[{"data":1,"prerenderedAt":563},["ShallowReactive",2],{"uc-parametric-claims-triggering":3,"uc-regulations":341},{"useCase":4,"evidence":172,"blitsAiDeployments":231,"benchmarks":232,"indicative":233,"related":236,"indexability":339,"includeUnpublished":178},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":48,"macroEstimates":76,"feasibility":77,"implementation":90,"risk":127,"blitsAi":149,"faq":151,"related":164,"datePublished":167,"dateModified":167,"lastVerified":167,"changelog":168,"slug":171},"AI for parametric insurance claims triggering","Parametric claims triggering","AI parametric insurance claims triggers","Descartes Underwriting says AI builds its parametric trigger models, while automated systems verify events, so payouts need no on site loss assessment.","published","AI models, trained on historical, simulated and satellite or radar data, that build the index a parametric insurance policy pays against and then measure the triggering event itself, a wildfire's extent once it happens, so a deterministic engine can calculate and release the payout without an adjuster assessing the loss on site.",[12,13,14],"parametric insurance automation","index based insurance triggers","AI driven catastrophe payouts",[16],"insurance",[18,19],"claims","product-and-pricing",[21,22,23],"prediction-and-scoring","computer-vision","synthetic-data-generation",[25],"api","back-office","autonomous","emerging","parametric","Traditional indemnity insurance pays only after a policyholder reports a loss and an assessor\nvisits to confirm it, which delays the money exactly when speed matters most: after a flood, a\ndrought, a hailstorm or a wildfire.\n\nParametric insurance answers a narrower question instead of an assessed loss: not \"how much\ndamage did you suffer\", but \"did the agreed index cross its threshold\". A fixed rule, such as a\nrainfall gauge, a wind speed reading or a satellite derived index checked against a threshold\nset before the policy is sold, can answer that question with deterministic engineering and no\nAI in the trigger at all. The World Bank describes its four\nsovereign catastrophe risk pools, CCRIF, PCRIC, ARC and SEADRIF, which together cover about 40\nlow and middle income countries, as parametric products \"based on official measurements of\nwindspeed or ground motion\", the same fixed rule design, with no AI in the trigger either.\n\nA smaller group of providers puts machine learning to work inside the index itself. Descartes\nUnderwriting says it uses AI, including generative models that simulate thousands of storm\nscenarios and AI that reconstructs past hail events from ground reports, radar, satellite and\nforecast data, to build and calibrate the models that define its triggers. For wildfires, it says AI\nalso measures the event itself once it happens, from post event burn analysis, so the payout\ncan be calculated without anyone visiting the site. That is the part of parametric insurance\nthat is genuinely an AI use case, and it is what this page covers.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"The World Bank describes its four sovereign catastrophe risk pools, covering about 40 low and middle income countries, as parametric products \"based on official measurements of windspeed or ground motion\", a fixed rule design with no AI in the trigger.","Sovereign catastrophe risk pools: 15 years on and still more to come","https://blogs.worldbank.org/en/psd/sovereign-catastrophe-risk-pools-15-years-and-still-more-come",2021,"1. **Simulate and calibrate.** Before any policy is sold, generative models simulate thousands\n   of realistic event scenarios, storms, hail, wildfires or droughts, against decades of\n   historical data, so underwriters can set a threshold and a payout schedule that tracks the\n   real loss closely enough to be worth buying.\n2. **Monitor the live feed.** Once the policy is live, the system pulls satellite, radar or\n   weather station data for the covered area on an ongoing basis, not only when a policyholder\n   calls in.\n3. **Measure the event with AI.** When a wildfire or a flood happens, computer vision models\n   read the satellite imagery to reconstruct the event, identify affected zones and measure its\n   extent and severity, the same assessment a loss adjuster would otherwise travel to make on\n   site.\n4. **Trigger and calculate.** A deterministic engine compares that measurement against the\n   policy's predefined index. When the threshold is crossed, it calculates the payout from the\n   agreed formula and starts payment, with no on site loss assessment and no claim form.\n5. **People handle the exceptions.** Sensor faults, disputed readings and thresholds crossed by\n   a hair's width go to a person before any payment, and every check of the index, whether or\n   not it triggered, is logged for audit.",[39,40,41,42],"speed","cost-to-serve","customer-experience","risk-reduction",[44,45,46,47],"processing-time-reduction","cost-reduction","accuracy","customer-satisfaction",{"referenceOrg":49,"inputs":50,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A parametric weather insurance program covering 50,000 insured sites or policyholders",[51,57,64],{"key":52,"label":53,"low":54,"high":55,"unit":52,"note":56},"policyholders","Policyholders or insured sites covered by the program",20000,80000,"Editorial assumption, replace with your own program size.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"traditionalClaimsCostPerEvent","Manual loss assessment cost the trigger replaces, per policyholder per triggered event",15,40,"USD per policyholder per triggered event","Editorial assumption for a manual loss adjustment or verification visit avoided. Replace with your own claims handling cost.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"eventsPerYear","Trigger events per policyholder per year",0.1,0.5,"triggered events per policyholder per year","Editorial assumption. Parametric triggers are usually calibrated to occasional, not annual, events; a trigger every year would push the premium close to the payout. Replace with your own historical trigger frequency.","policyholders * traditionalClaimsCostPerEvent * eventsPerYear","USD","per year","Claims assessment cost avoided per year","Only the avoided cost of loss assessment. It leaves out the cost of designing and calibrating the AI model, the data licences, and the reinsurance or capital backing the payouts, and it does not capture the value of faster liquidity after a loss, which is the main reason buyers choose parametric cover over indemnity insurance. It also does not fit a program with a single policyholder, such as a sovereign or municipal buyer, where a per policyholder cost has no meaning.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":85},"high","The payment automation itself is straightforward. The real engineering problem is training and calibrating the model against real historical losses so that basis risk, paying too little, too much or to the wrong party, is low enough that the product is worth buying, and keeping the underlying data feed live and unchanged for the life of every policy that references it.",[81,82,83,84],"Historical weather, satellite or sensor data long enough to train and calibrate a model against real, known losses","A live, contracted data feed of the same source for the full life of the policy","An agreed payout formula and schedule signed off by underwriting and actuarial before any policy is sold","Distribution and payment details for the policyholders, or the intermediary that pays them",[86,87,88,89],"Satellite, radar or meteorological data provider API","Policy administration system holding the trigger definition per policy","Payment or disbursement system for the payout itself","Reinsurance or capital markets interface where the underlying risk is passed on",{"steps":91,"guardrails":107,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":117},[92,95,98,101,104],{"title":93,"detail":94},"Calibrate the model against real losses first","Before writing a single policy, test the proposed trigger, whether a fixed rule or an AI model trained on historical and simulated events, against known losses in the area. A trigger that pays out when there was no real loss, or misses a real one, destroys trust immediately.",{"title":96,"detail":97},"Contract a data feed for the life of the policy, not just the launch","The satellite or weather data provider has to stay available, and its methodology unchanged, for as long as any live policy references it. Build in a fallback for when the feed goes down or is disputed.",{"title":99,"detail":100},"Agree the payout schedule before you need it","Define the exact payout at each threshold level with underwriting and actuarial sign off, and publish it to policyholders in plain language before the season starts, not after an event.",{"title":102,"detail":103},"Automate the trigger, not the exceptions","Let the system watch the feed, measure the event and calculate the payout automatically, but route sensor faults, data gaps and near miss thresholds to a person before any payment goes out.",{"title":105,"detail":106},"Report every check, paid or not","Log every time the index approached or crossed a threshold, whether or not it paid, so regulators, reinsurers and policyholders can see that the mechanism worked as designed.",[108,109,110],"A human confirms any payout before disbursement while the program is new, moving to sampling only once the trigger's reliability is proven","A documented fallback, a backup data source or a manual review path, for when the primary feed is unavailable or its readings are disputed","The trigger and payout formula are published to policyholders before the policy is sold, never created or adjusted after an event","Underwriting and actuarial teams design and sign off the model and the payout schedule before any policy is sold. Operations staff review disputed readings, sensor faults and near miss thresholds; once the mechanism is proven, a sample of trigger events is still checked after payment.",[113,114,115,116],"Time from the index crossing its threshold to payment reaching the policyholder","Basis risk, how often a real loss occurred without a trigger or a trigger fired without a comparable loss, checked against ground reports","Share of trigger events resolved without a human exception","Data feed uptime and dispute rate",[118,121,124],{"title":119,"detail":120},"Basis risk nobody explained","A policyholder who suffers real damage but is not paid because the index stayed just under threshold loses trust fast, even though the product worked exactly as designed. Explain the mechanism and its limits in plain language before selling it.",{"title":122,"detail":123},"A single data source with no fallback","If the feed the trigger depends on goes down or is disputed right after a disaster, the whole program stalls at the worst possible moment. Contract a backup source or a manual review path from day one.",{"title":125,"detail":126},"The model drifts from the loss it was built to track","Land use, construction standards or climate patterns change after the model is calibrated, quietly widening the gap between what it measures and what people actually lose. Recalibrate on a fixed schedule, not only after a complaint.",{"euAiAct":128,"regulations":131,"guidance":136,"controls":143,"incidents":148},{"tier":129,"basis":130},"context-dependent","Annex III point 5(c) covers AI systems for risk assessment and pricing in relation to natural persons for life and health insurance specifically. A property, agricultural or sovereign disaster index falls outside that point, so most parametric triggers are not high risk on that ground; it becomes high risk only if the same model prices or assesses life or health cover for a natural person. Separately, a fixed rule with no learned model behind it may not meet the Article 3(1) definition of an AI system at all, a question decided case by case rather than settled by this page.",[132,133,134,135],"eu-ai-act","solvency-ii","gdpr","eu-idd",[137],{"title":138,"issuer":139,"region":140,"url":141,"note":142},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","europe","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Sets out supervisory expectations for governance and risk management of AI systems used across the insurance value chain, including pricing, underwriting, claims management and fraud detection, that are not prohibited or high risk under the EU AI Act. The Opinion does not mention parametric or index based insurance.",[144,145,146,147],"AI or model inventory entry for the trigger model with an accountable actuarial owner","Independent calibration review of the model against historical losses before launch and on a fixed schedule after","Audit trail of every threshold check, whether or not it triggered a payout","A named fallback data source or manual process for feed outages or disputed readings",[],{"howToBuild":150},"On Blits.ai the trigger check runs as an **agentic task**, a scheduled, condition triggered\nworkflow that calls the satellite or weather data provider through a **custom function**,\nwhile a **flow** holds the deterministic comparison against the policy's index, so the\nformula stays version controlled and auditable, separate from anything a model drafts.\n\nBecause a trigger moves money, every one runs through **human in the loop approval** above a\nconfigurable threshold before a custom function calls the insurer's own payment or policy\nadministration system to release the payout, with a full **audit trail** per run. A\n**knowledge base** holds the published trigger and payout terms so a conversational **agent**\ncan explain to a policyholder, on **WhatsApp, SMS or voice**, why a payout did or did not fire.\nBlits.ai's **monitors** and **analytics** track the health and usage of the agent and its\nworkflow runs, not the insurer's own data feed or payment system, which sit outside the\nplatform. The platform is **model agnostic** and can run in **EU or UAE data residency**\nregions.",[152,155,158,161],{"question":153,"answer":154},"How is parametric insurance different from a normal claim?","A normal claim needs a policyholder to report a loss and an adjuster to assess it before payment. A parametric policy instead watches an agreed index and pays once a predefined threshold is crossed. Programs like the World Bank's sovereign catastrophe risk pools check that index with a fixed rule; a smaller group, including Descartes Underwriting, uses AI and machine learning to build the index and to measure the event itself, post event burn analysis for a wildfire's extent, so the payout can be calculated without anyone visiting the site.",{"question":156,"answer":157},"What stops a parametric trigger from paying out wrongly?","The model has to be calibrated against real historical losses before the policy is sold, and the program needs a fallback for when the data feed is disputed or unavailable. Even so, basis risk is real: a policyholder can suffer a loss the index does not capture, or the reverse, so the trigger and its limits should be explained in plain language upfront.",{"question":159,"answer":160},"Has this actually been used, or is it still theoretical?","Descartes Underwriting says it already runs AI, generative models that simulate storms and AI that reconstructs past hail events from ground reports, radar, satellite and forecast data, across more than 35 parametric products for corporate and public sector clients; for wildfires, it says the same AI identifies fire lines and assesses affected zones from post event burn analysis, and fully automated systems then verify the event and trigger the payout. A second named deployment shows the trigger side working outside Descartes too: Generali's parametric flood policy for the Conferenza Episcopale Italiana, built on the Floodbase platform, triggered and paid out during heavy rainfall in Northern Italy in 2025, with Floodbase crediting AI for the flood extent maps checked against the policy's threshold. Individual client outcomes are otherwise not disclosed publicly, and the World Bank's sovereign catastrophe risk pools still calculate their triggers from a fixed rule, not an AI model.",{"question":162,"answer":163},"Is a parametric insurance trigger high risk under the EU AI Act?","It depends on what it prices, and on whether a learned model sits behind the trigger at all. A parametric index for property, agriculture or a government disaster fund is not itself listed in Annex III. It becomes high risk under Annex III point 5(c) only if the same model prices or assesses life or health cover for a natural person. A fixed rule with no learned model behind it may not meet the Article 3(1) definition of an AI system in the first place.",[165,166],"catastrophe-exposure-assessment","insurance-pricing-and-actuarial-copilot","2026-09-30",[169],{"date":167,"note":170},"First published","parametric-claims-triggering",[173,202],{"title":174,"useCases":175,"organization":176,"vendors":180,"summary":183,"stage":184,"year":185,"channels":186,"languages":187,"metrics":189,"outcomeDisclosed":178,"sources":190,"verification":197,"grade":199,"id":200,"organizationSlug":201},"Descartes Underwriting: AI models that build parametric triggers and verify events",[171],{"name":177,"anonymized":178,"country":179,"region":140,"industry":16},"Descartes Underwriting",false,"FR",[181],{"name":177,"role":182},"in-house","Descartes Underwriting is a managing general agent that designs parametric weather and catastrophe products, backed by A+ rated insurers and sold through brokers to corporate and public sector clients. It says it uses machine learning and AI, including generative models that simulate thousands of storm scenarios, to build and calibrate the models that define its triggers; for hail specifically, it says AI links ground reports with radar, satellite and forecast data to reconstruct past events and measure their location, size and severity. For wildfires, it says AI also measures the triggering event itself once it happens, identifying fuel types, detecting fire lines and assessing affected zones from post event burn analysis. Fully automated systems then verify the event and trigger the payout, with no on site loss assessment. The company's site describes this technology and unnamed case studies across more than 35 parametric products; it does not name individual clients or disclose a measured outcome.","production",2026,[25],[188],"en",[],[191,194],{"url":192,"title":193,"publisher":177},"https://www.descartesunderwriting.com/about/technology","Technology",{"url":195,"title":196,"publisher":177},"https://www.descartesunderwriting.com","Descartes Underwriting homepage",{"level":198,"checkedAt":167},"source-verified","B","descartes-underwriting-ai-parametric-trigger-models",null,{"title":203,"useCases":204,"organization":205,"vendors":208,"summary":212,"stage":184,"year":213,"channels":214,"languages":215,"metrics":216,"outcomeDisclosed":178,"sources":217,"verification":228,"grade":229,"id":230,"organizationSlug":201},"Generali and Floodbase: AI flood maps trigger a parametric policy for CEI in Italy",[171],{"name":206,"anonymized":178,"country":207,"region":140,"industry":16},"Generali Italia","IT",[209],{"name":210,"role":211},"Floodbase","platform","Generali structured a nationwide parametric flood policy for the Conferenza Episcopale Italiana (CEI), which manages more than 25,000 church buildings across Italy, with reinsurance capacity from Swiss Re and Munich Re. The policy runs on the Floodbase platform, which Floodbase describes as continuously generating flood extent maps \"derived by combining satellite imagery and verified ground observations with scientifically and industry-leading AI\". During torrential rain in Northern Italy in 2025, CEI officials watched the event cross the policy's predefined trigger thresholds in Floodbase's FloodView application, the policy triggered within weeks of being adopted, and the payout followed with no on site loss assessment. Floodbase's account names the insurer and the client and describes a real triggered payout, but does not disclose a payout amount.",2025,[25],[188],[],[218,221,224],{"url":219,"title":220,"publisher":210},"https://www.floodbase.com/blog-posts/europes-largest-portfolio-parametric-flood-policy-has-already-triggered-in-italy","Europe's Largest Portfolio Parametric Flood Policy Has Already Triggered in Italy",{"url":222,"title":223,"publisher":210},"https://www.floodbase.com/","Floodbase | The AI platform for insuring uncovered flood risk",{"url":225,"title":226,"publisher":227},"https://www.reinsurancene.ws/swiss-re-and-munich-re-back-newly-launched-cei-generali-parametric-solution/","Swiss Re and Munich Re back newly launched CEI & Generali parametric solution","Reinsurance News",{"level":198,"checkedAt":167},"C","generali-floodbase-cei-italy-flood-trigger",0,[],{"low":234,"high":235},30000,1600000,[237,255,290,316],{"slug":165,"title":238,"shortTitle":239,"definition":240,"status":9,"industries":241,"functions":242,"patterns":245,"audience":26,"autonomy":248,"adoptionStage":28,"segment":249,"evidenceCount":250,"publicEvidenceCount":250,"organizations":251,"bestGrade":229,"headline":201,"lastVerified":167,"indexable":254},"AI for catastrophe and exposure assessment in insurance","Catastrophe exposure assessment","AI that turns satellite and geospatial data into a fast, portfolio wide view of which policies and properties are exposed to a catastrophe, before and immediately after the event, so exposure managers and claims teams can quantify the loss, prioritize response and reach affected customers first, without waiting for ground surveys.",[16],[18,243,244],"risk-management","operations",[22,246,21,247],"anomaly-detection","agentic-workflow","assist","catastrophe-and-exposure",2,[252,253],"Sompo Japan Insurance","Suncorp Group",true,{"slug":166,"title":256,"shortTitle":257,"definition":258,"status":9,"industries":259,"functions":260,"patterns":262,"audience":265,"autonomy":266,"adoptionStage":267,"segment":268,"evidenceCount":269,"publicEvidenceCount":269,"organizations":270,"bestGrade":199,"headline":280,"lastVerified":289,"indexable":254},"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],[19,243,261],"analytics-and-reporting",[21,263,247,264],"code-generation","summarization","employee-facing","copilot","early-adopters","pricing",9,[271,272,273,274,275,276,277,278,279],"Accelerant Holdings","AXA Spain","Canal Insurance Company","Canopius Group","Europ Assistance","Generali France","Kinsale Capital Group","MAIF","Wakam",{"kpi":281,"label":282,"unit":283,"n":284,"nUpTo":231,"kind":285,"value":286,"qualifier":287,"claimant":288,"organization":276,"vendorReported":178},"productivity-gain","Productivity gain","multiplier",1,"reported",5,"exact","organization","2026-09-26",{"slug":291,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":296,"patterns":298,"audience":26,"autonomy":248,"adoptionStage":301,"segment":18,"evidenceCount":302,"publicEvidenceCount":302,"organizations":303,"bestGrade":199,"headline":201,"lastVerified":315,"indexable":254},"claims-fraud-detection","AI for insurance claims fraud detection","Claims fraud detection","AI that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data to find suspicious claims, organised rings and inflated losses, and sends each alert with its reasons to a claims handler or special investigations unit for review.",[16],[18,297],"fraud-prevention",[246,21,299,22,300],"document-processing","classification-and-routing","mainstream",11,[304,305,306,307,308,309,310,311,312,313,314],"Assurant","AXA Switzerland","Covéa","General Insurance Association of Singapore","GNP Seguros","Jubilee Holdings","Lemonade","Old Mutual General Insurance","Shelter Insurance","Tawuniya (Company for Cooperative Insurance)","Tokio Marine & Nichido Fire Insurance","2026-09-27",{"slug":317,"title":318,"shortTitle":319,"definition":320,"status":9,"industries":321,"functions":322,"patterns":323,"audience":324,"autonomy":325,"adoptionStage":267,"segment":18,"evidenceCount":269,"publicEvidenceCount":269,"organizations":326,"bestGrade":229,"headline":334,"lastVerified":315,"indexable":254},"photo-based-damage-assessment","AI for photo based damage assessment in insurance claims","Photo damage assessment","Computer vision that assesses damage from photos or video of a vehicle or property taken by the policyholder, a repairer or an adjuster, identifies the damaged parts and the repair or replace decision, produces or checks the repair estimate, and flags total losses and inconsistencies for a person to review.",[16],[18],[22,21,247],"customer-facing","supervised-agent",[327,328,306,329,330,331,332,314,333],"Admiral Seguros","Ageas UK","Foyer","Porto Seguro","PZU","Sompo Japan","Warta",{"kpi":44,"label":335,"unit":283,"n":231,"nUpTo":284,"kind":285,"value":336,"qualifier":337,"claimant":338,"organization":333,"vendorReported":254},"Cycle time reduction",3,"up-to","vendor",{"indexable":254,"reasons":340},[],[342,348,353,361,369,376,382,389,397,404,411,417,423,429,436,442,448,455,460,466,473,479,484,489,493,500,505,510,517,521,528,535,541,547,552,557],{"id":132,"label":343,"issuer":344,"region":140,"url":345,"description":346,"useCases":347,"indexable":254},"EU AI Act","European Union","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.",250,{"id":134,"label":349,"issuer":344,"region":140,"url":350,"description":351,"useCases":352,"indexable":254},"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.",223,{"id":354,"label":355,"issuer":356,"region":357,"url":358,"description":359,"useCases":360,"indexable":254},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":362,"label":363,"issuer":364,"region":365,"url":366,"description":367,"useCases":368,"indexable":254},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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.",95,{"id":370,"label":371,"issuer":372,"region":140,"url":373,"description":374,"useCases":375,"indexable":254},"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.",73,{"id":377,"label":378,"issuer":344,"region":140,"url":379,"description":380,"useCases":381,"indexable":254},"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.",67,{"id":383,"label":384,"issuer":385,"region":140,"url":386,"description":387,"useCases":388,"indexable":254},"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.",50,{"id":390,"label":391,"issuer":392,"region":393,"url":394,"description":395,"useCases":396,"indexable":254},"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.",37,{"id":398,"label":399,"issuer":400,"region":393,"url":401,"description":402,"useCases":403,"indexable":254},"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":405,"label":406,"issuer":407,"region":357,"url":408,"description":409,"useCases":410,"indexable":254},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":412,"label":413,"issuer":414,"region":365,"url":415,"description":416,"useCases":410,"indexable":254},"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":418,"label":419,"issuer":344,"region":140,"url":420,"description":421,"useCases":422,"indexable":254},"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.",17,{"id":424,"label":425,"issuer":426,"region":140,"url":427,"description":428,"useCases":422,"indexable":254},"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.",{"id":430,"label":431,"issuer":432,"region":365,"url":433,"description":434,"useCases":435,"indexable":254},"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.",16,{"id":437,"label":438,"issuer":439,"region":357,"url":440,"description":441,"useCases":60,"indexable":254},"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.",{"id":443,"label":444,"issuer":344,"region":140,"url":445,"description":446,"useCases":447,"indexable":254},"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":449,"label":450,"issuer":451,"region":365,"url":452,"description":453,"useCases":454,"indexable":254},"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":456,"label":457,"issuer":344,"region":140,"url":458,"description":459,"useCases":454,"indexable":254},"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.",{"id":461,"label":462,"issuer":463,"region":365,"url":464,"description":465,"useCases":454,"indexable":254},"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":467,"label":468,"issuer":469,"region":357,"url":470,"description":471,"useCases":472,"indexable":254},"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.",12,{"id":474,"label":475,"issuer":476,"region":365,"url":477,"description":478,"useCases":302,"indexable":254},"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":480,"label":481,"issuer":344,"region":140,"url":482,"description":483,"useCases":302,"indexable":254},"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.",{"id":485,"label":486,"issuer":344,"region":140,"url":487,"description":488,"useCases":302,"indexable":254},"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":133,"label":490,"issuer":344,"region":140,"url":491,"description":492,"useCases":302,"indexable":254},"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":494,"label":495,"issuer":496,"region":140,"url":497,"description":498,"useCases":499,"indexable":254},"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.",10,{"id":501,"label":502,"issuer":392,"region":393,"url":503,"description":504,"useCases":499,"indexable":254},"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.",{"id":506,"label":507,"issuer":344,"region":140,"url":508,"description":509,"useCases":499,"indexable":254},"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":511,"label":512,"issuer":513,"region":365,"url":514,"description":515,"useCases":516,"indexable":254},"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.",7,{"id":135,"label":518,"issuer":344,"region":140,"url":519,"description":520,"useCases":516,"indexable":254},"Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":522,"label":523,"issuer":524,"region":525,"url":526,"description":527,"useCases":286,"indexable":254},"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":529,"label":530,"issuer":531,"region":140,"url":532,"description":533,"useCases":534,"indexable":254},"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":536,"label":537,"issuer":538,"region":140,"url":539,"description":540,"useCases":534,"indexable":254},"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":542,"label":543,"issuer":544,"region":393,"url":545,"description":546,"useCases":336,"indexable":254},"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":548,"label":549,"issuer":344,"region":140,"url":550,"description":551,"useCases":336,"indexable":254},"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":553,"label":554,"issuer":344,"region":140,"url":555,"description":556,"useCases":336,"indexable":254},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":558,"label":559,"issuer":560,"region":365,"url":561,"description":562,"useCases":336,"indexable":254},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790783079679]