[{"data":1,"prerenderedAt":726},["ShallowReactive",2],{"uc-underwriting-risk-assessment-copilot":3,"uc-regulations":518},{"useCase":4,"evidence":200,"blitsAiDeployments":411,"benchmarks":412,"indicative":428,"related":431,"indexability":516,"includeUnpublished":206},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":18,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":49,"macroEstimates":83,"feasibility":84,"implementation":97,"risk":140,"blitsAi":176,"faq":178,"related":188,"datePublished":194,"dateModified":194,"lastVerified":195,"changelog":196,"slug":199},"AI copilot for underwriting risk assessment","Underwriting risk assessment copilot","AI underwriting copilot for risk assessment","An underwriting copilot drafts the risk narrative the underwriter signs. Zurich North America underwriters report saving 2 hours per submission on average.","published","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.",[12,13,14],"underwriting copilot","underwriting assistant","AI underwriting workbench",[16],"insurance",[18,19],"underwriting","risk-management",[21,22,23,24],"rag-knowledge-assistant","summarization","content-generation","agentic-workflow",[26],"internal-tools","employee-facing","copilot","early-adopters","Once a submission is in appetite, the real work starts. A middle market or specialty underwriter\nreads hundreds of pages of operations descriptions, loss runs and supplementals, searches the web\nand internal systems for anything the broker did not mention, checks the risk against underwriting\nguidelines and writes a narrative that justifies the decision to a referral authority, an auditor or\na reinsurer.\n\nMuch of that time goes into finding and restating information rather than judging it, and the\nquality depends on how thorough each underwriter is on a busy day. Exposures buried in a website or\na court filing are easy to miss, narratives vary in structure, and experienced underwriters spend\nhours on write ups instead of broker relationships and complex risks.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"A Cytora case study on Markel UK reports that, before the project, Markel's underwriters spent more than 30% of their time on low skill, low value tasks such as rekeying risk data into different systems and pulling third party data by hand.","Markel uses Cytora and achieves +100% productivity uplift to fuel growth","https://www.cytora.com/risk-flow-center/blog/case-study-markel-records-113-productivity-increase-in-its-underwriting-team-following-cytora-partnership",2023,"1. **Assemble the file.** The copilot gathers the extracted submission, prior policies and claims,\n   internal notes and approved external data for the insured into one view.\n2. **Research the insured.** An agent searches approved sources (company website, news, court and\n   regulatory records) and summarizes what is relevant to the line of business, with links.\n3. **Check against guidelines.** Retrieval over the insurer's underwriting guidelines and referral\n   rules flags where the risk falls outside authority, where information is missing and which\n   questions to ask the broker.\n4. **Draft the narrative.** The copilot writes a first draft of the underwriting narrative or\n   referral note in the insurer's format, citing the document and page behind each statement.\n5. **Underwriter decides.** The underwriter edits the draft, sets terms and price, and signs the\n   decision; edits and outcomes are logged to improve prompts, retrieval and guidelines.",[39,40,41,42],"employee-productivity","risk-reduction","speed","compliance",[44,45,46,47,48],"time-saved-per-task","processing-time-reduction","employee-adoption","productivity-gain","accuracy",{"referenceOrg":50,"inputs":51,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A commercial insurer with 100 underwriters in middle market and specialty lines",[52,57,64,71],{"key":53,"label":54,"low":55,"high":55,"unit":53,"note":56},"underwriters","Underwriters using the copilot",100,"The reference insurer.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"filesPerUnderwriter","Submissions fully assessed per underwriter per year",150,250,"submissions per underwriter per year","Editorial assumption for middle market and specialty lines. Replace with your own volumes.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"hoursSaved","Hours saved per assessed submission",0.5,1.5,"hours per submission","Conservative against the benchmark on this page (Zurich North America underwriters report an average of 2 hours saved per submission).",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"costPerHour","Fully loaded cost of an underwriter hour",70,110,"USD per hour","Editorial assumption. Replace with your own fully loaded cost.","underwriters * filesPerUnderwriter * hoursSaved * costPerHour","USD","per year","Underwriter time released","Time released, not cash saved. It leaves out the value of better risk selection (exposures found that change a decision), more quotes per underwriter, platform and data costs, and the time underwriters spend checking drafts.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":92},"medium","The models are capable; the hard parts are clean underwriting guidelines to retrieve from, approved data sources, a narrative format underwriters accept, and a way to measure whether the drafts are right. Adoption depends on underwriters trusting the output, so accuracy tracking matters as much as the build.",[88,89,90,91],"Current underwriting guidelines, referral rules and authority levels per line, in retrievable form","Extracted submission data (from an intake process) and policy and claims history per insured","A list of approved external sources and data vendors, with use conditions","Examples of good underwriting narratives to set the format and tone",[93,94,95,96],"Underwriting workbench or CRM","Policy administration and claims systems (read only)","Third party data providers and web research tools","Document management for submissions and guidelines",{"steps":98,"guardrails":114,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":127},[99,102,105,108,111],{"title":100,"detail":101},"Choose a line where narratives are long and consistent","Middle market property and casualty, cyber and professional lines work well: the files are big, the narrative format is standard and underwriters feel the pain.",{"title":103,"detail":104},"Clean the guidelines first","Put underwriting guidelines and referral rules into one current, owned source. The copilot can only be as right as the guidelines it retrieves.",{"title":106,"detail":107},"Define what a good draft is","Agree a checklist with senior underwriters (exposures covered, guideline breaches flagged, sources cited) and score a sample of drafts against it before rollout.",{"title":109,"detail":110},"Pilot in a few offices with feedback loops","Start with a small group, hold regular feedback sessions and publish accuracy results to the users. Zurich North America combined accuracy tracking with feedback sessions and, according to its vendor, expanded from four offices to dozens within six months.",{"title":112,"detail":113},"Instrument adoption and edits","Track how often drafts are used, how much they are edited and whether flagged exposures change decisions; low edit rates on bad drafts are a warning sign, not a success.",[115,116,117,118,119],"The underwriter signs every decision; the copilot never binds, prices or declines on its own","Every statement in a draft links to its source document or web page","Research limited to an approved list of external sources","Guideline retrieval restricted to the current approved version, with owners and review dates","No use of protected characteristics or proxies for them in any scoring the copilot surfaces","Underwriters review, edit and own every narrative and decision. Underwriting management reviews a monthly sample of drafts against the quality checklist, and referral authorities see the draft and the underwriter's edits together.",[122,123,124,125,126],"Time from assignment to underwriting decision, per line","Share of drafts used and the average edit distance","Exposures flagged by the copilot that changed the decision","Accuracy of drafts on a scored monthly sample","Weekly active underwriters as a share of licensed users",[128,131,134,137],{"title":129,"detail":130},"Automation bias","Underwriters accept a fluent draft without checking it. Show sources inline, sample drafts for quality and make the underwriter confirm key facts.",{"title":132,"detail":133},"Stale guidelines","The copilot confidently applies a guideline that changed last quarter. Give each guideline an owner and a review date and retrieve only the current version.",{"title":135,"detail":136},"Research that drifts beyond approved sources","Web research pulls in unreliable or personal information about individuals. Restrict sources and log every page used.",{"title":138,"detail":139},"Time saved but decisions unchanged","If the copilot only speeds up writing, risk selection does not improve. Track flagged exposures and outcomes, not just minutes.",{"euAiAct":141,"regulations":144,"guidance":151,"controls":169,"incidents":175},{"tier":142,"basis":143},"context-dependent","For commercial property and casualty lines the copilot is not listed in Annex III. Used for risk assessment of natural persons in life or health insurance it falls under Annex III point 5(c) and is high risk, with risk management, data governance, logging and human oversight duties, and deployers must carry out a fundamental rights impact assessment under Article 27.",[145,146,147,148,149,150],"eu-ai-act","gdpr","dora","nist-ai-rmf","iso-42001","solvency-ii",[152,158,163],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","europe","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Published in August 2025 and addressed to national supervisors, it sets out how insurance sector legislation applies to AI systems that are not prohibited or high risk under the AI Act, including governance, fairness, explainability and human oversight.",{"title":159,"issuer":160,"region":155,"url":161,"note":162},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https://artificialintelligenceact.eu/annex/3/","Point 5(c) makes life and health insurance risk assessment and pricing of natural persons high risk.",{"title":164,"issuer":165,"region":166,"url":167,"note":168},"SB21-169, Protecting Consumers from Unfair Discrimination in Insurance Practices","Colorado Division of Insurance","north-america","https://doi.colorado.gov/for-consumers/sb21-169-protecting-consumers-from-unfair-discrimination-in-insurance-practices","Requires insurers to test external data, algorithms and predictive models for unfair discrimination; Regulation 10-1-1 sets governance requirements for life, private passenger auto and health benefit plan insurers.",[170,171,172,173,174],"AI inventory entry per line of business with an accountable underwriting owner","Documented approved source list and data use conditions","Retention of drafts, edits and final narratives for audit","Periodic bias and fairness testing where personal lines or individuals are in scope","Model provider risk assessment and exit plan under DORA",[],{"howToBuild":177},"On Blits.ai the copilot is an **AI agent** inside the underwriter's tools (web, Microsoft Teams or\nan API into the workbench) with a **knowledge base** of underwriting guidelines and referral rules,\nsearched with hybrid retrieval so exact guideline references and meaning both match. **SQL\nknowledge bases** and **custom functions** give read access to policy and claims history, and the\nbuilt in **web search and browsing tools** handle research on the insured, with the tool execution\npolicy controlling which tools the agent may use.\n\nDrafting the narrative runs as an **agentic workflow** that assembles the file, researches the\ninsured and returns a draft with sources as **structured output** for the workbench. **Guardrails**\nand **PII masking** keep personal data out of prompts where it is not needed, **execution tracing**\nrecords which sources each draft used, and **test suites** with LLM graded rules score drafts\nagainst the underwriting checklist on every change. Models can be switched per agent, and data can\nstay in EU or UAE regions.",[179,182,185],{"question":180,"answer":181},"How much time does an underwriting copilot save?","Zurich North America's middle market underwriters report saving an average of 2 hours per submission with AI drafted narratives. For Generali GC&C, turnaround times for its distribution channels on cyber submissions were cut by 50%. Both figures come from the vendor's case studies, and the Zurich figure is self reported by underwriters rather than a time study.",{"question":183,"answer":184},"Does it replace underwriting judgment?","No. The deployments on this page keep the underwriter as the decision maker: AIG shows the underwriter analyzing the AI output before quoting, Skyward Specialty keeps underwriters in the loop to apply their judgment, and at Zurich North America underwriters start from an AI first draft. The value is in reading more, missing less and writing faster.",{"question":186,"answer":187},"Is an underwriting copilot high risk under the EU AI Act?","It depends on the line. Commercial lines are outside Annex III. Risk assessment of individuals for life or health insurance is high risk under point 5(c), which brings conformity, logging and human oversight obligations.",[189,190,191,192,193],"commercial-underwriting-submission-triage","life-underwriting-medical-record-summarization","insurance-pricing-and-actuarial-copilot","credit-memo-drafting-agent","insurance-broker-and-agent-assistant","2026-09-27","2026-09-26",[197],{"date":194,"note":198},"First published","underwriting-risk-assessment-copilot",[201,230,267,287,308,335,359,389],{"title":202,"useCases":203,"organization":204,"vendors":208,"summary":211,"stage":212,"year":213,"channels":214,"languages":216,"metrics":218,"outcomeDisclosed":206,"sources":219,"verification":225,"grade":227,"id":228,"organizationSlug":229},"Accelerant: AI agents for data ingestion and AI tools for underwriting and actuarial analysis",[199,191],{"name":205,"anonymized":206,"region":207,"industry":16},"Accelerant Holdings",false,"global",[209],{"name":205,"role":210},"in-house","Accelerant runs a risk exchange that connects specialty MGAs (its Members) with risk capital. Its 2025 annual report says incoming data, from Member bordereaux to third party sources, is validated, transformed and governed using AI agents, that internally developed AI tools and models assist Members' underwriting, and that its risk evaluation tools help Members identify, classify, validate, research and price underwriting opportunities. Members also get AI supported claims insights, actuarial analysis and portfolio management to manage rate adequacy. Engineers, data scientists, product managers and designers made up 34% of its workforce at the end of 2025. No outcome figures are disclosed.","production",2025,[26,215],"api",[217],"en",[],[220],{"url":221,"title":222,"publisher":223,"date":224},"https://www.sec.gov/Archives/edgar/data/1997350/000199735026000003/arx-20251231.htm","Accelerant Holdings Form 10-K for 2025","Accelerant Holdings via SEC EDGAR","2026-03-18",{"level":226,"checkedAt":195},"source-verified","B","accelerant-ai-underwriting-and-actuarial-tools",null,{"title":231,"useCases":232,"organization":233,"vendors":236,"summary":245,"stage":212,"year":213,"channels":246,"languages":247,"metrics":248,"outcomeDisclosed":259,"sources":260,"verification":265,"grade":227,"id":266,"organizationSlug":229},"AIG: AIG Underwriter Assistance for submission ingestion, prioritization and augmentation",[189,199],{"name":234,"anonymized":206,"country":235,"region":166,"industry":16},"American International Group","US",[237,240,243],{"name":238,"role":239},"Anthropic","model-provider",{"name":241,"role":242},"Palantir","platform",{"name":244,"role":242},"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.",[26,215],[217],[249],{"kpi":250,"value":251,"unit":252,"qualifier":253,"period":254,"baseline":255,"claimant":256,"quote":257,"sourceUrl":258},"cycle-time-days",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,[261],{"url":258,"title":262,"publisher":263,"date":264},"AIG Investor Day 2025 presentation (Form 8-K, Exhibit 99.1)","American International Group via SEC EDGAR","2025-03-31",{"level":226,"checkedAt":195},"aig-underwriter-assistance",{"title":268,"useCases":269,"organization":270,"vendors":272,"summary":275,"stage":276,"year":213,"channels":277,"languages":278,"metrics":279,"outcomeDisclosed":206,"sources":280,"verification":285,"grade":227,"id":286,"organizationSlug":229},"Skyward Specialty: AI submission preprocessing and risk summaries across six business units",[199,189],{"name":271,"anonymized":206,"country":235,"region":166,"industry":16},"Skyward Specialty Insurance Group",[273],{"name":274,"role":242},"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",[26],[217],[],[281],{"url":282,"title":283,"publisher":271,"date":284},"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":226,"checkedAt":195},"skyward-specialty-sixfold-ai-underwriting",{"title":288,"useCases":289,"organization":290,"vendors":293,"summary":295,"stage":212,"year":296,"channels":297,"languages":298,"metrics":299,"outcomeDisclosed":206,"sources":300,"verification":306,"grade":227,"id":307,"organizationSlug":229},"Arch Capital: AI that brings past experience and submission data to underwriting decisions",[199],{"name":291,"anonymized":206,"country":292,"region":207,"industry":16},"Arch Capital Group","BM",[294],{"name":291,"role":210},"Arch's 2024 annual report says it uses AI for catastrophe modelling and predictive analytics and, in its insurance operations, to provide more information about past experiences and submissions so that its professionals can make more data driven underwriting decisions. Every new generative AI technology proposed for use in its operations requires approval and is monitored closely. No outcome figures are disclosed.",2024,[26],[217],[],[301],{"url":302,"title":303,"publisher":304,"date":305},"https://www.sec.gov/Archives/edgar/data/947484/000094748425000017/acgl-20241231.htm","Arch Capital Group Ltd. Form 10-K for 2024","Arch Capital Group via SEC EDGAR","2025-02-27",{"level":226,"checkedAt":195},"arch-capital-ai-underwriting-insights",{"title":309,"useCases":310,"organization":312,"vendors":315,"summary":318,"stage":212,"year":296,"channels":319,"languages":321,"metrics":322,"outcomeDisclosed":206,"sources":323,"verification":333,"grade":227,"id":334,"organizationSlug":229},"Hiscox: generative AI lead underwriting model for sabotage and terrorism risks",[189,199,311],"insurance-renewal-and-retention",{"name":313,"anonymized":206,"country":314,"region":155,"industry":16},"Hiscox","GB",[316],{"name":317,"role":242},"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.",[320,26],"email",[217],[],[324,329],{"url":325,"title":326,"publisher":327,"date":328},"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":330,"title":331,"publisher":327,"date":332},"https://www.hiscoxgroup.com/news/press-releases/2023/12-12-23","Hiscox and Google Cloud Collaborate on AI in lead underwriting for the London Market","2023-12-12",{"level":226,"checkedAt":194},"hiscox-generative-ai-lead-underwriting",{"title":336,"useCases":337,"organization":338,"vendors":340,"summary":343,"stage":212,"year":213,"channels":344,"languages":345,"metrics":346,"outcomeDisclosed":206,"sources":347,"verification":356,"grade":357,"id":358,"organizationSlug":229},"Bowhead Specialty: AI underwriting workbench for casualty submissions with Kalepa",[199],{"name":339,"anonymized":206,"country":235,"region":166,"industry":16},"Bowhead Specialty",[341],{"name":342,"role":242},"Kalepa","Bowhead's casualty underwriters use Kalepa's AI underwriting platform to bring research, data and underwriting guidelines into one workspace, surface information missing from broker submissions and apply appetite consistently. Bowhead's head of casualty says the book profile has improved because the tool helps avoid heightened risk profiles that were not visible in the broker's submission; no figures are given. Bowhead's own 2025 annual report states that it does not currently use generative AI tools, so the platform should be read as data and analytics support rather than a generative assistant.",[26],[217],[],[348,351],{"url":349,"title":350,"publisher":342},"https://www.kalepa.com/case-studies/driving-profitable-growth-bowhead-specialtys-results-with-kalepa","Driving Profitable Growth: Bowhead Specialty's Results with Kalepa",{"url":352,"title":353,"publisher":354,"date":355},"https://www.sec.gov/Archives/edgar/data/2002473/000162828026011089/bow-20251231.htm","Bowhead Specialty Holdings Inc. Form 10-K for 2025","Bowhead Specialty via SEC EDGAR","2026-02-24",{"level":226,"checkedAt":195},"C","bowhead-specialty-kalepa-underwriting-workbench",{"title":360,"useCases":361,"organization":362,"vendors":365,"summary":367,"stage":212,"year":213,"channels":368,"languages":369,"metrics":370,"outcomeDisclosed":259,"sources":384,"verification":387,"grade":357,"id":388,"organizationSlug":229},"Generali Global Corporate & Commercial: AI risk insights in the cyber underwriting workflow",[199,189],{"name":363,"anonymized":206,"country":364,"region":207,"industry":16},"Generali Global Corporate & Commercial","IT",[366],{"name":274,"role":242},"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.",[26],[217],[371,379],{"kpi":45,"value":372,"unit":373,"qualifier":374,"period":375,"claimant":376,"quote":377,"sourceUrl":378},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":46,"value":380,"unit":373,"qualifier":381,"period":382,"claimant":376,"quote":383,"sourceUrl":378},90,"at-least","cyber underwriters","The results were immediate: over 90% of underwriters actively adopted the platform, reporting consistently high accuracy scores.",[385],{"url":378,"title":386,"publisher":274},"Generali GC&C | Sixfold Case Study",{"level":226,"checkedAt":195},"generali-gcc-sixfold-cyber-underwriting",{"title":390,"useCases":391,"organization":392,"vendors":394,"summary":396,"stage":276,"year":296,"channels":397,"languages":398,"metrics":399,"outcomeDisclosed":259,"sources":406,"verification":409,"grade":357,"id":410,"organizationSlug":229},"Zurich North America: AI drafted underwriting narratives for middle market submissions",[199],{"name":393,"anonymized":206,"country":235,"region":166,"industry":16},"Zurich North America",[395],{"name":274,"role":242},"Zurich North America's U.S. Middle Market underwriters had to comb through hundreds of pages of exposures, operations, loss runs and supplemental forms per submission and then write a compliant underwriting narrative. After Sixfold was selected as one of nine winners of the Zurich Innovation Championship, the team rolled out a tool that gives underwriters an AI generated first draft of the narrative in Zurich's appetite, format and tone, with accuracy tracking and feedback sessions with underwriters. The vendor reports an average of 2 hours saved per submission and expansion from four offices to dozens within six months; an underwriter describes the tool surfacing a major exposure that changed a risk assessment.",[26],[217],[400],{"kpi":44,"value":401,"unit":402,"qualifier":374,"period":403,"claimant":376,"quote":404,"sourceUrl":405},120,"minutes","per submission, as reported by underwriters","Underwriters reported saving an average of 2 hours per submission","https://www.sixfold.ai/case-study/zurich",[407],{"url":405,"title":408,"publisher":274},"Zurich North America | Sixfold Case Study",{"level":226,"checkedAt":195},"zurich-north-america-sixfold-underwriting-narratives",0,[413,418,423],{"kpi":45,"label":414,"unit":373,"aggregate":259,"higherIsBetter":259,"n":251,"nUpTo":411,"median":372,"min":372,"max":372,"byClaimant":415,"vendorOnly":259,"points":416},"Cycle time reduction",{"organization":411,"vendor":251,"regulator":411,"independent":411},[417],{"evidenceId":388,"organization":363,"value":372,"qualifier":374,"claimant":376,"grade":357,"pooled":259},{"kpi":46,"label":419,"unit":373,"aggregate":259,"higherIsBetter":259,"n":251,"nUpTo":411,"median":380,"min":380,"max":380,"byClaimant":420,"vendorOnly":259,"points":421},"Employee adoption",{"organization":411,"vendor":251,"regulator":411,"independent":411},[422],{"evidenceId":388,"organization":363,"value":380,"qualifier":381,"claimant":376,"grade":357,"pooled":259},{"kpi":44,"label":424,"unit":402,"aggregate":259,"higherIsBetter":259,"n":251,"nUpTo":411,"median":401,"min":401,"max":401,"byClaimant":425,"vendorOnly":259,"points":426},"Time saved per task",{"organization":411,"vendor":251,"regulator":411,"independent":411},[427],{"evidenceId":410,"organization":393,"value":401,"qualifier":374,"claimant":376,"grade":357,"pooled":259},{"low":429,"high":430},525000,4125000,[432,457,468,487,500],{"slug":189,"title":433,"shortTitle":434,"definition":435,"status":9,"industries":436,"functions":437,"patterns":439,"audience":443,"autonomy":444,"adoptionStage":29,"segment":18,"evidenceCount":445,"publicEvidenceCount":445,"organizations":446,"bestGrade":227,"headline":452,"lastVerified":194,"indexable":259},"AI for commercial underwriting submission intake and triage","Underwriting submission triage","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.",[16],[18,438],"operations",[440,441,442,24],"document-processing","classification-and-routing","prediction-and-scoring","back-office","supervised-agent",9,[234,447,448,363,313,449,450,451,271],"AXIS Capital","CNA Financial","Kinsale Capital Group","Markel","Paragon Insurance Group",{"kpi":48,"label":453,"unit":373,"n":251,"nUpTo":411,"kind":454,"value":455,"qualifier":456,"claimant":256,"organization":451,"vendorReported":206},"Accuracy","reported",98,"approximately",{"slug":190,"title":458,"shortTitle":459,"definition":460,"status":9,"industries":461,"functions":462,"patterns":463,"audience":27,"autonomy":28,"adoptionStage":29,"segment":18,"evidenceCount":464,"publicEvidenceCount":464,"organizations":465,"bestGrade":227,"headline":229,"lastVerified":194,"indexable":259},"AI summarization of medical evidence for life and health underwriting","Life underwriting medical summaries","AI that reads the medical evidence behind a life or health insurance application (attending physician statements, electronic health records, lab results and disclosures), turns it into a structured, cited summary of conditions, treatments and dates, and maps it to the insurer's underwriting manual so an underwriter can decide faster and more consistently.",[16],[18],[440,22,21],2,[466,467],"Manulife","Prudential plc",{"slug":191,"title":469,"shortTitle":470,"definition":471,"status":9,"industries":472,"functions":473,"patterns":476,"audience":27,"autonomy":28,"adoptionStage":29,"segment":478,"evidenceCount":479,"publicEvidenceCount":479,"organizations":480,"bestGrade":227,"headline":484,"lastVerified":195,"indexable":259},"AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[16],[474,19,475],"product-and-pricing","analytics-and-reporting",[442,477,24,22],"code-generation","pricing",5,[205,481,482,449,483],"Europ Assistance","Generali France","MAIF",{"kpi":47,"label":485,"unit":486,"n":251,"nUpTo":411,"kind":454,"value":479,"qualifier":374,"claimant":256,"organization":482,"vendorReported":206},"Productivity gain","multiplier",{"slug":192,"title":488,"shortTitle":489,"definition":490,"status":9,"industries":491,"functions":493,"patterns":495,"audience":27,"autonomy":28,"adoptionStage":29,"segment":496,"evidenceCount":464,"publicEvidenceCount":464,"organizations":497,"bestGrade":227,"headline":229,"lastVerified":194,"indexable":259},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.",[492],"banking",[494,18,19],"lending-and-credit",[440,24,21,23],"specialized-businesses",[498,499],"Banestes","DBS Bank",{"slug":193,"title":501,"shortTitle":502,"definition":503,"status":9,"industries":504,"functions":505,"patterns":508,"audience":27,"autonomy":510,"adoptionStage":29,"segment":511,"evidenceCount":479,"publicEvidenceCount":479,"organizations":512,"bestGrade":227,"headline":229,"lastVerified":194,"indexable":259},"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],[506,507],"sales","knowledge-management",[21,509,23,22],"recommendation-and-personalization","assist","distribution",[466,467,513,514,515],"Sun Life","Waterdrop","Zurich Insurance Group",{"indexable":259,"reasons":517},[],[519,524,529,534,540,545,552,559,567,574,581,587,594,601,607,612,619,625,631,637,643,649,655,660,665,671,678,682,688,695,702,708,715,720],{"id":145,"label":520,"issuer":160,"region":155,"url":521,"description":522,"useCases":523,"indexable":259},"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":146,"label":525,"issuer":160,"region":155,"url":526,"description":527,"useCases":528,"indexable":259},"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":149,"label":530,"issuer":531,"region":207,"url":532,"description":533,"useCases":75,"indexable":259},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",{"id":148,"label":535,"issuer":536,"region":166,"url":537,"description":538,"useCases":539,"indexable":259},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":147,"label":541,"issuer":160,"region":155,"url":542,"description":543,"useCases":544,"indexable":259},"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":546,"label":547,"issuer":548,"region":155,"url":549,"description":550,"useCases":551,"indexable":259},"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":553,"label":554,"issuer":555,"region":155,"url":556,"description":557,"useCases":558,"indexable":259},"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":560,"label":561,"issuer":562,"region":563,"url":564,"description":565,"useCases":566,"indexable":259},"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":568,"label":569,"issuer":570,"region":563,"url":571,"description":572,"useCases":573,"indexable":259},"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":575,"label":576,"issuer":577,"region":207,"url":578,"description":579,"useCases":580,"indexable":259},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":582,"label":583,"issuer":584,"region":166,"url":585,"description":586,"useCases":580,"indexable":259},"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":588,"label":589,"issuer":590,"region":155,"url":591,"description":592,"useCases":593,"indexable":259},"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":595,"label":596,"issuer":597,"region":207,"url":598,"description":599,"useCases":600,"indexable":259},"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":602,"label":603,"issuer":160,"region":155,"url":604,"description":605,"useCases":606,"indexable":259},"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":608,"label":609,"issuer":160,"region":155,"url":610,"description":611,"useCases":606,"indexable":259},"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":613,"label":614,"issuer":615,"region":166,"url":616,"description":617,"useCases":618,"indexable":259},"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":620,"label":621,"issuer":160,"region":155,"url":622,"description":623,"useCases":624,"indexable":259},"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":626,"label":627,"issuer":628,"region":166,"url":629,"description":630,"useCases":624,"indexable":259},"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":632,"label":633,"issuer":634,"region":207,"url":635,"description":636,"useCases":624,"indexable":259},"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":638,"label":639,"issuer":160,"region":155,"url":640,"description":641,"useCases":642,"indexable":259},"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":644,"label":645,"issuer":646,"region":166,"url":647,"description":648,"useCases":642,"indexable":259},"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":650,"label":651,"issuer":562,"region":563,"url":652,"description":653,"useCases":654,"indexable":259},"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":656,"label":657,"issuer":160,"region":155,"url":658,"description":659,"useCases":654,"indexable":259},"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":661,"label":662,"issuer":160,"region":155,"url":663,"description":664,"useCases":654,"indexable":259},"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":666,"label":667,"issuer":668,"region":155,"url":669,"description":670,"useCases":445,"indexable":259},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":672,"label":673,"issuer":674,"region":166,"url":675,"description":676,"useCases":677,"indexable":259},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":150,"label":679,"issuer":160,"region":155,"url":680,"description":681,"useCases":677,"indexable":259},"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":683,"label":684,"issuer":160,"region":155,"url":685,"description":686,"useCases":687,"indexable":259},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":689,"label":690,"issuer":691,"region":692,"url":693,"description":694,"useCases":479,"indexable":259},"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":696,"label":697,"issuer":698,"region":155,"url":699,"description":700,"useCases":701,"indexable":259},"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":703,"label":704,"issuer":705,"region":155,"url":706,"description":707,"useCases":701,"indexable":259},"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":709,"label":710,"issuer":711,"region":563,"url":712,"description":713,"useCases":714,"indexable":259},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":716,"label":717,"issuer":160,"region":155,"url":718,"description":719,"useCases":714,"indexable":259},"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":721,"label":722,"issuer":723,"region":166,"url":724,"description":725,"useCases":714,"indexable":259},"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.",1790598298359]