[{"data":1,"prerenderedAt":550},["ShallowReactive",2],{"uc-storm-outage-prediction-and-restoration":3,"uc-regulations":322},{"useCase":4,"evidence":149,"blitsAiDeployments":239,"benchmarks":240,"indicative":247,"related":250,"indexability":320,"includeUnpublished":155},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":23,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"indicativeValue":41,"macroEstimates":70,"feasibility":71,"implementation":84,"risk":117,"blitsAi":130,"faq":132,"related":142,"datePublished":144,"dateModified":144,"lastVerified":144,"changelog":145,"slug":148},"AI storm outage prediction and restoration staging for power grids","Storm outage prediction and restoration","AI storm outage prediction for utilities","AI predicts storm damage days ahead so crews stage before outages start. CenterPoint Energy and Hydro One have used weather models to plan faster power restoration.","published","AI that combines weather forecasts, historical outage records and network data to predict how many outages a coming storm will cause, where, and how severe restoration will be, so a utility can stage crews and mutual aid before the storm arrives instead of assessing damage after it hits.",[12,13,14,15],"AI outage prediction model","storm restoration forecasting","proactive crew staging for storms","weather driven outage forecasting",[17],"energy-and-utilities",[19,20],"network-operations","field-service",[22],"prediction-and-scoring",[24,25],"internal-tools","api","employee-facing","copilot","early-adopters","grid","Storms are a leading cause of prolonged power outages, and IBM Consulting describes utility storm\nresponse as mostly manual, which can be expensive and cause delays in resolution. In a joint\nannouncement with its vendor Technosylva about CenterPoint Energy's integrated weather platform,\nCenterPoint Energy's chief executive said that preparing for extreme weather today requires\nearlier insight and better coordination than ever before. A utility that waits for outage reports\nto arrive before deciding where to send crews is, by construction, always a step behind the storm.",[],"1. **Combine outage history with weather and network data.** A model learns from past storms which\n   combinations of wind, precipitation, temperature and network characteristics, such as overhead\n   line exposure and vegetation density, produced which kind of outage.\n2. **Forecast the coming storm's impact.** As a named storm or weather system approaches, the model\n   runs against its forecast track and intensity to predict outage counts and likely locations,\n   commonly days ahead of impact.\n3. **Turn the forecast into a staging plan.** Storm response managers use the forecast to decide how\n   many crews to hold, where to pre position them, and whether to request mutual aid from\n   neighbouring utilities, before the first outage is reported.\n4. **Predict restoration time once outages start.** A companion model estimates a customer specific\n   estimated time of restoration from crew assignments, damage type and observed progress, so\n   communication teams can tell customers when to expect power back. This is a common design\n   addition rather than something the deployments on this page are documented as doing.\n5. **Compare the plan to what happened.** After each storm, predicted outage counts, locations and\n   restoration times are compared against the actual result and fed back into the model.",[34,35,36],"risk-reduction","cost-to-serve","customer-experience",[38,39,40],"mttr-reduction","cost-reduction","accuracy",{"referenceOrg":42,"inputs":43,"formula":65,"currency":66,"period":67,"resultLabel":68,"caveat":69},"An electric utility with 2 million customers in a storm prone region",[44,51,58],{"key":45,"label":46,"low":47,"high":48,"unit":49,"note":50},"majorStorms","Major storm events per year",3,8,"storms per year","Editorial assumption for a mid sized storm prone service territory; replace with your own storm history.",{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"costPerStorm","Restoration cost per major storm, including crews, mutual aid and overtime",5000000,20000000,"USD per storm","Editorial assumption; replace with your own historical storm cost data.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"costShareAvoided","Share of restoration cost avoided by staging crews and mutual aid ahead of the storm instead of after",0.03,0.1,"fraction of restoration cost","Editorial assumption. None of the deployments on this page reports a single company wide cost avoided figure; this range is deliberately conservative rather than derived from a source.","majorStorms * costPerStorm * costShareAvoided","USD","per year","Storm restoration cost avoided by proactive crew and mutual aid staging","Restoration cost only. It leaves out the value of faster restoration to customers, avoided regulatory penalties for slow response, and the cost of the forecasting platform itself; none of the evidence on this page reports a single measured cost avoided figure for this exact calculation.",[],{"complexity":72,"complexityNote":73,"dataPrerequisites":74,"integrations":79},"high","The forecasting model itself needs several years of outage history matched to weather and network data by circuit; the harder and more valuable part is changing the storm response process so the forecast actually changes when and where crews and mutual aid are ordered.",[75,76,77,78],"Historical outage records with cause, location and duration","Weather forecast and storm track data covering the service territory","Network topology and asset data by circuit, including overhead exposure and vegetation risk","Crew roster, contractor and mutual aid agreement data",[80,81,82,83],"Outage management system","Weather and storm forecast data provider","Crew dispatch and work management system","Geographic information system of the network",{"steps":85,"guardrails":101,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":110},[86,89,92,95,98],{"title":87,"detail":88},"Prove the forecast against real storm history first","Back test the outage and restoration time model against several past storms before it changes a single staging decision, comparing predicted counts, locations and restoration times against what actually happened.",{"title":90,"detail":91},"Cover the full sequence, not only prediction","Predicting how many outages are coming and where is only half the job; a utility also needs a customer specific estimated time of restoration once outages start, so field crews, contact centres and customers are all working from the same expectation. None of the deployments on this page is documented as having built this restoration time model; treat it as a design recommendation, not a proven pattern from the evidence here.",{"title":93,"detail":94},"Bring in every weather hazard your territory faces","CenterPoint Energy's platform brings outage forecasting, high wind and winter storm modelling, flood risk and wildfire intelligence into one system wide view rather than one model per hazard, because a real storm season rarely produces only one kind of risk.",{"title":96,"detail":97},"Put the forecast in front of the people who order crews and mutual aid","The value only appears once storm response managers actually use the forecast to place crews and request mutual aid days ahead of impact, not once the model is merely accurate in a test.",{"title":99,"detail":100},"Score every storm afterwards","Compare predicted outage counts, locations and restoration times against the actual result after every storm, and retrain on the gap.",[102,103,104],"Emergency operating procedures and worker safety rules always override the model's staging recommendation","A storm response manager approves the crew and mutual aid plan before it is executed","Restoration time estimates shown to customers are checked against field progress before they are communicated","Storm response managers and dispatchers review the predicted outage and staging plan and approve mutual aid requests; customer communication teams check restoration time estimates before they reach customers, especially early in a storm when field information is still limited.",[107,108,109],"Outage forecast accuracy, predicted versus actual count and location, per storm","Time to restoration compared with the plan and with past storms of similar severity","Crew and mutual aid cost per storm against the same measures",[111,114],{"title":112,"detail":113},"A forecast that arrives too late to act on","Staging crews and requesting mutual aid takes days; a model that only becomes confident hours before impact cannot change the plan, however accurate its final prediction turns out to be.",{"title":115,"detail":116},"Treating an accurate outage count as the finished plan","Knowing how many outages are coming is not the same as knowing the right crew and mutual aid plan; keep experienced storm response managers in charge of that decision.",{"euAiAct":118,"regulations":121,"guidance":125,"controls":126,"incidents":129},{"tier":119,"basis":120},"context-dependent","Annex III point 2 makes AI systems high risk when they are intended as safety components in the management and operation of the supply of electricity. A system that forecasts outages and restoration times to inform a staging plan that a storm response manager reviews and approves informs the response rather than directly protecting the network, and is then usually minimal or limited risk. The assessment changes if the same kind of prediction were wired to trigger protective actions automatically, without a person deciding, which would need assessment as a possible safety component.",[122,123,124],"eu-ai-act","nis2","nist-ai-rmf",[],[127,128],"Documented separation between advisory forecasting and any system that can dispatch crews or trigger grid switching automatically","Annual comparison of predicted versus actual storm outcomes, reviewed by engineering and safety teams, not only by the model's developers",[],{"howToBuild":131},"The outage and restoration time forecasting models are specialist utility analytics products;\nBlits.ai is not where you build that forecasting engine. What Blits.ai adds is the layer storm\nresponse teams and customers use around it: an **AI agent** with a **SQL knowledge base** over\nthe outage forecast, the staging plan and live restoration progress lets a storm response manager\nask, in plain language, which circuits are forecast to be hit hardest or how a specific area's\nestimated restoration time has changed.\n\nAn **agentic task** can watch for a condition, such as a forecast crossing a threshold for a\nservice area, and draft a mutual aid request or an internal briefing for a manager to approve\nthrough **human in the loop** confirmation. For customers, a **voice, SMS and WhatsApp** agent\ncan answer restoration time questions from the same underlying data once it is approved for\nrelease, with **human handover** for anyone who needs a person, and the platform's **EU and UAE\ndata residency** and **audit logging** fit a utility's regulatory requirements.",[133,136,139],{"question":134,"answer":135},"Does the AI decide where to send crews?","No deployment on this page describes the AI choosing where to send crews on its own. CenterPoint Energy's teams use multi day outage forecasts to set response levels and pre position crews. Hydro One used the forecast to position crews in the expected path of a storm, and NB Power, which was testing its tool, expected to use it to stage powerline and tree trimming crews ahead of severe weather. As good practice, a storm response manager should still review the forecast and staging plan before crews are committed, though the published sources here do not describe that review step for these specific deployments.",{"question":137,"answer":138},"How far ahead can these models forecast a storm's impact?","CenterPoint Energy's platform lets teams monitor evolving conditions days in advance of impact; none of the deployments on this page publish a single, precise lead time that applies to every storm type, since it depends on how far ahead the underlying weather forecast itself is reliable.",{"question":140,"answer":141},"Is this the same as vegetation management?","No, they are complementary. Vegetation management, covered on a separate page, plans risk based tree trimming year round, including ahead of storm season, to reduce vegetation caused outages generally; storm outage prediction plans the emergency response to a specific, named severe weather event that is already approaching.",[143],"power-line-vegetation-management","2026-09-29",[146],{"date":144,"note":147},"First published","storm-outage-prediction-and-restoration",[150,182,216],{"title":151,"useCases":152,"organization":153,"vendors":158,"summary":162,"stage":163,"year":164,"channels":165,"languages":166,"metrics":168,"outcomeDisclosed":155,"sources":169,"verification":177,"grade":179,"id":180,"organizationSlug":181},"NB Power: IBM outage prediction and resource optimization for ice storms",[148],{"name":154,"anonymized":155,"country":156,"region":157,"industry":17},"New Brunswick Power (NB Power)",false,"CA","north-america",[159],{"name":160,"role":161},"IBM","platform","NB Power, the electric utility for the Canadian province of New Brunswick, serves a cold, storm prone region where winters and ice storms regularly bring temperatures as low as minus 30 degrees Celsius, including a January 2017 ice storm that badly damaged the province's grid. IBM describes a partnership with NB Power that developed an AI powered Outage Prediction and Resource Optimization tool (OPRO), giving the utility better foresight to plan its response and proactively stage powerline and tree trimming crews in the key areas before severe weather arrives. In its own December 2019 report on extreme weather, NB Power says it had partnered with IBM on OPRO and was testing the system, which will allow it to better plan its response and proactively stage powerline and tree trimming crews in key regions ahead of impending weather.","pilot",2019,[24,25],[167],"en",[],[170,173],{"url":171,"title":172,"publisher":160},"https://www.ibm.com/new/product-blog/how-ai-emergency-preparedness-helps-the-energy-industry","How AI emergency preparedness helps the energy industry",{"url":174,"title":175,"publisher":176},"https://www.nbpower.com/media/1489807/191220-extreme-weather-report_final-en.pdf","Extreme Weather, Climate Change and Your Power","New Brunswick Power Corporation",{"level":178,"checkedAt":144},"source-verified","B","nb-power-outage-prediction-resource-optimization",null,{"title":183,"useCases":184,"organization":185,"vendors":187,"summary":189,"stage":190,"year":191,"channels":192,"languages":193,"metrics":194,"outcomeDisclosed":203,"sources":204,"verification":214,"grade":179,"id":215,"organizationSlug":181},"Hydro One: IBM Weather Company outage prediction for response staging",[148],{"name":186,"anonymized":155,"country":156,"region":157,"industry":17},"Hydro One",[188],{"name":160,"role":161},"Hydro One Inc., a subsidiary of Hydro One Limited, which describes itself as Ontario's largest electricity transmission and distribution provider, used the IBM Weather Company's Outage Prediction Tool, which analyses Hydro One's own customer data against weather patterns that have caused past power interruptions. IBM describes the tool as letting Hydro One map a weather forecast against response staging, activate emergency procedures and initiate an incident command centre for repairing lines and restoring power after an outage.","production",2018,[24,25],[167],[195],{"kpi":38,"value":196,"unit":197,"qualifier":198,"period":199,"claimant":200,"quote":201,"sourceUrl":202},33,"percent","approximately","restoration during an April 2018 ice storm","vendor","~33% faster power restoration during an ice storm","https://www.ibm.com/case-studies/hydro-one-networks-watson-media-weather",true,[205,206,211],{"url":171,"title":172,"publisher":160},{"url":207,"title":208,"publisher":209,"date":210},"https://www.prnewswire.com/news-releases/hydro-one-receives-two-emergency-response-awards-300660567.html","Hydro One receives two Emergency Response Awards","Hydro One Inc. (PR Newswire)","2018-06-06",{"url":202,"title":212,"publisher":160,"archivedUrl":213},"Hydro One Networks Inc.: Restoring customers' power 33 percent faster with AI-driven weather insights","http://web.archive.org/web/20210513135910/https://www.ibm.com/case-studies/hydro-one-networks-watson-media-weather",{"level":178,"checkedAt":144},"hydro-one-weather-outage-prediction",{"title":217,"useCases":218,"organization":219,"vendors":222,"summary":225,"stage":190,"year":226,"channels":227,"languages":228,"metrics":229,"outcomeDisclosed":155,"sources":230,"verification":236,"grade":237,"id":238,"organizationSlug":181},"CenterPoint Energy: integrated AI planning platform for extreme weather and outage forecasting",[148],{"name":220,"anonymized":155,"country":221,"region":157,"industry":17},"CenterPoint Energy","US",[223],{"name":224,"role":161},"Technosylva","CenterPoint Energy, which serves about 7 million metered electric and gas customers across Texas, Indiana, Ohio and Minnesota, deployed an integrated planning and operations platform built with Technosylva that brings outage forecasting, high wind and winter storm modelling, flood risk and wildfire intelligence into one system wide view. During recent weather events the company used the platform's multi day outage forecasts and storm impact modelling to set emergency response levels and pre position crews ahead of impact.",2026,[24,25],[167],[],[231],{"url":232,"title":233,"publisher":234,"date":235},"https://www.prnewswire.com/news-releases/centerpoint-energy-advances-extreme-weather-preparedness-and-response-efforts-with-integrated-aidriven-planning-platform-from-technosylva-302786803.html","CenterPoint Energy Advances Extreme Weather Preparedness and Response Efforts with Integrated, AI-Driven Planning Platform from Technosylva","PR Newswire (issued by Technosylva)","2026-06-01",{"level":178,"checkedAt":144},"C","centerpoint-energy-extreme-weather-ai-platform",0,[241],{"kpi":38,"label":242,"unit":197,"aggregate":203,"higherIsBetter":203,"n":243,"nUpTo":239,"median":196,"min":196,"max":196,"byClaimant":244,"vendorOnly":203,"points":245},"Time to repair reduction",1,{"organization":239,"vendor":243,"regulator":239,"independent":239},[246],{"evidenceId":215,"organization":186,"value":196,"qualifier":198,"claimant":200,"grade":179,"pooled":203},{"low":248,"high":249},450000,16000000,[251,264,287,305],{"slug":143,"title":252,"shortTitle":253,"definition":254,"status":9,"industries":255,"functions":256,"patterns":257,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"evidenceCount":259,"publicEvidenceCount":259,"organizations":260,"bestGrade":237,"headline":181,"lastVerified":263,"indexable":203},"AI vegetation management for power lines","Power line vegetation management","AI that analyses satellite, aerial or lidar imagery of the land along power lines to estimate where and how fast vegetation will grow into the lines or fall onto them, and turns that into a risk based trimming and hazard tree removal plan, replacing fixed trimming cycles and manual patrols.",[17],[19,20],[258,22],"computer-vision",2,[261,262],"Entergy","National Grid","2026-09-27",{"slug":265,"title":266,"shortTitle":267,"definition":268,"status":9,"industries":269,"functions":271,"patterns":273,"audience":276,"autonomy":277,"adoptionStage":28,"segment":278,"evidenceCount":279,"publicEvidenceCount":279,"organizations":280,"bestGrade":179,"headline":181,"lastVerified":263,"indexable":203},"predictive-network-maintenance","AI for predictive network maintenance in telecom","Predictive network maintenance","Machine learning that spots the early signs of network failure, such as degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention before customers lose service.",[270],"telecommunications",[19,20,272],"operations",[274,22,275],"anomaly-detection","agentic-workflow","back-office","supervised-agent","network",6,[281,282,283,284,285,286],"KDDI","Orange","Telefónica España","Telstra","Verizon","Vodafone",{"slug":288,"title":289,"shortTitle":290,"definition":291,"status":9,"industries":292,"functions":293,"patterns":295,"audience":300,"autonomy":277,"adoptionStage":301,"segment":302,"evidenceCount":259,"publicEvidenceCount":243,"organizations":303,"bestGrade":179,"headline":181,"lastVerified":263,"indexable":203},"network-outage-communication-agent","AI agent for network outage detection and customer communication","Outage communication","An AI agent that turns network alarms into a clear picture of which customers are affected by an outage and why, tells them proactively by message, app or phone with a cause and an estimated fix time, answers their questions during the incident, and updates them until service is restored.",[270],[294,19,20],"customer-service",[274,296,297,298,299],"classification-and-routing","content-generation","conversational-agent","voice-agent","customer-facing","emerging","front-office",[304],"Comcast",{"slug":306,"title":307,"shortTitle":308,"definition":309,"status":9,"industries":310,"functions":312,"patterns":313,"audience":26,"autonomy":314,"adoptionStage":28,"segment":315,"evidenceCount":47,"publicEvidenceCount":47,"organizations":316,"bestGrade":179,"headline":181,"lastVerified":263,"indexable":203},"industrial-asset-predictive-maintenance","AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","Machine learning that learns the normal behaviour of industrial and energy equipment from sensor and process data, flags early signs of degradation weeks or months before a failure, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[17,311],"manufacturing",[272,20],[274,22],"assist","asset-management",[317,318,319],"Duke Energy","Georgia-Pacific","Shell",{"indexable":203,"reasons":321},[],[323,330,336,344,350,357,363,370,378,385,392,399,404,410,417,424,430,437,443,449,455,462,467,474,479,484,489,495,502,507,515,522,528,534,539,544],{"id":122,"label":324,"issuer":325,"region":326,"url":327,"description":328,"useCases":329,"indexable":203},"EU AI Act","European Union","europe","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.",230,{"id":331,"label":332,"issuer":325,"region":326,"url":333,"description":334,"useCases":335,"indexable":203},"gdpr","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.",207,{"id":337,"label":338,"issuer":339,"region":340,"url":341,"description":342,"useCases":343,"indexable":203},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":124,"label":345,"issuer":346,"region":157,"url":347,"description":348,"useCases":349,"indexable":203},"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.",92,{"id":351,"label":352,"issuer":353,"region":326,"url":354,"description":355,"useCases":356,"indexable":203},"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.",71,{"id":358,"label":359,"issuer":325,"region":326,"url":360,"description":361,"useCases":362,"indexable":203},"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":364,"label":365,"issuer":366,"region":326,"url":367,"description":368,"useCases":369,"indexable":203},"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":371,"label":372,"issuer":373,"region":374,"url":375,"description":376,"useCases":377,"indexable":203},"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":379,"label":380,"issuer":381,"region":374,"url":382,"description":383,"useCases":384,"indexable":203},"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":386,"label":387,"issuer":388,"region":157,"url":389,"description":390,"useCases":391,"indexable":203},"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.",22,{"id":393,"label":394,"issuer":395,"region":340,"url":396,"description":397,"useCases":398,"indexable":203},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":123,"label":400,"issuer":325,"region":326,"url":401,"description":402,"useCases":403,"indexable":203},"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":405,"label":406,"issuer":407,"region":326,"url":408,"description":409,"useCases":403,"indexable":203},"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":411,"label":412,"issuer":413,"region":157,"url":414,"description":415,"useCases":416,"indexable":203},"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":418,"label":419,"issuer":420,"region":340,"url":421,"description":422,"useCases":423,"indexable":203},"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":425,"label":426,"issuer":325,"region":326,"url":427,"description":428,"useCases":429,"indexable":203},"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":431,"label":432,"issuer":433,"region":157,"url":434,"description":435,"useCases":436,"indexable":203},"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":438,"label":439,"issuer":440,"region":157,"url":441,"description":442,"useCases":436,"indexable":203},"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":444,"label":445,"issuer":325,"region":326,"url":446,"description":447,"useCases":448,"indexable":203},"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":450,"label":451,"issuer":452,"region":340,"url":453,"description":454,"useCases":448,"indexable":203},"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":456,"label":457,"issuer":458,"region":157,"url":459,"description":460,"useCases":461,"indexable":203},"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.",11,{"id":463,"label":464,"issuer":325,"region":326,"url":465,"description":466,"useCases":461,"indexable":203},"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":468,"label":469,"issuer":470,"region":326,"url":471,"description":472,"useCases":473,"indexable":203},"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":475,"label":476,"issuer":373,"region":374,"url":477,"description":478,"useCases":473,"indexable":203},"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":480,"label":481,"issuer":325,"region":326,"url":482,"description":483,"useCases":473,"indexable":203},"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":485,"label":486,"issuer":325,"region":326,"url":487,"description":488,"useCases":473,"indexable":203},"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":490,"label":491,"issuer":325,"region":326,"url":492,"description":493,"useCases":494,"indexable":203},"solvency-ii","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.",9,{"id":496,"label":497,"issuer":498,"region":157,"url":499,"description":500,"useCases":501,"indexable":203},"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":503,"label":504,"issuer":325,"region":326,"url":505,"description":506,"useCases":279,"indexable":203},"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":508,"label":509,"issuer":510,"region":511,"url":512,"description":513,"useCases":514,"indexable":203},"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.",5,{"id":516,"label":517,"issuer":518,"region":326,"url":519,"description":520,"useCases":521,"indexable":203},"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":523,"label":524,"issuer":525,"region":326,"url":526,"description":527,"useCases":521,"indexable":203},"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":529,"label":530,"issuer":531,"region":374,"url":532,"description":533,"useCases":47,"indexable":203},"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":535,"label":536,"issuer":325,"region":326,"url":537,"description":538,"useCases":47,"indexable":203},"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":540,"label":541,"issuer":325,"region":326,"url":542,"description":543,"useCases":47,"indexable":203},"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":545,"label":546,"issuer":547,"region":157,"url":548,"description":549,"useCases":47,"indexable":203},"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.",1790683494150]