[{"data":1,"prerenderedAt":571},["ShallowReactive",2],{"uc-catastrophe-exposure-assessment":3,"uc-regulations":349},{"useCase":4,"evidence":181,"blitsAiDeployments":241,"benchmarks":242,"indicative":243,"related":246,"indexability":347,"includeUnpublished":187},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":21,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":46,"macroEstimates":75,"feasibility":76,"implementation":90,"risk":128,"blitsAi":149,"faq":151,"related":164,"datePublished":169,"dateModified":170,"lastVerified":170,"changelog":171,"slug":180},"AI for catastrophe and exposure assessment in insurance","Catastrophe exposure assessment","AI catastrophe exposure assessment for insurers","Satellite flood data matched to property data shows which customers a flood hit. ICEYE says Suncorp supported customers at least a week faster in 2022.","published","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.",[12,13,14],"catastrophe response analytics","portfolio exposure assessment","satellite claims triage",[16],"insurance",[18,19,20],"claims","risk-management","operations",[22,23,24,25],"computer-vision","anomaly-detection","prediction-and-scoring","agentic-workflow",[27,28],"api","internal-tools","back-office","assist","emerging","catastrophe-and-exposure","After a major flood, hurricane or wildfire, an insurer's first question is simple and hard to\nanswer fast: which of our policyholders were actually in the affected area, and how badly. Ground\nsurveys and adjuster visits take days to weeks, and the customers who need help most, people whose\nhomes are flooded or burned, can be the hardest to reach through a normal contact centre queue.\nTraditional catastrophe modelling looks at the portfolio before an event, for pricing and\nreinsurance; it was not built to say, soon after landfall, which specific addresses are affected\ntoday.\n\nSatellite radar flood data covering an affected region is already sold to insurers. Turning that\ndata into a ranked list matched to a specific policy\nportfolio, so claims and contact centre teams can act on it, is the part this use case addresses.",[],"1. **Watch for a trigger event.** A named storm, a flood warning or a wildfire perimeter update\n   starts the process, either automatically from a data provider's feed or through a manual\n   confirmation.\n2. **Pull the hazard footprint.** The system retrieves the satellite radar or optical data for the\n   affected area, such as a flood extent and depth map or a wildfire perimeter, from a licensed\n   geospatial data provider.\n3. **Match it to the portfolio.** Every policy's geocoded address is checked against the hazard\n   footprint, so the system can say which policies fall inside the affected area and, where the\n   data supports it, how severely.\n4. **Rank and route.** Affected policies are ranked by likely severity and value at risk and pushed\n   into the claims and customer contact systems as a prioritized list, not a separate map nobody\n   opens during a crisis.\n5. **People act on it.** Claims teams prioritize adjuster visits and reserve reviews, and contact\n   centres proactively reach out to the highest priority customers first, instead of waiting for\n   everyone affected to call in.",[37,38,39,40],"speed","customer-experience","cost-to-serve","risk-reduction",[42,43,44,45],"response-time-reduction","processing-time-reduction","customer-satisfaction","cost-reduction",{"referenceOrg":47,"inputs":48,"formula":70,"currency":71,"period":72,"resultLabel":73,"caveat":74},"A property insurer with 300,000 policies in a flood or hurricane exposed region",[49,56,63],{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"policiesInPath","Policies typically in the path of a major regional catastrophe",15000,50000,"policies per major event","Editorial assumption, replace with your own catastrophe exposure data for the region.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"assessmentCostPerPolicy","Cost of a manual desk or field assessment avoided or accelerated per affected policy",50,150,"USD per policy","Editorial assumption for a manual site visit or desk review avoided or sped up. Replace with your own claims handling cost.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"shareAvoidedOrAccelerated","Share of affected policies where satellite data avoids or meaningfully speeds up a manual visit",0.2,0.4,"fraction of affected policies","Editorial assumption, replace with your own. Not every affected policy needs a site visit in the first place, so this is well under all affected policies.","policiesInPath * assessmentCostPerPolicy * shareAvoidedOrAccelerated","USD","per major catastrophe event","Claims assessment cost impact (avoided cost plus the value of faster handling) per major catastrophe event","Blends a real cost avoided (no desk or field assessment needed at all) with the value of an assessment that only happens faster, which is not itself a cash saving. It leaves out the cost of the satellite or geospatial data subscription and integration work, and any effect on loss reserves, reinsurance recoveries or customer retention.",[],{"complexity":77,"complexityNote":78,"dataPrerequisites":79,"integrations":84},"high","The hard part is matching the imagery to a reliably geocoded policy portfolio and getting a ranked, trustworthy list into claims and contact centre systems soon after an event, while ground access is still limited.",[80,81,82,83],"Geocoded policy and property addresses (real coordinates, not just a postcode) across the exposed portfolio","A licensed satellite or geospatial data feed for the peril that matters most (flood extent, wildfire perimeter, wind swath)","A defined trigger, such as a named storm or a flood warning, that starts the assessment process","Historical claims data per hazard zone to check the exposure output against",[85,86,87,88,89],"Policy administration system for the geocoded portfolio","Claims management system to receive the prioritized, affected policy list","A satellite or geospatial data provider API for the relevant peril","Customer contact channels for proactive outreach to affected policyholders","A catastrophe or exposure management platform for portfolio level aggregation",{"steps":91,"guardrails":107,"humanInTheLoop":112,"kpisToInstrument":113,"failureModes":118},[92,95,98,101,104],{"title":93,"detail":94},"Geocode the portfolio before anything else","A satellite hazard map is only as useful as the coordinates it is matched against. Turning postal addresses into reliable geocoordinates for the whole book is usually the longest step, and it pays off before the next event, not just this one.",{"title":96,"detail":97},"Start with one peril and one data feed","Pick the peril that hits the book hardest, flood, wildfire or wind, and license one geospatial data provider for it rather than trying to cover every peril at once.",{"title":99,"detail":100},"Define the trigger and the workflow","Agree what starts the assessment (a named storm, a flood warning, a wildfire perimeter update), what runs automatically, and what a person must confirm before it reaches claims or customers.",{"title":102,"detail":103},"Deliver a ranked list, not a map","Push the prioritized, affected policy list into the systems claims and contact centre teams already use. A standalone GIS viewer gets ignored during a live event.",{"title":105,"detail":106},"Test it on a past event before the next one","Replay the satellite data and the portfolio from a real past catastrophe and compare the system's affected policy list against what the claims data later showed, before relying on it live.",[108,109,110,111],"Every flagged policy carries the data source, its resolution and its timestamp, so a claims handler can judge how much to trust it","A minimum resolution and confidence threshold before a property is marked affected; below it, the policy goes to manual review","A human confirms before any automated customer contact or reserve change based on the exposure data alone","Third party geospatial data providers are checked for licence terms and update frequency before being relied on live","Claims and exposure teams decide what to do with the prioritized list: who gets called first, what reserve to hold, when to send an adjuster. The AI ranks and flags policies; it does not settle a claim, change a reserve or contact a customer on its own.",[114,115,116,117],"Time from the trigger event to a prioritized list of affected policies","Share of affected policies correctly identified against later claims data","Customer contact time for policies in the affected area, before and after","False positive rate, policies flagged as affected that had no resulting claim",[119,122,125],{"title":120,"detail":121},"Resolution too coarse for the portfolio","Satellite flood or wildfire data has a real resolution limit, and a policy at the edge of a flagged area may not actually be affected. Treat the output as a priority list for human follow up, not a verdict on any individual policy.",{"title":123,"detail":124},"Geocoding gaps hide exposure","Policies with only a postcode or a PO box cannot be matched against the hazard footprint and silently fall out of the list. Track and report the share of the portfolio that could not be geocoded.",{"title":126,"detail":127},"Built for one event, unused for the next","A one off integration rushed together for a single storm does not survive to the next event if nobody owns the data feed contract and the workflow. Assign an owner and renew the data licence before the next season it needs to cover.",{"euAiAct":129,"regulations":132,"guidance":136,"controls":143,"incidents":148},{"tier":130,"basis":131},"minimal","Not listed in Annex III: the system aggregates satellite and portfolio data to prioritize claims response and inform exposure management, and does not decide an individual's cover, price or claim outcome. It would need reassessment if the same output were used to automatically decline or reduce a specific claim without human review.",[133,134,135],"eu-ai-act","solvency-ii","gdpr",[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 how existing insurance sector legislation on governance, risk management and data quality applies to AI systems across the insurance value chain that are not high risk under the AI Act. It does not name catastrophe or exposure analytics specifically.",[144,145,146,147],"AI inventory entry for the exposure model with an accountable owner","Documented resolution and confidence thresholds per peril, reviewed after each major event","Audit trail linking every flagged or prioritized policy to the data source and timestamp used","Post event validation comparing flagged policies against the claims that were actually reported",[],{"howToBuild":150},"On Blits.ai this runs as an **agentic workflow**, triggered on a schedule or via an **API token**,\nfor example from the insurer's own catastrophe alerting system, when a new hazard footprint needs\nassessing. A **custom function** pulls that footprint for the triggered event over REST, and a **SQL\nknowledge base** gives the agent read access to the geocoded policy portfolio, so it can match\naddresses inside the affected area and rank them by likely severity and value at risk.\n\nBecause this touches real customers at a vulnerable moment, any proactive outreach runs behind\n**human in the loop confirmation** before it goes out over the **outbound email channel**. The\nranked, affected policy list is written back into the insurer's claims or contact system through\nanother **custom function** call over REST, so claims and contact centre teams work from their\nown systems rather than a separate map. Every run is recorded in **run history** with a full\n**audit trail**, and a **monitor** keeps a scheduled health check on the agent that runs this\nworkflow. The platform is **model agnostic** and can run in **EU or UAE data residency** regions.",[152,155,158,161],{"question":153,"answer":154},"Can AI tell an insurer which policies a flood or hurricane affected before ground surveys are possible?","Yes, within real limits on resolution and confidence. ICEYE says its Flood Insights product combines satellite radar imagery with algorithms and machine learning to quantify flood extent, and Sompo Japan Insurance signed a contract in September 2026 to use it for its own claims response. Suncorp's earlier case study with Arturo and ICEYE reports that combining satellite flood data with property data supported customers at least a week faster during Australia's 2022 floods. Treat the output as a priority list for human follow up, not a verdict on any individual claim.",{"question":156,"answer":157},"Is this the same as catastrophe modelling for pricing and capital?","No. Traditional catastrophe models estimate probable losses across a portfolio before an event, for pricing and reinsurance. This use case is about knowing, soon after a real event, which specific policies are actually in the affected area, so claims and contact centre teams can act on the highest priority cases first.",{"question":159,"answer":160},"What data does an insurer need before starting?","A portfolio geocoded to real coordinates, not just a postcode, and a licensed satellite or geospatial data feed for the peril that matters most to the book. Without accurate geocoding, the satellite data has nothing reliable to match against.",{"question":162,"answer":163},"Is this high risk under the EU AI Act?","Usually not. It is not listed in Annex III because it prioritizes response and informs exposure management rather than deciding an individual's cover, price or claim outcome. It would need a fresh risk assessment if the same output were used to automatically decline or reduce a specific claim without a person reviewing it.",[165,166,167,168],"claims-triage-and-straight-through-processing","photo-based-damage-assessment","insurance-pricing-and-actuarial-copilot","parametric-claims-triggering","2026-09-29","2026-09-30",[172,174,176,178],{"date":170,"note":173},"Published after review by an automated review workflow (independent skeptic review).",{"date":170,"note":175},"Review fix pass: added ICEYE's Flood Insights product page (states \"algorithms, and machine learning\") as evidence that ICEYE's satellite flood product line uses AI, since neither the Suncorp nor the Sompo source page used the words AI, machine learning or algorithm on their own. Reworded FAQ 1 to separate the AI method claim (sourced to ICEYE's product page and the Sompo contract) from Suncorp's outcome claim, which is not itself described as AI by its source. Dropped \"AI\" from the Suncorp evidence title. Replaced the Suncorp evidence's response-time-reduction metric (wrong unit, sourced to a gated teaser question) with the declarative outcome in the summary and no formal KPI, since no taxonomy KPI fits an unquantified claim of days saved. Rewrote metaDescription to attribute the outcome to ICEYE and stay within what the source says. Rewrote blitsAi.howToBuild to drop the human handover queue and policy count analytics claims, neither of which the feature inventory supports for a background workflow with no live conversation, and replaced them with the custom function write back, run history and audit trail the inventory does support. Removed three unsourced factual generalizations from problem and feasibility.complexityNote (call volumes spiking, imagery being easy to buy, imagery usually arriving as an unmatched map).",{"date":170,"note":177},"Unpublished by an automated review workflow (independent skeptic review).",{"date":169,"note":179},"First published","catastrophe-exposure-assessment",[182,214],{"title":183,"useCases":184,"organization":185,"vendors":190,"summary":194,"stage":195,"year":196,"channels":197,"languages":198,"metrics":200,"outcomeDisclosed":187,"sources":201,"verification":209,"grade":211,"id":212,"organizationSlug":213},"Sompo Japan: satellite flood intelligence for catastrophe claims response",[180],{"name":186,"anonymized":187,"country":188,"region":189,"industry":16},"Sompo Japan Insurance",false,"JP","asia-pacific",[191],{"name":192,"role":193},"ICEYE","platform","Sompo Japan Insurance signed a contract with ICEYE to use its Flood Insights satellite radar product as part of the insurer's Hikeshi DNA 2030 Project, which aims to enhance regional resilience; strengthening its response before, during and after natural disasters is one of the project's pillars. Yasuhiro Tsukahara, CEO of ICEYE Japan, said that rapid, accurate intelligence on the extent and severity of flooding can help enable a faster, more efficient claims response after a major flood event. ICEYE's own Flood Insights product page states that the product \"combines ICEYE's radar satellite imagery with an abundance of third-party data, algorithms, and machine learning\" to quantify flood extent, which is the AI component of this deployment. The announcement discloses no volume, cost or speed figures for the deployment itself.","announced",2026,[27],[199],"en",[],[202,206],{"url":203,"title":204,"publisher":192,"date":205},"https://www.iceye.com/newsroom/press-releases/sompo-japan-and-iceye-partner-to-accelerate-flood-claims-response-using-satellite-intelligence?hsLang=en","Sompo Japan and ICEYE partner to accelerate flood claims response using satellite intelligence","2026-09-07",{"url":207,"title":208,"publisher":192},"https://www.iceye.com/solutions/insurance/flood-insights","Flood Insights | Insurance solutions | ICEYE",{"level":210,"checkedAt":170},"source-verified","C","sompo-japan-iceye-flood-insights",null,{"title":215,"useCases":216,"organization":217,"vendors":220,"summary":224,"stage":225,"year":226,"channels":227,"languages":228,"metrics":229,"outcomeDisclosed":230,"sources":231,"verification":239,"grade":211,"id":240,"organizationSlug":213},"Suncorp: satellite flood data and property matching for rapid catastrophe response",[180],{"name":218,"anonymized":187,"country":219,"region":189,"industry":16},"Suncorp Group","AU",[221,222],{"name":192,"role":193},{"name":223,"role":193},"Arturo","During the 2022 Australian floods, Suncorp combined ICEYE's satellite radar flood data with Arturo's property data. ICEYE's case study describes this as the technology that enabled Suncorp to assess flood impacts accurately, prioritize resources, and support customers faster by at least a week. The case study landing page itself does not use the words AI, machine learning or algorithm; ICEYE's separate Flood Insights product page states that its satellite flood products combine third party data, algorithms and machine learning to quantify flood extent, but does not name this Suncorp deployment specifically. No numeric breakdown of the underlying calculation is given beyond the \"at least a week\" figure.","production",2022,[27,28],[199],[],true,[232,235,238],{"url":233,"title":234,"publisher":192},"https://www.iceye.com/lp/suncorp-flood-insights-case-study?hsLang=en","Rapid catastrophe response: Suncorp case study",{"url":236,"title":237,"publisher":192},"https://www.iceye.com/resources/case-studies","ICEYE case studies",{"url":207,"title":208,"publisher":192},{"level":210,"checkedAt":170},"suncorp-satellite-catastrophe-response",0,[],{"low":244,"high":245},150000,3000000,[247,282,305,333],{"slug":165,"title":248,"shortTitle":249,"definition":250,"status":9,"industries":251,"functions":252,"patterns":253,"audience":29,"autonomy":257,"adoptionStage":258,"segment":18,"evidenceCount":259,"publicEvidenceCount":260,"organizations":261,"bestGrade":271,"headline":272,"lastVerified":281,"indexable":230},"AI for claims triage and straight through processing","Claims triage and STP","AI that reads each new insurance claim and its documents, scores its complexity, cover questions, fraud and recovery signals, sends it to the right handling path and handler, and settles simple, low risk claims end to end within set limits without a person touching them.",[16],[18,20],[254,24,255,25,256],"classification-and-routing","document-processing","summarization","supervised-agent","early-adopters",11,9,[262,263,264,265,266,267,268,269,270],"Admiral Seguros","Allianz Partners","Hiscox","Lemonade","MAG Seguros","QBE Insurance Group","Sedgwick","Tokio Marine & Nichido Fire Insurance","Travelers","B",{"kpi":273,"label":274,"unit":275,"n":276,"nUpTo":276,"kind":277,"value":278,"qualifier":279,"claimant":280,"organization":265,"vendorReported":187},"automation-rate","Automation rate","percent",1,"reported",55,"approximately","organization","2026-09-26",{"slug":166,"title":283,"shortTitle":284,"definition":285,"status":9,"industries":286,"functions":287,"patterns":288,"audience":289,"autonomy":257,"adoptionStage":258,"segment":18,"evidenceCount":260,"publicEvidenceCount":260,"organizations":290,"bestGrade":211,"headline":298,"lastVerified":304,"indexable":230},"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],[22,24,25],"customer-facing",[262,291,292,293,294,295,296,269,297],"Ageas UK","Covéa","Foyer","Porto Seguro","PZU","Sompo Japan","Warta",{"kpi":43,"label":299,"unit":300,"n":241,"nUpTo":276,"kind":277,"value":301,"qualifier":302,"claimant":303,"organization":297,"vendorReported":230},"Cycle time reduction","multiplier",3,"up-to","vendor","2026-09-27",{"slug":167,"title":306,"shortTitle":307,"definition":308,"status":9,"industries":309,"functions":310,"patterns":313,"audience":315,"autonomy":316,"adoptionStage":258,"segment":317,"evidenceCount":260,"publicEvidenceCount":260,"organizations":318,"bestGrade":271,"headline":328,"lastVerified":281,"indexable":230},"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],[311,19,312],"product-and-pricing","analytics-and-reporting",[24,314,25,256],"code-generation","employee-facing","copilot","pricing",[319,320,321,322,323,324,325,326,327],"Accelerant Holdings","AXA Spain","Canal Insurance Company","Canopius Group","Europ Assistance","Generali France","Kinsale Capital Group","MAIF","Wakam",{"kpi":329,"label":330,"unit":300,"n":276,"nUpTo":241,"kind":277,"value":331,"qualifier":332,"claimant":280,"organization":324,"vendorReported":187},"productivity-gain","Productivity gain",5,"exact",{"slug":168,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":338,"patterns":339,"audience":29,"autonomy":341,"adoptionStage":31,"segment":342,"evidenceCount":343,"publicEvidenceCount":343,"organizations":344,"bestGrade":271,"headline":213,"lastVerified":170,"indexable":230},"AI for parametric insurance claims triggering","Parametric claims triggering","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.",[16],[18,311],[24,22,340],"synthetic-data-generation","autonomous","parametric",2,[345,346],"Descartes Underwriting","Generali Italia",{"indexable":230,"reasons":348},[],[350,356,361,369,377,384,390,396,403,410,417,423,429,435,442,449,455,462,467,473,480,486,491,496,500,507,512,517,524,529,536,543,549,555,560,565],{"id":133,"label":351,"issuer":352,"region":140,"url":353,"description":354,"useCases":355,"indexable":230},"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":135,"label":357,"issuer":352,"region":140,"url":358,"description":359,"useCases":360,"indexable":230},"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":362,"label":363,"issuer":364,"region":365,"url":366,"description":367,"useCases":368,"indexable":230},"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":370,"label":371,"issuer":372,"region":373,"url":374,"description":375,"useCases":376,"indexable":230},"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":378,"label":379,"issuer":380,"region":140,"url":381,"description":382,"useCases":383,"indexable":230},"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":385,"label":386,"issuer":352,"region":140,"url":387,"description":388,"useCases":389,"indexable":230},"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":391,"label":392,"issuer":393,"region":140,"url":394,"description":395,"useCases":59,"indexable":230},"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.",{"id":397,"label":398,"issuer":399,"region":189,"url":400,"description":401,"useCases":402,"indexable":230},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":404,"label":405,"issuer":406,"region":189,"url":407,"description":408,"useCases":409,"indexable":230},"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":411,"label":412,"issuer":413,"region":365,"url":414,"description":415,"useCases":416,"indexable":230},"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":418,"label":419,"issuer":420,"region":373,"url":421,"description":422,"useCases":416,"indexable":230},"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":424,"label":425,"issuer":352,"region":140,"url":426,"description":427,"useCases":428,"indexable":230},"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":430,"label":431,"issuer":432,"region":140,"url":433,"description":434,"useCases":428,"indexable":230},"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":436,"label":437,"issuer":438,"region":373,"url":439,"description":440,"useCases":441,"indexable":230},"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":443,"label":444,"issuer":445,"region":365,"url":446,"description":447,"useCases":448,"indexable":230},"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":450,"label":451,"issuer":352,"region":140,"url":452,"description":453,"useCases":454,"indexable":230},"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":456,"label":457,"issuer":458,"region":373,"url":459,"description":460,"useCases":461,"indexable":230},"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":463,"label":464,"issuer":352,"region":140,"url":465,"description":466,"useCases":461,"indexable":230},"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":468,"label":469,"issuer":470,"region":373,"url":471,"description":472,"useCases":461,"indexable":230},"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":474,"label":475,"issuer":476,"region":365,"url":477,"description":478,"useCases":479,"indexable":230},"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":481,"label":482,"issuer":483,"region":373,"url":484,"description":485,"useCases":259,"indexable":230},"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":487,"label":488,"issuer":352,"region":140,"url":489,"description":490,"useCases":259,"indexable":230},"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":492,"label":493,"issuer":352,"region":140,"url":494,"description":495,"useCases":259,"indexable":230},"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":134,"label":497,"issuer":352,"region":140,"url":498,"description":499,"useCases":259,"indexable":230},"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":501,"label":502,"issuer":503,"region":140,"url":504,"description":505,"useCases":506,"indexable":230},"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":508,"label":509,"issuer":399,"region":189,"url":510,"description":511,"useCases":506,"indexable":230},"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":513,"label":514,"issuer":352,"region":140,"url":515,"description":516,"useCases":506,"indexable":230},"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":518,"label":519,"issuer":520,"region":373,"url":521,"description":522,"useCases":523,"indexable":230},"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":525,"label":526,"issuer":352,"region":140,"url":527,"description":528,"useCases":523,"indexable":230},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":530,"label":531,"issuer":532,"region":533,"url":534,"description":535,"useCases":331,"indexable":230},"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":537,"label":538,"issuer":539,"region":140,"url":540,"description":541,"useCases":542,"indexable":230},"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":544,"label":545,"issuer":546,"region":140,"url":547,"description":548,"useCases":542,"indexable":230},"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":550,"label":551,"issuer":552,"region":189,"url":553,"description":554,"useCases":301,"indexable":230},"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":556,"label":557,"issuer":352,"region":140,"url":558,"description":559,"useCases":301,"indexable":230},"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":561,"label":562,"issuer":352,"region":140,"url":563,"description":564,"useCases":301,"indexable":230},"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":566,"label":567,"issuer":568,"region":373,"url":569,"description":570,"useCases":301,"indexable":230},"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.",1790783077694]