[{"data":1,"prerenderedAt":650},["ShallowReactive",2],{"uc-insurance-pricing-and-actuarial-copilot":3,"uc-regulations":445},{"useCase":4,"evidence":207,"blitsAiDeployments":336,"benchmarks":337,"indicative":349,"related":352,"indexability":443,"includeUnpublished":213},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":21,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":40,"indicativeValue":46,"macroEstimates":80,"feasibility":81,"implementation":94,"risk":137,"blitsAi":183,"faq":185,"related":195,"datePublished":201,"dateModified":201,"lastVerified":202,"changelog":203,"slug":206},"AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI for insurance pricing and actuarial teams","AI pricing copilots automate data preparation and model search while actuaries sign off the rates. Generali France reports modelling five times faster with Akur8.","published","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.",[12,13,14],"AI for insurance pricing","actuarial modelling automation","ratemaking copilot",[16],"insurance",[18,19,20],"product-and-pricing","risk-management","analytics-and-reporting",[22,23,24,25],"prediction-and-scoring","code-generation","agentic-workflow","summarization",[27],"internal-tools","employee-facing","copilot","early-adopters","pricing","Pricing and actuarial teams work to fixed review cycles with a high volume of work. Building or\nupdating a risk model in traditional tools can take weeks of data preparation, variable selection\nand code, often split across separate tools such as SAS, Python or R, where every change to the\ndata means manual code updates that are hard to version and audit. Competitor filings, experience\nstudies and reserving reviews compete for the same people.\n\nThe constraint is not only speed. Pricing models must be explainable to supervisors, tested for\nunfair discrimination and consistent with fair value rules, so machine learning that improves\naccuracy but cannot be explained is hard to defend. The opportunity is AI that removes the manual\nwork while keeping models transparent and actuaries in control.",[],"1. **Prepare the data.** The platform imports policy, claims and quote data, handles missing values\n   and builds candidate features, including approved external data.\n2. **Build transparent models faster.** Automated search explores thousands of variable\n   combinations and interactions and proposes interpretable models (for example generalized linear\n   or additive models), while actuaries choose and adjust the final model.\n3. **Set rates.** Actuaries compare pricing scenarios and their effect on volume, loss ratio and\n   fairness tests, then approve the rates.\n4. **Ask questions in plain language.** A generative assistant answers questions over rate\n   filings, experience data and reserving outputs, and drafts code or documentation for review.\n5. **Deploy and monitor.** Approved rates go to the rating engine through an audited deployment,\n   and model performance is monitored against actual experience.",[36,37,38,39],"speed","employee-productivity","risk-reduction","compliance",[41,42,43,44,45],"productivity-gain","cycle-time-days","processing-time-reduction","accuracy","error-reduction",{"referenceOrg":47,"inputs":48,"formula":75,"currency":76,"period":77,"resultLabel":78,"caveat":79},"An insurer with a pricing and actuarial team of 20 people",[49,55,62,68],{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54},"actuaries","Pricing and actuarial staff",20,"people","The reference insurer.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"modellingShare","Share of their time spent on model building and data preparation",0.3,0.5,"fraction of working time","Editorial assumption. Replace with your own time allocation.",{"key":63,"label":64,"low":59,"high":65,"unit":66,"note":67},"timeSaved","Share of that time the AI removes",0.75,"fraction of modelling time","Conservative against the evidence on this page (Generali France's actuarial studies manager reports modelling five times faster; Akur8 reports that Europ Assistance cut work that took weeks to one or two days).",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"costPerPerson","Fully loaded annual cost per person",120000,180000,"USD per year","Editorial assumption. Replace with your own fully loaded cost.","actuaries * modellingShare * timeSaved * costPerPerson","USD","per year","Actuarial capacity released","Capacity released, not cash saved. It leaves out the usually larger value of better risk selection and faster rate changes on the loss ratio, software licences, and the validation and governance effort that pricing models need.",[],{"complexity":82,"complexityNote":83,"dataPrerequisites":84,"integrations":89},"high","Automated modelling platforms already run in production at insurers such as MAIF and Generali France; the effort is in data quality, integration with rating engines, model governance and fairness testing. Generative assistants for actuarial analysis are newer and need controls on data access and on code they generate.",[85,86,87,88],"Clean, joined policy, exposure, claims and quote data at the level pricing needs","Approved external data sources with documented use conditions","Model governance standards and fairness testing methods","Rate filings and experience studies in a searchable form",[90,91,92,93],"Data warehouse or lakehouse holding policy and claims data","Rating engine for deployment","Model risk management inventory","Version control and documentation repository",{"steps":95,"guardrails":111,"humanInTheLoop":117,"kpisToInstrument":118,"failureModes":124},[96,99,102,105,108],{"title":97,"detail":98},"Pick one product with a rate review due","Run the new approach in parallel with the existing process on a real rate review so the results can be compared on accuracy, time and explainability.",{"title":100,"detail":101},"Keep models explainable by design","Prefer methods that produce interpretable models (GLM or GAM style) or add robust explanations, because regulators and fair value reviews will ask why each factor is there.",{"title":103,"detail":104},"Build fairness testing into the workflow","Test rating factors and outcomes for proxies of protected characteristics before approval, as rules such as Colorado's SB21-169 require for covered lines.",{"title":106,"detail":107},"Govern generated code and analysis","Treat code or documentation drafted by a generative assistant like a junior's work: reviewed, tested and versioned before it touches production models.",{"title":109,"detail":110},"Monitor after deployment","Compare predicted and actual experience monthly and set triggers for review.",[112,113,114,115,116],"Actuaries select and sign off every model and rate; nothing deploys without approval","Fairness and proxy testing before any new factor is approved","Full lineage from data to deployed rate, with version control","Generative assistants have read only access to data and cannot deploy","Price optimization constrained by fair value and renewal pricing rules where they apply","Actuaries own model selection, rate approval and professional sign off. Model risk management validates pricing models independently, and compliance reviews fairness test results and rate filings before they go to regulators.",[119,120,121,122,123],"Elapsed days from data extract to approved model, per review","Models built or refreshed per actuary per quarter","Predictive lift of new models versus the current rates on holdout data","Fairness test results per model version","Actual versus expected loss ratio after deployment",[125,128,131,134],{"title":126,"detail":127},"More accurate, less explainable","A complex model wins on lift but cannot be explained in a filing. Set explainability requirements before modelling starts.",{"title":129,"detail":130},"Proxy discrimination","External data or interactions act as proxies for protected characteristics. Test outcomes by group and document the reasons for every factor.",{"title":132,"detail":133},"Speed without governance","Faster models mean more changes than validation can keep up with. Scale validation capacity with modelling capacity.",{"title":135,"detail":136},"Unreviewed generated code","Code drafted by an assistant contains a subtle error that flows into rates. Require review and tests for every change.",{"euAiAct":138,"regulations":141,"guidance":149,"controls":172,"incidents":178},{"tier":139,"basis":140},"context-dependent","Pricing and risk assessment of natural persons for life and health insurance is high risk under Annex III point 5(c). Pricing for property and casualty products, and actuarial analysis that does not price individuals, are not listed, although supervisors still expect sound model governance.",[142,143,144,145,146,147,148],"eu-ai-act","gdpr","uk-consumer-duty","nist-ai-rmf","iso-42001","solvency-ii","eu-idd",[150,156,161,167],{"title":151,"issuer":152,"region":153,"url":154,"note":155},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(c) makes AI for risk assessment and pricing of natural persons in life and health insurance high risk.",{"title":157,"issuer":158,"region":153,"url":159,"note":160},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Covers fairness, data governance, explainability and human oversight for AI in insurance, including pricing models outside the high risk list.",{"title":162,"issuer":163,"region":164,"url":165,"note":166},"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","Holds insurers accountable for testing external consumer data, algorithms and predictive models so they do not unfairly discriminate on the basis of a protected class; amended Regulation 10-1-1 sets governance requirements for life, private passenger auto and health insurers.",{"title":168,"issuer":169,"region":153,"url":170,"note":171},"PS21/5: General insurance pricing practices market study, feedback to CP20/19 and final rules","Financial Conduct Authority","https://www.fca.org.uk/publication/policy/ps21-5.pdf","Limits UK home and motor renewal prices to the equivalent new business price, which constrains price optimization models.",[173,174,175,176,177],"Model inventory entries with owners, validation status and approval history","Fairness and proxy testing records per model version","Rate change approval workflow with actuarial sign off","Access controls separating analysis tools from production deployment","Post deployment monitoring with documented triggers",[179],{"title":180,"url":181,"note":182},"Suckers List: How Allstate's Secret Auto Insurance Algorithm Squeezes Big Spenders","https://themarkup.org/allstates-algorithm/2020/02/25/car-insurance-suckers-list","Reporting by The Markup and Consumer Reports on a price adjustment algorithm Allstate filed in Maryland, which regulators rejected as discriminatory; Allstate said its rating plans comply with state laws and regulations. A reminder that pricing models are judged on outcomes, not only on predictive accuracy.",{"howToBuild":184},"Blits.ai does not replace a pricing platform or rating engine. It fits the analysis around them: an\n**AI agent** with **SQL knowledge bases** over experience and filing data answers actuaries'\nquestions in plain language, and a **knowledge base** of rate filings, pricing guidelines and model\ndocumentation, searched with **hybrid retrieval**, answers questions over those documents.\n\n**Agentic workflows** can be triggered on a schedule to prepare recurring analyses (experience\nmonitoring, actual versus expected reports, competitor filing summaries), with **human in the loop\napproval** before results are used, and the file generation tool produces the output documents.\nConnect the databases with read only accounts and **custom functions**, limit the tools a workflow\nmay call with the tool execution policy, use **execution tracing** to inspect every turn and the\nqueries it ran, and run **test suites** that check answers against known figures. The platform is\nmodel agnostic, and data can stay in EU or UAE regions.",[186,189,192],{"question":187,"answer":188},"How much faster does AI make insurance pricing?","Figures published by the vendor Akur8 report large gains: Generali France's actuarial studies manager says modelling is five times faster, and Akur8's case study says Europ Assistance now completes pricing work that took weeks in one or two days. These are not independently audited and do not measure effects on loss ratios.",{"question":190,"answer":191},"Is AI pricing high risk under the EU AI Act?","For life and health insurance of individuals, yes, under Annex III point 5(c). For motor, home and commercial lines the Act does not list pricing as high risk, but fairness, explainability and national pricing rules such as the FCA's renewal pricing rules still apply.",{"question":193,"answer":194},"Do actuaries still decide?","Yes. The tools on this page automate data preparation, variable search and analysis, while actuaries select models and sign off rates. Kinsale reports that AI tool use is most prevalent in its IT, actuarial and analytical teams.",[196,197,198,199,200],"underwriting-risk-assessment-copilot","model-risk-validation-copilot","insurance-renewal-and-retention","governed-text-to-sql-analytics","conversational-insurance-quote-and-buy","2026-09-27","2026-09-26",[204],{"date":201,"note":205},"First published","insurance-pricing-and-actuarial-copilot",[208,237,262,293,318],{"title":209,"useCases":210,"organization":211,"vendors":215,"summary":218,"stage":219,"year":220,"channels":221,"languages":223,"metrics":225,"outcomeDisclosed":213,"sources":226,"verification":232,"grade":234,"id":235,"organizationSlug":236},"Accelerant: AI agents for data ingestion and AI tools for underwriting and actuarial analysis",[196,206],{"name":212,"anonymized":213,"region":214,"industry":16},"Accelerant Holdings",false,"global",[216],{"name":212,"role":217},"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,[27,222],"api",[224],"en",[],[227],{"url":228,"title":229,"publisher":230,"date":231},"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":233,"checkedAt":202},"source-verified","B","accelerant-ai-underwriting-and-actuarial-tools",null,{"title":238,"useCases":239,"organization":241,"vendors":244,"summary":246,"stage":219,"year":220,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":213,"sources":250,"verification":260,"grade":234,"id":261,"organizationSlug":236},"Kinsale Capital: AI driven submission routing and company wide AI tools for underwriting and actuarial teams",[240,206],"commercial-underwriting-submission-triage",{"name":242,"anonymized":213,"country":243,"region":164,"industry":16},"Kinsale Capital Group","US",[245],{"name":242,"role":217},"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.",[27],[224],[],[251,256],{"url":252,"title":253,"publisher":254,"date":255},"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":257,"title":258,"publisher":254,"date":259},"https://www.sec.gov/Archives/edgar/data/1669162/000166916226000015/knsl-20251231.htm","Kinsale Capital Group Form 10-K for 2025","2026-02-20",{"level":233,"checkedAt":202},"kinsale-ai-submission-routing",{"title":263,"useCases":264,"organization":265,"vendors":268,"summary":272,"stage":219,"year":273,"channels":274,"languages":275,"metrics":276,"outcomeDisclosed":286,"sources":287,"verification":290,"grade":291,"id":292,"organizationSlug":236},"Europ Assistance: pricing model updates in one or two days instead of weeks",[206],{"name":266,"anonymized":213,"country":267,"region":153,"industry":16},"Europ Assistance","FR",[269],{"name":270,"role":271},"Akur8","platform","Europ Assistance adopted Akur8's cloud pricing platform, whose automated modelling cut the time spent running and updating pricing models. The vendor reports that work that took weeks now takes one or two days, that teams can reuse fitted models on new datasets, and that built in documentation lets stakeholders review and challenge the whole pricing process.",2026,[27],[],[277],{"kpi":42,"value":278,"unit":279,"qualifier":280,"period":281,"baseline":282,"claimant":283,"quote":284,"sourceUrl":285},2,"days","up-to","pricing model execution and updates","weeks before the platform","vendor","As a result, what once took weeks in the pricing process can now be completed in just one or two days.","https://www.akur8.com/success-stories/from-weeks-to-one-day-how-europ-assistance-accelerated-pricing-with-akur8",true,[288],{"url":285,"title":289,"publisher":270},"From weeks to one day: how Europ Assistance accelerated pricing with Akur8.",{"level":233,"checkedAt":202},"C","europ-assistance-akur8-pricing",{"title":294,"useCases":295,"organization":296,"vendors":298,"summary":300,"stage":219,"year":273,"channels":301,"languages":302,"metrics":304,"outcomeDisclosed":286,"sources":313,"verification":316,"grade":291,"id":317,"organizationSlug":236},"Generali France: automated, transparent pricing model building with Akur8",[206],{"name":297,"anonymized":213,"country":267,"region":153,"industry":16},"Generali France",[299],{"name":270,"role":271},"Generali France's actuarial studies team uses Akur8, a pricing platform that automates the repetitive parts of building risk models while keeping the process transparent and auditable for actuaries. Its actuarial studies manager says modelling is five times faster and that the shared interface improved communication inside the team.",[27],[303],"fr",[305],{"kpi":41,"value":306,"unit":307,"qualifier":308,"period":309,"claimant":310,"quote":311,"sourceUrl":312},5,"multiplier","exact","speed of pricing model building","organization","Modeling speed is 5x faster, while keeping a thoroughly transparent and auditable process.","https://www.akur8.com/resources/testimonials",[314],{"url":312,"title":315,"publisher":270},"Best actuarial software: Discover Akur8 Customer Reviews",{"level":233,"checkedAt":202},"generali-france-akur8-pricing-models",{"title":319,"useCases":320,"organization":321,"vendors":323,"summary":325,"stage":326,"year":273,"channels":327,"languages":328,"metrics":329,"outcomeDisclosed":213,"sources":330,"verification":334,"grade":291,"id":335,"organizationSlug":236},"MAIF: one pricing platform for data preparation, modelling and geographic models",[206],{"name":322,"anonymized":213,"country":267,"region":153,"industry":16},"MAIF",[324],{"name":270,"role":271},"MAIF, a French mutual insurer, moved its pricing workflow from separate SAS and Python tools into Akur8, where data preparation, model building and geographic modelling happen in one place. The vendor says its machine learning explores thousands of variable combinations in parallel to find the most predictive features while actuaries keep control of the final selection. MAIF's pricing teams now manage several hundred databases of up to 30 million rows and several thousand models and versions on the platform; no time saving is quantified.","scaled",[27],[303],[],[331],{"url":332,"title":333,"publisher":270},"https://www.akur8.com/success-stories/how-maif-cut-modeling-time-and-built-thousands-of-models-with-akur8","How MAIF cut modeling time and built thousands of models with Akur8",{"level":233,"checkedAt":202},"maif-akur8-pricing-models",0,[338,344],{"kpi":41,"label":339,"unit":307,"aggregate":286,"higherIsBetter":286,"n":340,"nUpTo":336,"median":306,"min":306,"max":306,"byClaimant":341,"vendorOnly":213,"points":342},"Productivity gain",1,{"organization":340,"vendor":336,"regulator":336,"independent":336},[343],{"evidenceId":317,"organization":297,"value":306,"qualifier":308,"claimant":310,"grade":291,"pooled":286},{"kpi":42,"label":345,"unit":279,"aggregate":213,"higherIsBetter":213,"n":336,"nUpTo":340,"median":236,"min":236,"max":236,"byClaimant":346,"vendorOnly":213,"points":347},"Cycle time",{"organization":336,"vendor":336,"regulator":336,"independent":336},[348],{"evidenceId":292,"organization":266,"value":278,"qualifier":280,"claimant":283,"grade":291,"pooled":213},{"low":350,"high":351},360000,1350000,[353,377,398,414,431],{"slug":196,"title":354,"shortTitle":355,"definition":356,"status":9,"industries":357,"functions":358,"patterns":360,"audience":28,"autonomy":29,"adoptionStage":30,"segment":359,"evidenceCount":363,"publicEvidenceCount":363,"organizations":364,"bestGrade":234,"headline":372,"lastVerified":202,"indexable":286},"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],[359,19],"underwriting",[361,25,362,24],"rag-knowledge-assistant","content-generation",8,[212,365,366,367,368,369,370,371],"American International Group","Arch Capital Group","Bowhead Specialty","Generali Global Corporate & Commercial","Hiscox","Skyward Specialty Insurance Group","Zurich North America",{"kpi":43,"label":373,"unit":374,"n":340,"nUpTo":336,"kind":375,"value":376,"qualifier":308,"claimant":283,"organization":368,"vendorReported":286},"Cycle time reduction","percent","reported",50,{"slug":197,"title":378,"shortTitle":379,"definition":380,"status":9,"industries":381,"functions":385,"patterns":387,"audience":28,"autonomy":29,"adoptionStage":390,"segment":391,"evidenceCount":392,"publicEvidenceCount":392,"organizations":393,"bestGrade":234,"headline":236,"lastVerified":397,"indexable":286},"AI copilot for model risk validation and monitoring","Model risk validation","A copilot for independent model validation and review, whether run by a bank's validation function, an external tester or a supervisor, that checks model documentation against the model risk standard, generates and scores challenger tests (for generative AI, often with an LLM as a judge calibrated against human experts), watches production models for drift and drafts and consistency checks the validation report. An accountable validator owns every conclusion.",[382,16,383,384],"banking","capital-markets","wealth-and-asset-management",[19,386],"regulatory-compliance",[24,388,362,389],"document-processing","anomaly-detection","emerging","second-line",3,[394,395,396],"European Central Bank (ECB Banking Supervision)","Standard Chartered","United Overseas Bank (UOB)","2026-09-28",{"slug":198,"title":399,"shortTitle":400,"definition":401,"status":9,"industries":402,"functions":403,"patterns":406,"audience":409,"autonomy":29,"adoptionStage":390,"segment":410,"evidenceCount":392,"publicEvidenceCount":392,"organizations":411,"bestGrade":234,"headline":236,"lastVerified":202,"indexable":286},"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],[359,404,405],"customer-service","sales",[22,388,407,408],"conversational-agent","recommendation-and-personalization","back-office","distribution",[369,412,413],"Nsure.com","Zurich Insurance Group",{"slug":199,"title":415,"shortTitle":416,"definition":417,"status":9,"industries":418,"functions":423,"patterns":425,"audience":28,"autonomy":426,"adoptionStage":30,"evidenceCount":392,"publicEvidenceCount":392,"organizations":427,"bestGrade":234,"headline":236,"lastVerified":201,"indexable":286},"Governed text to SQL analytics assistant","Governed SQL analytics","An assistant that turns a business user's plain language question into a query against governed data, runs it under that user's own data permissions and returns the table or chart together with the SQL and the tables used, so routine ad hoc questions no longer queue for the data team.",[419,382,16,420,421,422],"cross-industry","retail-and-ecommerce","technology","pharma-and-life-sciences",[20,424],"it-and-engineering",[407,23,361],"assist",[428,429,430],"Bayer","LinkedIn","Uber Technologies",{"slug":200,"title":432,"shortTitle":433,"definition":434,"status":9,"industries":435,"functions":436,"patterns":437,"audience":439,"autonomy":440,"adoptionStage":30,"segment":410,"evidenceCount":340,"publicEvidenceCount":340,"organizations":441,"bestGrade":234,"headline":236,"lastVerified":201,"indexable":286},"Conversational AI for insurance quote and buy","Conversational quote and buy","A customer facing AI agent that sells insurance directly in a conversation: 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],[405,404],[407,24,408,438],"voice-agent","customer-facing","supervised-agent",[442],"Lemonade",{"indexable":286,"reasons":444},[],[446,451,456,462,468,474,481,486,494,501,507,513,520,527,533,538,545,551,557,563,569,575,581,586,591,598,604,608,613,620,627,633,639,644],{"id":142,"label":447,"issuer":152,"region":153,"url":448,"description":449,"useCases":450,"indexable":286},"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":143,"label":452,"issuer":152,"region":153,"url":453,"description":454,"useCases":455,"indexable":286},"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":146,"label":457,"issuer":458,"region":214,"url":459,"description":460,"useCases":461,"indexable":286},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":145,"label":463,"issuer":464,"region":164,"url":465,"description":466,"useCases":467,"indexable":286},"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":469,"label":470,"issuer":152,"region":153,"url":471,"description":472,"useCases":473,"indexable":286},"dora","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":475,"label":476,"issuer":477,"region":153,"url":478,"description":479,"useCases":480,"indexable":286},"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":144,"label":482,"issuer":169,"region":153,"url":483,"description":484,"useCases":485,"indexable":286},"FCA Consumer Duty","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":487,"label":488,"issuer":489,"region":490,"url":491,"description":492,"useCases":493,"indexable":286},"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":495,"label":496,"issuer":497,"region":490,"url":498,"description":499,"useCases":500,"indexable":286},"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":502,"label":503,"issuer":504,"region":214,"url":505,"description":506,"useCases":52,"indexable":286},"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":508,"label":509,"issuer":510,"region":164,"url":511,"description":512,"useCases":52,"indexable":286},"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":514,"label":515,"issuer":516,"region":153,"url":517,"description":518,"useCases":519,"indexable":286},"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":521,"label":522,"issuer":523,"region":214,"url":524,"description":525,"useCases":526,"indexable":286},"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":528,"label":529,"issuer":152,"region":153,"url":530,"description":531,"useCases":532,"indexable":286},"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":534,"label":535,"issuer":152,"region":153,"url":536,"description":537,"useCases":532,"indexable":286},"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":539,"label":540,"issuer":541,"region":164,"url":542,"description":543,"useCases":544,"indexable":286},"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":546,"label":547,"issuer":152,"region":153,"url":548,"description":549,"useCases":550,"indexable":286},"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":552,"label":553,"issuer":554,"region":164,"url":555,"description":556,"useCases":550,"indexable":286},"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":558,"label":559,"issuer":560,"region":214,"url":561,"description":562,"useCases":550,"indexable":286},"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":564,"label":565,"issuer":152,"region":153,"url":566,"description":567,"useCases":568,"indexable":286},"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":570,"label":571,"issuer":572,"region":164,"url":573,"description":574,"useCases":568,"indexable":286},"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":576,"label":577,"issuer":489,"region":490,"url":578,"description":579,"useCases":580,"indexable":286},"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":582,"label":583,"issuer":152,"region":153,"url":584,"description":585,"useCases":580,"indexable":286},"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":587,"label":588,"issuer":152,"region":153,"url":589,"description":590,"useCases":580,"indexable":286},"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":592,"label":593,"issuer":594,"region":153,"url":595,"description":596,"useCases":597,"indexable":286},"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":599,"label":600,"issuer":601,"region":164,"url":602,"description":603,"useCases":363,"indexable":286},"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":147,"label":605,"issuer":152,"region":153,"url":606,"description":607,"useCases":363,"indexable":286},"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":148,"label":609,"issuer":152,"region":153,"url":610,"description":611,"useCases":612,"indexable":286},"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":614,"label":615,"issuer":616,"region":617,"url":618,"description":619,"useCases":306,"indexable":286},"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":621,"label":622,"issuer":623,"region":153,"url":624,"description":625,"useCases":626,"indexable":286},"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":628,"label":629,"issuer":630,"region":153,"url":631,"description":632,"useCases":626,"indexable":286},"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":634,"label":635,"issuer":636,"region":490,"url":637,"description":638,"useCases":392,"indexable":286},"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":640,"label":641,"issuer":152,"region":153,"url":642,"description":643,"useCases":392,"indexable":286},"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":645,"label":646,"issuer":647,"region":164,"url":648,"description":649,"useCases":392,"indexable":286},"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.",1790598298042]