick a high volume line with a clear appetite (small commercial property, cyber, E&S casualty) and measure today's baseline: submissions per week, share reviewed, time to first response, quote and bind ratios.",{"title":118,"detail":119},"Fix the schema before the model","Agree with underwriters which fields matter and how they are defined. Extraction quality is judged against this schema, so vague fields produce endless disputes about accuracy.",{"title":121,"detail":122},"Measure field accuracy on real submissions","Run the extractor on a few hundred historical submissions and compare with the values underwriters actually used. Set a confidence threshold per field below which a human must confirm.",{"title":124,"detail":125},"Encode appetite and clearance as reviewable rules","Keep hard rules (excluded classes, territories, limits) deterministic and visible to underwriting management; use models only for ranking and propensity, not for silent declines.",{"title":127,"detail":128},"Put the output where underwriters work","Deliver the enriched, ranked file inside the existing workbench with a link to the source page for every value. A separate screen gets ignored.",{"title":130,"detail":131},"Close the loop","Log every correction underwriters make and every quote, decline and bind outcome, and use them to retrain extraction and ranking each month.",[133,134,135,136,137],"No automatic declines at launch; declines are drafted by the system and confirmed by an underwriter","Every extracted value carries its source page and a confidence score","Appetite rules owned and signed off by underwriting management, with version control","Only approved third party data sources for enrichment, with licence and use conditions recorded","Personal data in submissions (named individuals, claimant details) masked before it reaches a model that does not need it","Underwriters make every quote, decline and referral decision and confirm low confidence fields. Underwriting management owns the appetite rules and reviews a weekly sample of auto ranked and declined submissions to check that good business is not being buried.",[140,141,142,143,144],"Share of submissions reviewed within one business day, before and after","Field level extraction accuracy on a weekly audited sample","Time from submission receipt to first broker response","Quote ratio and bind ratio for top ranked versus lower ranked submissions","Underwriter corrections per submission",[146,149,152,155],{"title":147,"detail":148},"Accuracy measured on the wrong thing","A single overall accuracy number hides that the fields that drive pricing (revenue, total insured value, loss history) are the ones that fail. Measure accuracy per critical field.",{"title":150,"detail":151},"Ranking that encodes yesterday's appetite","A propensity model trained on past binds keeps favouring classes the insurer is trying to exit. Retrain after every appetite change and let underwriting override the ranking.",{"title":153,"detail":154},"Silent declines and broker damage","Automated declines sent without a human check lose broker trust quickly when one is wrong. Keep a person on every decline until the error rate is proven low.",{"title":156,"detail":157},"Workbench integration left for later","If extracted data still has to be copied into the policy system, the time saving disappears. Plan the write back from the start.",{"euAiAct":159,"regulations":162,"guidance":169,"controls":181,"incidents":187},{"tier":160,"basis":161},"minimal","Intake and triage for commercial insurance is not listed in Annex III, which covers risk assessment and pricing of natural persons in life and health insurance. It moves up to high risk only if the same pipeline is used to assess or price life or health cover for individuals.",[163,164,165,166,167,168],"eu-ai-act","gdpr","dora","nist-ai-rmf","iso-42001","solvency-ii",[170,176],{"title":171,"issuer":172,"region":173,"url":174,"note":175},"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","Sets out how existing insurance legislation (governance, risk management, data quality, human oversight) applies to AI systems that are not high risk under the AI Act.",{"title":177,"issuer":178,"region":173,"url":179,"note":180},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https://artificialintelligenceact.eu/annex/3/","Point 5(c) lists life and health insurance risk assessment and pricing of natural persons; commercial lines triage is outside it.",[182,183,184,185,186],"AI inventory entry per line with an accountable underwriting owner","Documented appetite rules with version history and sign off","Audit trail linking each ranked or declined submission to the data and rule versions used","Weekly quality sample of extraction and triage outcomes","Third party and model provider risk assessment under DORA for hosted AI services",[],{"howToBuild":189},"On Blits.ai this runs as an **agentic workflow**, started through the **REST API** from the\nbroker portal or mailbox integration, or from a flow on the inbound **email channel** with a\ntrigger workflow block. The platform's document ingestion reads PDF, DOCX, XLSX and Outlook .msg\nfiles, and an **agent with structured output** extracts the fields into the insurer's schema. **Custom functions** call the clearance service, the appetite rules\nand approved data providers over REST, and a **SQL knowledge base** gives the agent read access to\naccount and loss history.\n\nDeclines and referrals go through **human in the loop approval** above a configurable threshold,\nso an underwriter confirms before anything reaches the broker. **PII masking** at the gateway,\nwith custom masking patterns per bot, masks identifiers such as email addresses, phone numbers\nand account or policy numbers before they reach a model. Every run has a full **audit trail and\ntrace**, and **test suites** replay a labelled set of past submissions before each change goes\nlive to track field accuracy. The\nplatform is model agnostic, so extraction and ranking can use different models, and it can run in\nEU or UAE data residency regions.",[191,194,197,200],{"question":192,"answer":193},"How accurate is AI extraction from broker submissions?","Good enough to use when measured per field and checked on low confidence values. Paragon's CTO says its submission extraction is around 98 to 99% accurate with Kalepa, better than its former manual team. That is a single figure from one deployment; accuracy can differ between applications, schedules and scanned loss runs, so measure it per field and per document type.",{"question":195,"answer":196},"Should the AI decline submissions on its own?","Not at first. AIG and Hiscox describe AI that extracts, enriches or prices while an underwriter reviews the output before anything goes to the broker, and Skyward Specialty says its Sixfold platform ranks and assesses submissions while keeping underwriters in the loop. Hard appetite rules can draft declines, but a person should confirm them until the error rate is proven.",{"question":198,"answer":199},"What does it do for brokers?","Faster answers. Sixfold reports that Generali GC&C cut turnaround for its distribution channels on cyber submissions by 50%, and AIG says its assistant prepares submissions for underwriter review within one day instead of a process it described as taking weeks.",{"question":201,"answer":202},"Is submission triage high risk under the EU AI Act?","For commercial lines, no: Annex III covers life and health insurance risk assessment and pricing for natural persons. Insurance supervisors still expect governance, data quality and human oversight under existing rules, as EIOPA's 2025 opinion on AI governance sets out.",[204,205,206,207,208],"underwriting-risk-assessment-copilot","intelligent-document-processing","correspondence-triage-and-routing","insurance-renewal-and-retention","insurance-broker-and-agent-assistant","2026-09-27",[211],{"date":209,"note":212},"First published","commercial-underwriting-submission-triage",[215,260,280,304,324,348,381,409,437],{"title":216,"useCases":217,"organization":218,"vendors":223,"summary":232,"stage":233,"year":234,"channels":235,"languages":236,"metrics":238,"outcomeDisclosed":248,"sources":249,"verification":254,"grade":257,"id":258,"organizationSlug":259},"AIG: AIG Underwriter Assistance for submission ingestion, prioritization and augmentation",[213,204],{"name":219,"anonymized":220,"country":221,"region":222,"industry":16},"American International Group",false,"US","north-america",[224,227,230],{"name":225,"role":226},"Anthropic","model-provider",{"name":228,"role":229},"Palantir","platform",{"name":231,"role":229},"Amazon Web Services","At its March 2025 investor day, AIG presented AIG Underwriter Assistance, a generative AI solution in production in Financial Lines that extracts data from broker and agent submissions, augments it with AIG and approved third party data, summarizes each submission and ranks submissions by appetite and propensity to bind, after which the underwriter analyzes the output and quotes. AIG describes the prior submission to quote process as taking about three to four weeks, with underwriters unable to review every submission, and says the assistant prepares submissions for review within one day. AIG frames the build around a human in the loop principle and was extending the same components to claims.","production",2025,[28,27],[237],"en",[239],{"kpi":56,"value":240,"unit":241,"qualifier":242,"period":243,"baseline":244,"claimant":245,"quote":246,"sourceUrl":247},1,"days","up-to","submission to underwriter ready file, in production lines","about three to four weeks in the previous submission to quote process, as shown by AIG","organization","AIG Underwriter Assistance Synthesizes and Prepares Submissions for Underwriter Review Within One Day","https://www.sec.gov/Archives/edgar/data/5272/000000527225000017/aig_investorxdayx2025.htm",true,[250],{"url":247,"title":251,"publisher":252,"date":253},"AIG Investor Day 2025 presentation (Form 8-K, Exhibit 99.1)","American International Group via SEC EDGAR","2025-03-31",{"level":255,"checkedAt":256},"source-verified","2026-09-26","B","aig-underwriter-assistance",null,{"title":261,"useCases":262,"organization":263,"vendors":265,"summary":268,"stage":233,"year":234,"channels":269,"languages":270,"metrics":271,"outcomeDisclosed":220,"sources":272,"verification":278,"grade":257,"id":279,"organizationSlug":259},"CNA: AI solutions for underwriting triage and submission responsiveness",[213],{"name":264,"anonymized":220,"country":221,"region":222,"industry":16},"CNA Financial",[266],{"name":264,"role":267},"in-house","In its fourth quarter 2025 earnings remarks, CNA said it had deployed a number of AI solutions across underwriting, claims and the back office over the past year and rolled out generative AI tools to every employee. Management reported faster triage, better submission responsiveness and measurable time savings, and framed further investment around risk selection, service quality and efficiency. No figures were disclosed.",[28],[237],[],[273],{"url":274,"title":275,"publisher":276,"date":277},"https://www.sec.gov/Archives/edgar/data/21175/000002117526000008/q42025ex994earningsremarks.htm","CNA fourth quarter 2025 earnings remarks (Form 8-K, Exhibit 99.4)","CNA Financial via SEC EDGAR","2026-02-09",{"level":255,"checkedAt":256},"cna-ai-underwriting-triage",{"title":281,"useCases":282,"organization":284,"vendors":286,"summary":288,"stage":233,"year":234,"channels":289,"languages":290,"metrics":291,"outcomeDisclosed":220,"sources":292,"verification":302,"grade":257,"id":303,"organizationSlug":259},"Kinsale Capital: AI driven submission routing and company wide AI tools for underwriting and actuarial teams",[213,283],"insurance-pricing-and-actuarial-copilot",{"name":285,"anonymized":220,"country":221,"region":222,"industry":16},"Kinsale Capital Group",[287],{"name":285,"role":267},"Kinsale, a US excess and surplus lines insurer that sources about 95% of its premium through wholesale brokers, told investors in January 2026 that AI driven routing improves the accuracy of submission routing and underwriter productivity, alongside an average submission clearance time of 9 minutes. Its 2025 annual report says it gave every employee an enterprise AI tool licence in 2025, that use is most prevalent in its IT, actuarial and analytical teams with selective use in underwriting, and that it also uses internally developed agents. Kinsale does not attribute the clearance time to AI, so no metric is recorded.",[28],[237],[],[293,298],{"url":294,"title":295,"publisher":296,"date":297},"https://www.sec.gov/Archives/edgar/data/1669162/000166916226000004/investorday-1x8x2026.htm","Kinsale Capital Group Investor Day presentation (Form 8-K, Exhibit 99.1)","Kinsale Capital Group via SEC EDGAR","2026-01-08",{"url":299,"title":300,"publisher":296,"date":301},"https://www.sec.gov/Archives/edgar/data/1669162/000166916226000015/knsl-20251231.htm","Kinsale Capital Group Form 10-K for 2025","2026-02-20",{"level":255,"checkedAt":256},"kinsale-ai-submission-routing",{"title":305,"useCases":306,"organization":307,"vendors":309,"summary":312,"stage":313,"year":234,"channels":314,"languages":315,"metrics":316,"outcomeDisclosed":220,"sources":317,"verification":322,"grade":257,"id":323,"organizationSlug":259},"Skyward Specialty: AI submission preprocessing and risk summaries across six business units",[204,213],{"name":308,"anonymized":220,"country":221,"region":222,"industry":16},"Skyward Specialty Insurance Group",[310],{"name":311,"role":229},"Sixfold","Skyward Specialty announced in December 2025 that its partnership with Sixfold was entering its second year. The platform preprocesses submissions and generates recommendations on prioritization, appetite alignment and risk summarization and assessment, while underwriters stay in the loop to apply their judgment. The platform is live across six business units and more than 10 product lines, with an average deployment timeline of 8 to 10 weeks. The company presents the partnership as a step toward fully AI powered underwriting across its US property and casualty lines; no outcome figures were disclosed.","scaled",[28],[237],[],[318],{"url":319,"title":320,"publisher":308,"date":321},"https://skywardinsurance.com/press-releases/skyward-specialty-and-sixfold-partner-to-advance-ai-powered-underwriting/","Skyward Specialty and Sixfold Partner to Advance AI-Powered Underwriting","2025-12-18",{"level":255,"checkedAt":256},"skyward-specialty-sixfold-ai-underwriting",{"title":325,"useCases":326,"organization":327,"vendors":330,"summary":333,"stage":233,"year":334,"channels":335,"languages":336,"metrics":337,"outcomeDisclosed":220,"sources":338,"verification":346,"grade":257,"id":347,"organizationSlug":259},"Hiscox: generative AI lead underwriting model for sabotage and terrorism risks",[213,204,207],{"name":328,"anonymized":220,"country":329,"region":173,"industry":16},"Hiscox","GB",[331],{"name":332,"role":229},"Google Cloud","Hiscox London Market combined its own Hiscox AI Laboratories (Hailo) with Google Cloud's Gemini model to automate lead underwriting from email submission to quote in its sabotage and terrorism line. In scope risks are assessed by the model and the process generates an email to the broker with pricing and other data completed, ready for underwriter review. After a December 2023 proof of concept, in which Hiscox said the manual extraction step can take up to three days and quotes could be produced within three minutes, the model went live in August 2024. It initially covers renewals of existing US and Canadian sabotage and terrorism risks, excluding the New York and Chicago metro areas.",2024,[26,28],[237],[],[339,344],{"url":340,"title":341,"publisher":342,"date":343},"https://www.hiscoxgroup.com/news/press-releases/2024/12-08-24","Hiscox's generative AI-enhanced lead underwriting model enabled by Google Cloud goes live","Hiscox Group","2024-08-12",{"url":37,"title":36,"publisher":342,"date":345},"2023-12-12",{"level":255,"checkedAt":209},"hiscox-generative-ai-lead-underwriting",{"title":349,"useCases":350,"organization":351,"vendors":355,"summary":357,"stage":233,"year":234,"channels":358,"languages":359,"metrics":360,"outcomeDisclosed":248,"sources":375,"verification":378,"grade":379,"id":380,"organizationSlug":259},"Generali Global Corporate & Commercial: AI risk insights in the cyber underwriting workflow",[204,213],{"name":352,"anonymized":220,"country":353,"region":354,"industry":16},"Generali Global Corporate & Commercial","IT","global",[356],{"name":311,"role":229},"As its cyber book grew, Generali GC&C chose Sixfold as its first external AI partner and connected it to its cyber data sources and scoring system, so that all available risk information is structured in a dashboard for the underwriter against Generali's own guidelines. The vendor reports that over 90% of underwriters adopted the platform, that most cyber submissions now go through it with turnaround times for distribution cut by 50%, and that risk engineering reports take a few hours instead of about two days. Its Global Head of Operations and IT, Matthew Richardson, is quoted as saying that Sixfold's input is now required for every quote.",[28],[237],[361,369],{"kpi":51,"value":362,"unit":363,"qualifier":364,"period":365,"claimant":366,"quote":367,"sourceUrl":368},50,"percent","exact","turnaround for distribution channels on cyber submissions","vendor","Today, most Cyber submissions are accelerated through the Sixfold solution, cutting turnaround times for our distribution channels by 50%.","https://www.sixfold.ai/case-study/generali-gc-c",{"kpi":370,"value":371,"unit":363,"qualifier":372,"period":373,"claimant":366,"quote":374,"sourceUrl":368},"employee-adoption",90,"at-least","cyber underwriters","The results were immediate: over 90% of underwriters actively adopted the platform, reporting consistently high accuracy scores.",[376],{"url":368,"title":377,"publisher":311},"Generali GC&C | Sixfold Case Study",{"level":255,"checkedAt":256},"C","generali-gcc-sixfold-cyber-underwriting",{"title":382,"useCases":383,"organization":384,"vendors":386,"summary":389,"stage":313,"year":234,"channels":390,"languages":391,"metrics":392,"outcomeDisclosed":248,"sources":404,"verification":407,"grade":379,"id":408,"organizationSlug":259},"Paragon Insurance Group: automated submission ingestion, clearance and prioritization with Kalepa",[213],{"name":385,"anonymized":220,"country":221,"region":222,"industry":16},"Paragon Insurance Group",[387],{"name":388,"role":229},"Kalepa","Paragon runs about 25 specialty programs, some of which receive upward of 50,000 submissions a year, and its underwriters could review only about 30% of incoming submissions. With Kalepa, every submission is ingested, cleared and ranked by fit and likelihood to bind within minutes, with research from news, legal filings and third party data attached. Paragon's CTO says extraction is now around 98 to 99% accurate, better than the former manual operations team, and its E&S president says the quote to bind ratio doubled within the first year.",[26,28],[237],[393,399],{"kpi":53,"value":394,"unit":363,"qualifier":395,"period":396,"baseline":397,"claimant":245,"quote":398,"sourceUrl":42},98,"approximately","submission data extraction, after full inbox rollout","manual extraction by an operations team","We're somewhere around 98 to 99% accurate now - even more accurate than when we had an operations team doing this manually.",{"kpi":54,"value":400,"unit":401,"qualifier":364,"period":402,"claimant":245,"quote":403,"sourceUrl":42},2,"multiplier","quote to bind ratio, first year","We're seeing a better quote-to-bind ratio. In the past year it has doubled from what it was before.",[405],{"url":42,"title":41,"publisher":388,"date":406},"2026-03-18",{"level":255,"checkedAt":256},"paragon-kalepa-submission-triage",{"title":410,"useCases":411,"organization":412,"vendors":415,"summary":417,"stage":233,"year":334,"channels":418,"languages":419,"metrics":420,"outcomeDisclosed":248,"sources":427,"verification":435,"grade":379,"id":436,"organizationSlug":259},"AXIS: AI classification and appetite matching of new business applications with Sixfold",[213],{"name":413,"anonymized":220,"country":414,"region":354,"industry":16},"AXIS Capital","BM",[416],{"name":311,"role":229},"AXIS underwriters spent much of each new submission classifying the applicant into the right industry and writing a snapshot of its operations. AXIS started Sixfold with an underwriter facing dashboard and, after the pilot, integrated it into its automated clearance process, where it applies industry codes and matches cases against AXIS risk appetite. The vendor reports more than 15,000 applications analyzed in the first month and an implementation of about six weeks. AXIS's 2025 annual report separately describes AI tools used to empower underwriters and an AI Underwriting Working Group that monitors AI activity affecting underwriting.",[28,27],[237],[421],{"kpi":55,"value":422,"unit":423,"qualifier":372,"period":424,"claimant":366,"quote":425,"sourceUrl":426},15000,"count","applications analyzed in the first month","In the first month, AXIS analyzed more than 15,000 applications using Sixfold’s AI.","https://www.sixfold.ai/case-study/axis",[428,430],{"url":426,"title":429,"publisher":311},"AXIS | Sixfold Case Study",{"url":431,"title":432,"publisher":433,"date":434},"https://www.sec.gov/Archives/edgar/data/1214816/000121481626000097/axs-20251231.htm","AXIS Capital Holdings Limited Form 10-K for 2025","AXIS Capital via SEC EDGAR","2026-02-27",{"level":255,"checkedAt":256},"axis-sixfold-submission-classification",{"title":438,"useCases":439,"organization":440,"vendors":442,"summary":445,"stage":233,"year":38,"channels":446,"languages":447,"metrics":448,"outcomeDisclosed":248,"sources":454,"verification":458,"grade":379,"id":459,"organizationSlug":259},"Markel UK: AI supported triage and routing of broker submissions with Cytora",[213],{"name":441,"anonymized":220,"country":329,"region":173,"industry":16},"Markel",[443],{"name":444,"role":229},"Cytora","Before the change, the most senior underwriter in each Markel UK team triaged every incoming submission against appetite, and underwriters rekeyed risk data into several systems and pulled third party data by hand. With Cytora, broker submissions are digitized, enriched with external data, prioritized against Markel's underwriting and distribution strategy and routed to the right specialist as decision ready risks, with data flowing into the CRM and policy systems. The vendor reports a 113% productivity uplift (GWP per FTE) and a quote turnaround SLA for strategic partners cut from 24 hours to 2 hours.",[26,28],[237],[449],{"kpi":52,"value":450,"unit":363,"qualifier":364,"period":451,"claimant":366,"quote":452,"sourceUrl":453},113,"gross written premium per underwriting FTE","an uplift of 113% in productivity (GWP/FTE) in their underwriting teams","https://www.cytora.com/risk-flow-center/blog/case-study-markel-records-113-productivity-increase-in-its-underwriting-team-following-cytora-partnership",[455],{"url":453,"title":456,"publisher":444,"date":457},"Markel uses Cytora and achieves +100% productivity uplift to fuel growth","2023-09-22",{"level":255,"checkedAt":209},"markel-cytora-submission-triage",0,[462,467,472,477,482,487],{"kpi":53,"label":463,"unit":363,"aggregate":248,"higherIsBetter":248,"n":240,"nUpTo":460,"median":394,"min":394,"max":394,"byClaimant":464,"vendorOnly":220,"points":465},"Accuracy",{"organization":240,"vendor":460,"regulator":460,"independent":460},[466],{"evidenceId":408,"organization":385,"value":394,"qualifier":395,"claimant":245,"grade":379,"pooled":248},{"kpi":54,"label":468,"unit":401,"aggregate":248,"higherIsBetter":248,"n":240,"nUpTo":460,"median":400,"min":400,"max":400,"byClaimant":469,"vendorOnly":220,"points":470},"Conversion uplift",{"organization":240,"vendor":460,"regulator":460,"independent":460},[471],{"evidenceId":408,"organization":385,"value":400,"qualifier":364,"claimant":245,"grade":379,"pooled":248},{"kpi":51,"label":473,"unit":363,"aggregate":248,"higherIsBetter":248,"n":240,"nUpTo":460,"median":362,"min":362,"max":362,"byClaimant":474,"vendorOnly":248,"points":475},"Cycle time reduction",{"organization":460,"vendor":240,"regulator":460,"independent":460},[476],{"evidenceId":380,"organization":352,"value":362,"qualifier":364,"claimant":366,"grade":379,"pooled":248},{"kpi":55,"label":478,"unit":423,"aggregate":220,"higherIsBetter":248,"n":240,"nUpTo":460,"median":422,"min":422,"max":422,"byClaimant":479,"vendorOnly":248,"points":480},"Interactions handled",{"organization":460,"vendor":240,"regulator":460,"independent":460},[481],{"evidenceId":436,"organization":413,"value":422,"qualifier":372,"claimant":366,"grade":379,"pooled":248},{"kpi":52,"label":483,"unit":363,"aggregate":248,"higherIsBetter":248,"n":240,"nUpTo":460,"median":450,"min":450,"max":450,"byClaimant":484,"vendorOnly":248,"points":485},"Productivity gain",{"organization":460,"vendor":240,"regulator":460,"independent":460},[486],{"evidenceId":459,"organization":441,"value":450,"qualifier":364,"claimant":366,"grade":379,"pooled":248},{"kpi":56,"label":488,"unit":241,"aggregate":220,"higherIsBetter":220,"n":460,"nUpTo":240,"median":259,"min":259,"max":259,"byClaimant":489,"vendorOnly":220,"points":490},"Cycle time",{"organization":460,"vendor":460,"regulator":460,"independent":460},[491],{"evidenceId":258,"organization":219,"value":240,"qualifier":242,"claimant":245,"grade":257,"pooled":220},{"low":493,"high":494},320000.00000000006,1866666.6666666667,[496,517,541,560,576],{"slug":204,"title":497,"shortTitle":498,"definition":499,"status":9,"industries":500,"functions":501,"patterns":503,"audience":507,"autonomy":508,"adoptionStage":31,"segment":18,"evidenceCount":509,"publicEvidenceCount":509,"organizations":510,"bestGrade":257,"headline":515,"lastVerified":256,"indexable":248},"AI copilot for underwriting risk assessment","Underwriting risk assessment copilot","A copilot that assembles everything relevant to a risk (the submission, loss history, internal guidelines, 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.",[16],[18,502],"risk-management",[504,505,506,24],"rag-knowledge-assistant","summarization","content-generation","employee-facing","copilot",8,[511,219,512,513,352,328,308,514],"Accelerant Holdings","Arch Capital Group","Bowhead Specialty","Zurich North America",{"kpi":51,"label":473,"unit":363,"n":240,"nUpTo":460,"kind":516,"value":362,"qualifier":364,"claimant":366,"organization":352,"vendorReported":248},"reported",{"slug":205,"title":518,"shortTitle":519,"definition":520,"status":9,"industries":521,"functions":526,"patterns":529,"audience":29,"autonomy":30,"adoptionStage":531,"evidenceCount":532,"publicEvidenceCount":533,"organizations":534,"bestGrade":257,"headline":540,"lastVerified":209,"indexable":248},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[522,523,524,525],"cross-industry","government","automotive","manufacturing",[19,527,528],"case-management","finance-and-accounting",[21,530,22],"computer-vision","mainstream",7,5,[535,536,537,538,539],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":53,"label":463,"unit":363,"n":240,"nUpTo":460,"kind":516,"value":371,"qualifier":372,"claimant":366,"organization":535,"vendorReported":248},{"slug":206,"title":542,"shortTitle":543,"definition":544,"status":9,"industries":545,"functions":547,"patterns":549,"audience":29,"autonomy":30,"adoptionStage":531,"segment":29,"evidenceCount":550,"publicEvidenceCount":550,"organizations":551,"bestGrade":257,"headline":558,"lastVerified":209,"indexable":248},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[522,546,16,523],"banking",[19,548,527],"customer-service",[22,21,505],6,[552,553,554,555,556,557],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":53,"label":463,"unit":363,"n":240,"nUpTo":460,"kind":516,"value":559,"qualifier":364,"claimant":366,"organization":556,"vendorReported":248},91,{"slug":207,"title":561,"shortTitle":562,"definition":563,"status":9,"industries":564,"functions":565,"patterns":567,"audience":29,"autonomy":508,"adoptionStage":570,"segment":571,"evidenceCount":572,"publicEvidenceCount":572,"organizations":573,"bestGrade":257,"headline":259,"lastVerified":256,"indexable":248},"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],[18,548,566],"sales",[23,21,568,569],"conversational-agent","recommendation-and-personalization","emerging","distribution",3,[328,574,575],"Nsure.com","Zurich Insurance Group",{"slug":208,"title":577,"shortTitle":578,"definition":579,"status":9,"industries":580,"functions":581,"patterns":583,"audience":507,"autonomy":584,"adoptionStage":31,"segment":571,"evidenceCount":533,"publicEvidenceCount":533,"organizations":585,"bestGrade":257,"headline":259,"lastVerified":209,"indexable":248},"AI assistant for insurance brokers and agents","Broker and agent assistant","An AI assistant for tied agents, independent brokers, advisors and the insurer's own distribution staff that answers product, underwriting and process questions from approved sources, prepares personalized customer engagement and follow ups, validates and prioritizes leads, and drafts meeting notes and emails, so producers spend more time with customers.",[16],[566,582],"knowledge-management",[504,569,506,505],"assist",[586,587,588,589,575],"Manulife","Prudential plc","Sun Life","Waterdrop",{"indexable":248,"reasons":591},[],[593,598,603,609,615,620,627,634,642,649,655,661,668,675,681,686,693,699,705,711,717,723,729,734,739,746,752,756,761,768,775,781,787,792],{"id":163,"label":594,"issuer":178,"region":173,"url":595,"description":596,"useCases":597,"indexable":248},"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":164,"label":599,"issuer":178,"region":173,"url":600,"description":601,"useCases":602,"indexable":248},"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":167,"label":604,"issuer":605,"region":354,"url":606,"description":607,"useCases":608,"indexable":248},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":166,"label":610,"issuer":611,"region":222,"url":612,"description":613,"useCases":614,"indexable":248},"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":165,"label":616,"issuer":178,"region":173,"url":617,"description":618,"useCases":619,"indexable":248},"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":621,"label":622,"issuer":623,"region":173,"url":624,"description":625,"useCases":626,"indexable":248},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":628,"label":629,"issuer":630,"region":173,"url":631,"description":632,"useCases":633,"indexable":248},"uk-consumer-duty","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":635,"label":636,"issuer":637,"region":638,"url":639,"description":640,"useCases":641,"indexable":248},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":643,"label":644,"issuer":645,"region":638,"url":646,"description":647,"useCases":648,"indexable":248},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":650,"label":651,"issuer":652,"region":354,"url":653,"description":654,"useCases":70,"indexable":248},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":656,"label":657,"issuer":658,"region":222,"url":659,"description":660,"useCases":70,"indexable":248},"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":662,"label":663,"issuer":664,"region":173,"url":665,"description":666,"useCases":667,"indexable":248},"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":669,"label":670,"issuer":671,"region":354,"url":672,"description":673,"useCases":674,"indexable":248},"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":676,"label":677,"issuer":178,"region":173,"url":678,"description":679,"useCases":680,"indexable":248},"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":682,"label":683,"issuer":178,"region":173,"url":684,"description":685,"useCases":680,"indexable":248},"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":687,"label":688,"issuer":689,"region":222,"url":690,"description":691,"useCases":692,"indexable":248},"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":694,"label":695,"issuer":178,"region":173,"url":696,"description":697,"useCases":698,"indexable":248},"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":700,"label":701,"issuer":702,"region":222,"url":703,"description":704,"useCases":698,"indexable":248},"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":706,"label":707,"issuer":708,"region":354,"url":709,"description":710,"useCases":698,"indexable":248},"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":712,"label":713,"issuer":178,"region":173,"url":714,"description":715,"useCases":716,"indexable":248},"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":718,"label":719,"issuer":720,"region":222,"url":721,"description":722,"useCases":716,"indexable":248},"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":724,"label":725,"issuer":637,"region":638,"url":726,"description":727,"useCases":728,"indexable":248},"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":730,"label":731,"issuer":178,"region":173,"url":732,"description":733,"useCases":728,"indexable":248},"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":735,"label":736,"issuer":178,"region":173,"url":737,"description":738,"useCases":728,"indexable":248},"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":740,"label":741,"issuer":742,"region":173,"url":743,"description":744,"useCases":745,"indexable":248},"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":747,"label":748,"issuer":749,"region":222,"url":750,"description":751,"useCases":509,"indexable":248},"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":168,"label":753,"issuer":178,"region":173,"url":754,"description":755,"useCases":509,"indexable":248},"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":757,"label":758,"issuer":178,"region":173,"url":759,"description":760,"useCases":550,"indexable":248},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":762,"label":763,"issuer":764,"region":765,"url":766,"description":767,"useCases":533,"indexable":248},"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":769,"label":770,"issuer":771,"region":173,"url":772,"description":773,"useCases":774,"indexable":248},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":776,"label":777,"issuer":778,"region":173,"url":779,"description":780,"useCases":774,"indexable":248},"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":782,"label":783,"issuer":784,"region":638,"url":785,"description":786,"useCases":572,"indexable":248},"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":788,"label":789,"issuer":178,"region":173,"url":790,"description":791,"useCases":572,"indexable":248},"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":793,"label":794,"issuer":795,"region":222,"url":796,"description":797,"useCases":572,"indexable":248},"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.",1790598299532]