[{"data":1,"prerenderedAt":656},["ShallowReactive",2],{"uc-straight-through-small-business-cyber-underwriting":3,"uc-regulations":435},{"useCase":4,"evidence":210,"blitsAiDeployments":300,"benchmarks":301,"indicative":314,"related":317,"indexability":433,"includeUnpublished":216},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":24,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":44,"valueDrivers":45,"kpis":50,"indicativeValue":57,"macroEstimates":92,"feasibility":93,"implementation":107,"risk":153,"blitsAi":184,"faq":186,"related":199,"datePublished":205,"dateModified":205,"lastVerified":205,"changelog":206,"slug":209},"AI straight through underwriting for small business cyber insurance","Straight through cyber underwriting","AI straight through cyber insurance underwriting","AI scans a business's external footprint, scores cyber risk and issues a bindable quote with no underwriter. At-Bay quotes in under 2 minutes up to $100M revenue.","published","Straight through cyber underwriting is AI that scans a small business's external footprint, scores its cyber risk and issues a bindable quote without an underwriter touching it. Flagged or large risks are referred to an underwriter, and the corrections underwriters make can be used to retrain the model.",[12,13,14],"automated cyber underwriting","instant cyber quotes","touchless underwriting for small business",[16],"insurance",[18,19],"underwriting","risk-management",[21,22,23],"prediction-and-scoring","classification-and-routing","agentic-workflow",[25,26,27],"api","email","internal-tools","back-office","supervised-agent","early-adopters","small-commercial-underwriting","A small business cyber submission that waits for an underwriter delays the broker's answer, and At-Bay\nsays its Mailbot quotes small accounts automatically, which frees its underwriters for more\nsophisticated work. The usual alternative, a long questionnaire, is slow for the applicant, and\nCoalition says its scan based reports shorten lengthy questionnaires and cut the quoting process\ntime in half. A questionnaire also depends on the applicant's own answers about controls such as\npatching, multi factor authentication and backups.\n\nThe risk also moves. Coalition reports that cyber claim costs for small businesses rose 56% to an\naverage of $149,000 in the year before its 2022 post, and At-Bay's chief technology officer says\nassessing risk cannot be done once, nor by asking questions or running a vulnerability scan. Insurers that sell through brokers and platforms therefore try to replace the questionnaire\nwith outside in data on each applicant, score it with a model, and keep underwriters for the\naccounts that need judgment.\n\nThis page covers the automated decision itself. Preparing a file for a human underwriter is\ncovered by submission triage, and drafting the risk narrative by the underwriting copilot.",[34,39],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"Coalition says cyber claims costs for small businesses increased 56% to an average of $149,000 in the year before its 2022 post, and that the average ransom demand made to its policyholders rose more than 20% to $1.8 million.","Empowering Brokers with Active Risk Assessment","https:\u002F\u002Fwww.coalitioninc.com\u002Fblog\u002Fbroker-education\u002Factive-risk-assessment-improves-insurance-underwriting",2022,{"statement":40,"sourceTitle":41,"sourceUrl":42,"year":43},"At-Bay says that over three years of assessing email providers it found that customers of Microsoft 365 are twice as likely to experience a claim as customers of Google Workspace.","New Threats, New Tech: How Innovation Is Redefining Cyber Risk","https:\u002F\u002Fwww.at-bay.com\u002Farticles\u002Finnovation-redefining-cyber-risk\u002F",2025,"1. **Take a short application.** The broker or a partner platform submits a short application\n   through a portal or an API. Coalition says its scanning lets it drop many questions, and At-Bay's\n   API takes a short set of questions.\n2. **Scan the outside.** The insurer scans the applicant's internet facing systems the way an\n   attacker would, enumerates its technology and checks it against vulnerability and dark web data.\n   Coalition compares the footprint with its own incident response and claims data to build a\n   personalized risk score.\n3. **Score and price.** A model prices the risk. Rules sort the quote into automatic or referred:\n   At-Bay quotes automatically up to $100 million in revenue and refers larger clients to an\n   underwriter. Coalition describes heuristics that decide which quotes a human reviews.\n4. **Issue a bindable quote.** Eligible applicants receive quotes in seconds or minutes, which the\n   broker can bind. Small accounts can also arrive by email, where a bot reads the submission and\n   replies with a quote, as At-Bay describes.\n5. **Refer what the model should not decide.** Underwriters price the referred risks. In Coalition's\n   active learning design a quote may be escalated to a human reviewer when the model price falls\n   outside an acceptable range, and the experts' price adjustments are used to train the model.\n6. **Keep watching after binding.** Coalition says it continues assessments and alerting\n   throughout the policy period, and Cowbell says its closed loop approach bundles continuous risk\n   assessment with pre and post breach risk improvement services.",[46,47,48,49],"speed","cost-to-serve","revenue-growth","risk-reduction",[51,52,53,54,55,56],"automation-rate","cycle-time-days","processing-time-reduction","conversion-rate-uplift","accuracy","cost-reduction",{"referenceOrg":58,"inputs":59,"formula":87,"currency":88,"period":89,"resultLabel":90,"caveat":91},"A cyber insurer receiving 20,000 small business submissions a year",[60,66,73,80],{"key":61,"label":62,"low":63,"high":63,"unit":64,"note":65},"submissions","Small business submissions per year",20000,"submissions per year","The reference insurer.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"autoShare","Share of submissions quoted without an underwriter",0.5,0.8,"fraction of submissions","Editorial assumption. At-Bay says its platform gives automatic quotes for most API submissions (https:\u002F\u002Fwww.at-bay.com\u002Fabout\u002Fpartnerships\u002F). Measure on your own appetite.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"minutesPerSubmission","Underwriter time per small business submission today",20,45,"minutes per submission","Editorial assumption. Replace with a time study of your own small business desk.",{"key":81,"label":82,"low":83,"high":84,"unit":85,"note":86},"costPerHour","Fully loaded cost of an underwriting hour",60,100,"USD per hour","Editorial assumption. Replace with your own fully loaded cost.","submissions * autoShare * minutesPerSubmission \u002F 60 * costPerHour","USD","per year","Underwriting capacity released","Capacity released, not cash saved, unless headcount changes. It leaves out the larger effects of quoting faster (more business bound) and of selecting risk better, the loss experience of automatically quoted accounts, and the cost of scanning data, models and integration.",[],{"complexity":94,"complexityNote":95,"dataPrerequisites":96,"integrations":101},"high","The decision engine needs an outside in data pipeline that scans applicants and a pricing model trained on the insurer's own claims, so the loss history is the limiting input. Around those sit appetite and referral rules the actuaries and underwriting management sign off, a broker portal or API, and a policy system that can bind and issue without manual steps. An incumbent has to plan for the policy administration and distribution integration as well as the model.",[97,98,99,100],"At least a few years of cyber claims with the applicant's technology profile at the time of binding","Scanning and threat data covering the applicant's domains, cloud and remote access exposure","Written appetite and referral rules per class and revenue band","A labelled record of which quotes underwriters changed and by how much",[102,103,104,105,106],"Broker portal and partner API for application and quote","Scanning and threat intelligence data sources","Rating engine and policy administration system that can bind and issue automatically","Underwriting workbench for referrals and corrections","Email intake for small accounts",{"steps":108,"guardrails":127,"humanInTheLoop":133,"kpisToInstrument":134,"failureModes":140},[109,112,115,118,121,124],{"title":110,"detail":111},"Start with the narrow band the model can learn","Pick revenue and class bands where you have claims history, such as technology firms under a revenue limit, and quote only those automatically at first. Refer everything else.",{"title":113,"detail":114},"Replace questions with data, one question at a time","For each questionnaire item, test whether a scan or an external data point answers it as well. Remove the question only when the data agrees with the answer on a sample of past applicants.",{"title":116,"detail":117},"Build the human review loop first","Define which quotes go to an underwriter, record the price the human sets, and use the difference to improve the model, as Coalition describes with active learning.",{"title":119,"detail":120},"Back test before binding anything","Score a year of past submissions, compare the model's price and referral decision with what underwriters did, and look at the segments where the two disagree most.",{"title":122,"detail":123},"Release in stages with a loss watch","Move from shadow mode, to automatic quotes that underwriters sample, to bindable quotes in one channel. Track claims frequency of the automatic group against the referred group.",{"title":125,"detail":126},"Plan the policy period work","Decide what happens when a scan finds a new exposure after binding: an alert to the insured, a remediation offer, or a referral at renewal.",[128,129,130,131,132],"Hard appetite limits (class, revenue, limits) enforced in rules outside the model","Automatic quotes capped at a policy limit and revenue band until loss experience is proven","Scanning limited to passive, publicly visible information, with the legal basis checked per country","Every quote stores the data, model version and rules that produced it","Pricing models tested for unfair discrimination between segments before release","Underwriters price and decide the referred risks, review a weekly sample of automatic quotes, and own the appetite and referral rules. Actuaries sign off model changes, and a person approves any change that widens the revenue band or limits that are quoted automatically.",[135,136,137,138,139],"Share of in appetite submissions quoted automatically, and share bound","Time from submission to bindable quote, by channel","Loss ratio of automatic quotes versus referred quotes","Share of automatic quotes that an underwriter later changes, and by how much","Questions removed from the application and the applicant drop off rate",[141,144,147,150],{"title":142,"detail":143},"Scan data read as a verdict","An external scan sees what is exposed, not what is controlled inside. A model that treats a missed finding as safe misprices the applicants whose controls the scan cannot see. Keep questions that scans cannot answer.",{"title":145,"detail":146},"Adverse selection in the automatic band","Fast, cheap quotes attract the applicants who expect to need them. Watch the loss ratio of the automatic group separately from the rest of the book.",{"title":148,"detail":149},"Model drift as attackers change","Claims in cyber move quickly, and a model trained on last year's losses prices the wrong signals. Retrain on a schedule and track how often underwriters override the model.",{"title":151,"detail":152},"Aggregation risk hidden by speed","Thousands of small policies that share one cloud provider or software product form one large exposure. Measure accumulation across the automatically written book, not per policy.",{"euAiAct":154,"regulations":157,"guidance":164,"controls":176,"incidents":183},{"tier":155,"basis":156},"context-dependent","Annex III point 5(c) covers risk assessment and pricing of natural persons in life and health insurance. Underwriting of business cyber insurance is not listed, so the quote decision itself carries no high risk duties. The tier would change if the same engine were used to price life or health cover for individuals. Where an email or chat agent replies to brokers or applicants who are natural persons, the transparency duty of Article 50 applies unless it is obvious from the context that they are dealing with AI.",[158,159,160,161,162,163],"eu-ai-act","gdpr","solvency-ii","dora","nist-ai-rmf","iso-42001",[165,171],{"title":166,"issuer":167,"region":168,"url":169,"note":170},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","europe","https:\u002F\u002Fwww.eiopa.europa.eu\u002Fpublications\u002Fopinion-artificial-intelligence-governance-and-risk-management_en","Clarifies how existing insurance legislation, including Solvency II and the Insurance Distribution Directive, applies to insurance AI systems that are neither prohibited nor high risk under the AI Act. It is addressed to national supervisors.",{"title":172,"issuer":173,"region":168,"url":174,"note":175},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 5(c) names life and health insurance risk assessment and pricing of natural persons; business cyber underwriting is outside it.",[177,178,179,180,181,182],"AI inventory entry per model with an accountable underwriting and actuarial owner","Model documentation, back test results and sign off for every release","Versioned appetite, referral and limit rules","Audit trail from each quote to the scan data, model and rules used","Periodic review of override rates, loss ratio by channel and accumulation","Legal review of scanning practices per jurisdiction, and a way for an applicant to see and correct scan findings",[],{"howToBuild":185},"The scoring model and the scanning data come from the insurer or specialist providers. On Blits.ai\nthe workflow around them runs as an **agentic workflow**, started from the broker portal through\nthe **REST API channel** or from a flow on the inbound **email channel** for small accounts.\n**Custom functions** call the scanning service, the rating engine and the policy system, and a\n**SQL knowledge base** gives the agent read access to account history and prior quotes. An\n**agent with structured output** reads emailed submissions into the application schema.\n\nReferrals go through **human in the loop approval** above a configurable threshold, so an\nunderwriter confirms before a quote leaves the building. **PII masking** at the gateway masks\nidentifiers before they reach a model, every run has a full **audit trail and trace**, and\n**test suites** replay past submissions before an appetite or model change goes live.\n**Monitors** run scheduled checks against the agent and alert by email or webhook on failure and\nrecovery. The platform is **model agnostic** and can run in **EU or UAE data residency** regions.",[187,190,193,196],{"question":188,"answer":189},"How fast can an automated cyber quote be?","At-Bay says its API generates quotes in less than 2 minutes for businesses up to $100M in revenue, and Cowbell says its platform compresses the process from submission to issue to less than 5 minutes. Both figures come from the insurers' own pages.",{"question":191,"answer":192},"Are all small business cyber quotes automatic?","No. At-Bay says its platform gives automatic quotes for most API submissions and that its underwriting team handles referrals and more complex submissions. Coalition describes heuristics that decide which quotes a human reviews, and says a quote may be escalated to a human reviewer when the model price falls outside an acceptable range.",{"question":194,"answer":195},"Is the scan more reliable than the questionnaire?","It sees different things. Coalition says its scanning removes many questions because it can identify, for example, a company's industry and the number of its machines connected to the internet, while At-Bay's technology chief says risk cannot be assessed only by asking questions or running a scan. Insurers use both and add claims data.",{"question":197,"answer":198},"Is automated business cyber underwriting high risk under the EU AI Act?","Business cyber underwriting is not on the Annex III list, which names life and health insurance for natural persons. Existing insurance rules on governance, data quality and fair treatment still apply, as EIOPA's opinion on AI governance sets out.",[200,201,202,203,204],"commercial-underwriting-submission-triage","underwriting-risk-assessment-copilot","conversational-insurance-quote-and-buy","insurance-pricing-and-actuarial-copilot","merchant-underwriting-and-risk-monitoring","2026-10-13",[207],{"date":205,"note":208},"First published","straight-through-small-business-cyber-underwriting",[211,246,277],{"title":212,"useCases":213,"organization":214,"vendors":219,"summary":222,"stage":223,"year":43,"channels":224,"languages":225,"metrics":227,"outcomeDisclosed":236,"sources":237,"verification":241,"grade":243,"id":244,"organizationSlug":245},"At-Bay: automatic cyber and tech E&O quotes for small and mid sized businesses",[209],{"name":215,"anonymized":216,"country":217,"region":218,"industry":16},"At-Bay",false,"US","north-america",[220],{"name":215,"role":221},"in-house","At-Bay, a cyber insurer, assesses applicants with scanning data and machine learning and issues bindable cyber, tech E&O and miscellaneous professional liability quotes automatically, through its broker platform and an API for partner platforms. Its API page says applicants up to $100 million in revenue are eligible for automatic quotes while larger clients are quotable with underwriter approval, and that its underwriting team receives referrals and complex submissions. Its chief technology officer says At-Bay uses AI to automate risk decisions and quoting with minimal human touch for smaller businesses, including a bot that reads emailed submissions and quotes small accounts.","production",[25,26,27],[226],"en",[228],{"kpi":52,"value":229,"unit":230,"qualifier":231,"period":232,"claimant":233,"quote":234,"sourceUrl":235},2,"minutes","up-to","per quote, businesses up to $100M in revenue","organization","Generate quotes in less than 2 minutes for businesses up to $100M in revenue.","https:\u002F\u002Fwww.at-bay.com\u002Fabout\u002Fpartnerships\u002F",true,[238,240],{"url":235,"title":239,"publisher":215},"Partnerships and API",{"url":42,"title":41,"publisher":215},{"level":242,"checkedAt":205},"source-verified","B","at-bay-automated-cyber-quoting",null,{"title":247,"useCases":248,"organization":249,"vendors":251,"summary":253,"stage":223,"year":38,"channels":254,"languages":255,"metrics":256,"outcomeDisclosed":236,"sources":263,"verification":275,"grade":243,"id":276,"organizationSlug":245},"Coalition: scanning and machine learning pricing for automated cyber quotes",[209],{"name":250,"anonymized":216,"country":217,"region":218,"industry":16},"Coalition",[252],{"name":250,"role":221},"Coalition, a cyber insurer, scans an applicant's external footprint when a broker submits a quote and uses the result to remove questions from the application and to build a risk score. Its own technical post describes how quotes that pass heuristics are priced by an automated production model while flagged quotes go to cybersecurity and insurance experts, whose price adjustments are fed back to improve the model (active learning). At the UK launch in 2022 Tom Draper, Head of Insurance, UK, said the company offers brokers the ability to quote and bind in seconds. Its Active Risk Assessment page says the reports shorten lengthy questionnaires for brokers while cutting quoting process time in half. Coalition publishes no share of quotes priced without review.",[25,27],[226],[257],{"kpi":53,"value":258,"unit":259,"qualifier":260,"period":261,"claimant":233,"quote":262,"sourceUrl":37},50,"percent","exact","quoting process time for brokers using Active Risk Assessment reports","For brokers, the reports shorten lengthy questionnaires and improve accuracy in underwriting cyber and executive risks — while cutting quoting process time in half.",[264,267,268,271],{"url":265,"title":266,"publisher":250},"https:\u002F\u002Fwww.coalitioninc.com\u002Fblog\u002Fimproving-cyber-underwriting-with-active-learning","Improving Cyber Underwriting with Active Learning",{"url":37,"title":36,"publisher":250},{"url":269,"title":270,"publisher":250},"https:\u002F\u002Fwww.coalitioninc.com\u002Fblog\u002Fcyber-insurance\u002Fai-future-actuarial-science","AI and the Future of Actuarial Science",{"url":272,"title":273,"publisher":250,"date":274},"https:\u002F\u002Fwww.coalitioninc.com\u002Fannouncements\u002Fcoalition-active-insurance-pioneer-uk-launch-cyber-insurance","Active Insurance Pioneer Coalition Launches in UK Cyber Insurance Market","2022-09-05",{"level":242,"checkedAt":205},"coalition-active-risk-assessment-cyber-quotes",{"title":278,"useCases":279,"organization":280,"vendors":282,"summary":284,"stage":223,"year":285,"channels":286,"languages":287,"metrics":288,"outcomeDisclosed":236,"sources":294,"verification":298,"grade":243,"id":299,"organizationSlug":245},"Cowbell: continuous AI underwriting for small and mid sized business cyber insurance",[209],{"name":281,"anonymized":216,"country":217,"region":218,"industry":16},"Cowbell",[283],{"name":281,"role":221},"Cowbell, a cyber insurer for small and mid sized enterprises, describes in its March 2021 Series A announcement an AI based approach to risk selection and pricing. It calls Cowbell Factors its continuous risk assessment services, says it has a risk pool of 10 million continuously monitored organizations, and says its continuous underwriting platform compresses the insurance process from submission to issue to less than 5 minutes. The figures are the company's own and no independent measure is given.",2021,[25,27],[226],[289],{"kpi":52,"value":290,"unit":230,"qualifier":231,"period":291,"claimant":233,"quote":292,"sourceUrl":293},5,"submission to issue","In its unique AI-based approach to risk selection and pricing, Cowbell’s continuous underwriting platform, powered by Cowbell Factors, compresses the insurance process from submission to issue to less than 5 minutes.","https:\u002F\u002Fcowbell.insure\u002F?p=7413",[295],{"url":293,"title":296,"publisher":281,"date":297},"Cowbell Cyber Raises $20 Million in Series A Funding to Scale AI-powered Cyber Insurance Offering","2021-03-11",{"level":242,"checkedAt":205},"cowbell-continuous-cyber-underwriting",0,[302,308],{"kpi":52,"label":303,"unit":230,"aggregate":216,"higherIsBetter":216,"n":300,"nUpTo":229,"median":245,"min":245,"max":245,"byClaimant":304,"vendorOnly":216,"points":305},"Cycle time",{"organization":300,"vendor":300,"regulator":300,"independent":300},[306,307],{"evidenceId":299,"organization":281,"value":290,"qualifier":231,"claimant":233,"grade":243,"pooled":216},{"evidenceId":244,"organization":215,"value":229,"qualifier":231,"claimant":233,"grade":243,"pooled":216},{"kpi":53,"label":309,"unit":259,"aggregate":236,"higherIsBetter":236,"n":310,"nUpTo":300,"median":258,"min":258,"max":258,"byClaimant":311,"vendorOnly":216,"points":312},"Cycle time reduction",1,{"organization":310,"vendor":300,"regulator":300,"independent":300},[313],{"evidenceId":276,"organization":250,"value":258,"qualifier":260,"claimant":233,"grade":243,"pooled":236},{"low":315,"high":316},200000,1200000,[318,344,365,384,407],{"slug":200,"title":319,"shortTitle":320,"definition":321,"status":9,"industries":322,"functions":323,"patterns":325,"audience":28,"autonomy":29,"adoptionStage":30,"segment":18,"evidenceCount":327,"publicEvidenceCount":327,"organizations":328,"bestGrade":243,"headline":338,"lastVerified":343,"indexable":236},"AI for commercial underwriting submission intake and triage","Underwriting submission triage","AI that reads incoming broker submissions for commercial insurance so underwriters open a complete, prioritized file instead of an inbox. It reads emails, applications, schedules of values, loss runs and supplements, extracts the risk data into a structured record, checks clearance and appetite, and enriches and ranks the risk with internal and third party data.",[16],[18,324],"operations",[326,22,21,23],"document-processing",9,[329,330,331,332,333,334,335,336,337],"American International Group","AXIS Capital","CNA Financial","Generali Global Corporate & Commercial","Hiscox","Kinsale Capital Group","Markel","Paragon Insurance Group","Skyward Specialty Insurance Group",{"kpi":55,"label":339,"unit":259,"n":310,"nUpTo":300,"kind":340,"value":341,"qualifier":342,"claimant":233,"organization":336,"vendorReported":216},"Accuracy","reported",98,"approximately","2026-09-27",{"slug":201,"title":345,"shortTitle":346,"definition":347,"status":9,"industries":348,"functions":349,"patterns":350,"audience":354,"autonomy":355,"adoptionStage":30,"segment":18,"evidenceCount":356,"publicEvidenceCount":356,"organizations":357,"bestGrade":243,"headline":362,"lastVerified":364,"indexable":236},"AI copilot for underwriting risk assessment","Underwriting risk assessment copilot","An underwriting risk assessment copilot is an AI tool that assembles everything relevant to a risk, such as the submission, loss history and underwriting guidelines, for an insurance underwriter. It also gathers third party data and public information, highlights exposures and gaps against the insurer's underwriting guidelines, and drafts the underwriting narrative or referral note, while the underwriter makes and signs every decision.",[16],[18,19],[351,352,353,23],"rag-knowledge-assistant","summarization","content-generation","employee-facing","copilot",8,[358,329,359,360,332,333,337,361],"Accelerant Holdings","Arch Capital Group","Bowhead Specialty","Zurich North America",{"kpi":53,"label":309,"unit":259,"n":310,"nUpTo":300,"kind":340,"value":258,"qualifier":260,"claimant":363,"organization":332,"vendorReported":236},"vendor","2026-09-26",{"slug":202,"title":366,"shortTitle":367,"definition":368,"status":9,"industries":369,"functions":370,"patterns":373,"audience":377,"autonomy":29,"adoptionStage":30,"segment":378,"evidenceCount":379,"publicEvidenceCount":379,"organizations":380,"bestGrade":243,"headline":245,"lastVerified":343,"indexable":236},"Conversational AI for insurance quote and buy","Conversational quote and buy","Conversational AI for insurance quote and buy is a customer facing agent that sells insurance directly in a conversation, asking rating questions and returning a price. It asks the rating questions in plain language, explains cover options, returns a price from the insurer's rating engine, handles objections and takes payment to bind the policy, with a licensed human available for advice and anything outside its limits.",[16],[371,372],"sales","customer-service",[374,23,375,376],"conversational-agent","recommendation-and-personalization","voice-agent","customer-facing","distribution",3,[381,382,383],"Aviva","Lemonade","Plymouth Rock Assurance",{"slug":203,"title":385,"shortTitle":386,"definition":387,"status":9,"industries":388,"functions":389,"patterns":392,"audience":354,"autonomy":355,"adoptionStage":30,"segment":394,"evidenceCount":327,"publicEvidenceCount":327,"organizations":395,"bestGrade":243,"headline":403,"lastVerified":364,"indexable":236},"AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","An insurance pricing and actuarial copilot is AI that speeds up the work of pricing and actuarial teams, from risk and demand model building to natural language analysis of data. It builds automated, transparent risk and demand models and analyzes rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[16],[390,19,391],"product-and-pricing","analytics-and-reporting",[21,393,23,352],"code-generation","pricing",[358,396,397,398,399,400,334,401,402],"AXA Spain","Canal Insurance Company","Canopius Group","Europ Assistance","Generali France","MAIF","Wakam",{"kpi":404,"label":405,"unit":406,"n":310,"nUpTo":300,"kind":340,"value":290,"qualifier":260,"claimant":233,"organization":400,"vendorReported":216},"productivity-gain","Productivity gain","multiplier",{"slug":204,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":415,"patterns":418,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":356,"publicEvidenceCount":356,"organizations":420,"bestGrade":243,"headline":429,"lastVerified":343,"indexable":236},"AI for merchant underwriting and risk monitoring","Merchant underwriting and monitoring","AI merchant underwriting and risk monitoring helps acquirers, payment facilitators and software platforms with embedded payments decide which merchants to accept and on what terms, and watch for risk afterward. It checks what a business really sells and how risky it is at onboarding, then watches every active merchant for changes in behaviour, ranking the few that need an analyst so fraud, prohibited trade and credit losses are caught early.",[412,413,414],"payments","technology","banking",[416,417,19],"onboarding-and-kyc","fraud-prevention",[21,419,22,352,23],"anomaly-detection",[421,422,423,424,425,426,427,428],"Airwallex","Autura","Cherry Technologies","Ignition","Phorest","Tekmetric","Visa","Weave Communications",{"kpi":430,"label":431,"unit":259,"n":229,"nUpTo":300,"kind":340,"value":432,"qualifier":342,"claimant":363,"organization":428,"vendorReported":236},"alert-volume-reduction","Alert volume reduction",89,{"indexable":236,"reasons":434},[],[436,441,446,453,459,466,471,478,486,493,500,506,512,519,526,533,539,545,552,557,561,568,574,581,586,591,597,602,608,614,621,627,633,639,645,650],{"id":158,"label":437,"issuer":173,"region":168,"url":438,"description":439,"useCases":440,"indexable":236},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":159,"label":442,"issuer":173,"region":168,"url":443,"description":444,"useCases":445,"indexable":236},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":163,"label":447,"issuer":448,"region":449,"url":450,"description":451,"useCases":452,"indexable":236},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":162,"label":454,"issuer":455,"region":218,"url":456,"description":457,"useCases":458,"indexable":236},"NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":460,"label":461,"issuer":462,"region":168,"url":463,"description":464,"useCases":465,"indexable":236},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":161,"label":467,"issuer":173,"region":168,"url":468,"description":469,"useCases":470,"indexable":236},"DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":472,"label":473,"issuer":474,"region":168,"url":475,"description":476,"useCases":477,"indexable":236},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":479,"label":480,"issuer":481,"region":482,"url":483,"description":484,"useCases":485,"indexable":236},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":487,"label":488,"issuer":489,"region":482,"url":490,"description":491,"useCases":492,"indexable":236},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":494,"label":495,"issuer":496,"region":218,"url":497,"description":498,"useCases":499,"indexable":236},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":501,"label":502,"issuer":173,"region":168,"url":503,"description":504,"useCases":505,"indexable":236},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":507,"label":508,"issuer":509,"region":449,"url":510,"description":511,"useCases":505,"indexable":236},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":513,"label":514,"issuer":515,"region":168,"url":516,"description":517,"useCases":518,"indexable":236},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":520,"label":521,"issuer":522,"region":218,"url":523,"description":524,"useCases":525,"indexable":236},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":527,"label":528,"issuer":529,"region":449,"url":530,"description":531,"useCases":532,"indexable":236},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":534,"label":535,"issuer":173,"region":168,"url":536,"description":537,"useCases":538,"indexable":236},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":540,"label":541,"issuer":542,"region":218,"url":543,"description":544,"useCases":538,"indexable":236},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":546,"label":547,"issuer":548,"region":218,"url":549,"description":550,"useCases":551,"indexable":236},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":553,"label":554,"issuer":173,"region":168,"url":555,"description":556,"useCases":551,"indexable":236},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":160,"label":558,"issuer":173,"region":168,"url":559,"description":560,"useCases":551,"indexable":236},"Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":562,"label":563,"issuer":564,"region":218,"url":565,"description":566,"useCases":567,"indexable":236},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":569,"label":570,"issuer":571,"region":449,"url":572,"description":573,"useCases":567,"indexable":236},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":575,"label":576,"issuer":577,"region":168,"url":578,"description":579,"useCases":580,"indexable":236},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":582,"label":583,"issuer":173,"region":168,"url":584,"description":585,"useCases":580,"indexable":236},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":587,"label":588,"issuer":173,"region":168,"url":589,"description":590,"useCases":580,"indexable":236},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":592,"label":593,"issuer":481,"region":482,"url":594,"description":595,"useCases":596,"indexable":236},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":598,"label":599,"issuer":173,"region":168,"url":600,"description":601,"useCases":596,"indexable":236},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":603,"label":604,"issuer":605,"region":218,"url":606,"description":607,"useCases":356,"indexable":236},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":609,"label":610,"issuer":173,"region":168,"url":611,"description":612,"useCases":613,"indexable":236},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":615,"label":616,"issuer":617,"region":618,"url":619,"description":620,"useCases":290,"indexable":236},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":622,"label":623,"issuer":173,"region":168,"url":624,"description":625,"useCases":626,"indexable":236},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":628,"label":629,"issuer":630,"region":168,"url":631,"description":632,"useCases":626,"indexable":236},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":634,"label":635,"issuer":636,"region":168,"url":637,"description":638,"useCases":626,"indexable":236},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":640,"label":641,"issuer":642,"region":482,"url":643,"description":644,"useCases":379,"indexable":236},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":646,"label":647,"issuer":173,"region":168,"url":648,"description":649,"useCases":379,"indexable":236},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":651,"label":652,"issuer":653,"region":218,"url":654,"description":655,"useCases":379,"indexable":236},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904361321]