[{"data":1,"prerenderedAt":556},["ShallowReactive",2],{"uc-industrial-asset-predictive-maintenance":3,"uc-regulations":346},{"useCase":4,"evidence":182,"blitsAiDeployments":264,"benchmarks":265,"indicative":272,"related":275,"indexability":344,"includeUnpublished":188},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":23,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":47,"macroEstimates":76,"feasibility":77,"implementation":90,"risk":132,"blitsAi":164,"faq":166,"related":176,"datePublished":177,"dateModified":177,"lastVerified":177,"changelog":178,"slug":181},"AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","AI predictive maintenance for industrial assets","C3 AI said in 2022 that Shell monitors over 10,000 pieces of equipment with it; AVEVA says one Duke Energy catch avoided $34 million in cost.","published","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.",[12,13,14,15,16],"predictive maintenance for industrial equipment","equipment failure prediction","condition based maintenance with AI","asset performance management","remote monitoring and diagnostics centre",[18,19],"energy-and-utilities","manufacturing",[21,22],"operations","field-service",[24,25],"anomaly-detection","prediction-and-scoring",[27,28],"internal-tools","api","employee-facing","assist","early-adopters","asset-management","Plants and power stations run on pumps, compressors, valves, turbines, motors and conveyors that\nwear out. Many are maintained on a fixed calendar or run until they fail. Calendar maintenance\nreplaces parts that still had life in them; run to failure means an unplanned stop, emergency\nparts at premium prices, lost production and, in energy, safety and environmental risk.\n\nThe signals of an approaching failure are usually there: a bearing runs slightly warmer, vibration\ncreeps up, a valve takes longer to close. But a large site has thousands of sensors, and the few\nexperienced engineers who can read those patterns cannot watch all of them. Some companies also\nexpect to lose that knowledge: Georgia-Pacific said many of its site experts were \"retiring soon\"\n(AWS). Predictive maintenance lets models watch every asset continuously and send\nthe experts only the cases that need their judgment.",[],"1. **Collect the signals.** Temperature, vibration, pressure, flow, current and process data\n   stream from the control systems and historians into one data platform.\n2. **Learn normal behaviour.** For each asset, a model learns how its readings relate to each\n   other under normal operation, or is trained on labelled past failures where they exist.\n3. **Detect and predict.** The model flags deviations early and, where history allows, estimates\n   the likely failure mode and the time left, weeks or months ahead.\n4. **Triage centrally.** Analysts in a central monitoring and diagnostics centre review early\n   warnings, set aside false alerts and confirm real issues with the site. At the time of the AVEVA\n   story, Duke Energy ran such a centre, with five analysts, for over 87% of its generating fleet\n   (AVEVA). At most Shell assets, a remote engineer vets each alert before it goes to the asset\n   engineers (Shell).\n5. **Plan the work.** Confirmed issues become prioritised work orders with the parts and the\n   window for the repair, planned into the next outage instead of forcing an unplanned one.\n6. **Feed back.** What the technicians find on the equipment is recorded against the alert, so the\n   models and thresholds improve.",[37,38,39,40],"risk-reduction","cost-to-serve","speed","employee-productivity",[42,43,44,45,46],"cost-savings","cost-reduction","mttr-reduction","detection-rate-improvement","false-positive-reduction",{"referenceOrg":48,"inputs":49,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A process plant or power station with 150 critical rotating and process assets",[50,57,64],{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"downtimeHours","Unplanned downtime hours per year on critical assets",100,150,"hours per year","Editorial assumption, replace with your own downtime records.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"costPerHour","Cost of one hour of unplanned downtime",20000,60000,"USD per hour","Editorial assumption covering lost production, emergency repair and restart. Replace with your own figure.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"avoidedShare","Share of unplanned downtime avoided or converted into planned work",0.1,0.3,"fraction of downtime hours","Editorial assumption. The evidence on this page reports one early catch at Duke Energy that AVEVA says avoided more than 34 million US dollars in cost for that single event (AVEVA), but no fleet wide downtime reduction, so the range stays conservative.","downtimeHours * costPerHour * avoidedShare","USD","per year","Unplanned downtime cost avoided","Counts avoided downtime only. It leaves out sensors, data platform and analyst costs, the cost of the planned repairs that replace breakdowns, savings from fewer unnecessary calendar maintenance tasks, and the safety and environmental value of avoided failures.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":85},"high","The models are well understood; the hard parts are clean sensor data from many different control systems, too few recorded failures to learn from, and changing the maintenance process so that alerts actually become planned work.",[81,82,83,84],"Historian or control system data per asset at a useful sampling rate","An asset register with criticality, failure modes and maintenance history","Past failures and work orders linked to the sensor data where possible","Engineering knowledge of normal operating ranges per asset type",[86,87,88,89],"Plant historian and control systems (SCADA, DCS)","Data platform for streaming and model training","Enterprise asset management or computerised maintenance management system for work orders","Alerting to the monitoring centre and site teams",{"steps":91,"guardrails":107,"humanInTheLoop":112,"kpisToInstrument":113,"failureModes":119},[92,95,98,101,104],{"title":93,"detail":94},"Start with critical assets and known failure modes","Rank assets by the cost and risk of failure and pick one asset class with sensor coverage and a few documented failures, such as large pumps or compressors.",{"title":96,"detail":97},"Get the data flowing and trusted","Connect the historian, fix tag names and units, and agree with the site which readings are reliable. Many first alerts turn out to be sensor faults, not machine faults.",{"title":99,"detail":100},"Stand up a central monitoring team","Put a small group of experienced engineers between the models and the sites to triage alerts, so the sites only see confirmed issues.",{"title":102,"detail":103},"Wire alerts into maintenance planning","Create work orders in the maintenance system from confirmed alerts, with a priority and a repair window, and record what the technician found.",{"title":105,"detail":106},"Scale by asset class, then by site","Reuse models and templates across identical equipment, track avoided failures with a written case per catch, and widen coverage one asset class at a time. Shell set itself a target of 10,000 monitored pieces of critical equipment for 2021 and reported reaching it.",[108,109,110,111],"Alerts advise; protection systems and trips stay in the certified control and safety systems","A human engineer confirms every alert before a work order or shutdown is raised","Model changes tested against past data before release, with version history","Sensor health checks so that faulty instruments are not read as failing machines","Monitoring and diagnostics engineers triage every alert and decide whether it becomes work. Site maintenance planners choose when to repair, and reliability engineers review missed failures and false alarms each month to tune the models.",[114,115,116,117,118],"Unplanned downtime hours on monitored assets, before and after","Documented early catches and their estimated avoided cost","Share of alerts confirmed as real issues","Lead time between first alert and failure or repair","Failures on monitored assets that the models missed",[120,123,126,129],{"title":121,"detail":122},"Alert fatigue","Too many alerts with too little context and sites stop reacting. Triage centrally and report the confirmation rate.",{"title":124,"detail":125},"No link to the maintenance process","Alerts land in a dashboard nobody plans from. Create work orders from confirmed alerts in the maintenance system.",{"title":127,"detail":128},"Too few failures to learn from","Critical assets rarely fail, so supervised models lack examples. Use anomaly detection on normal behaviour and engineering rules alongside.",{"title":130,"detail":131},"Savings nobody believes","Claimed savings that cannot be traced to a specific catch lose credibility. Write up each early catch with what would have happened.",{"euAiAct":133,"regulations":136,"guidance":141,"controls":158,"incidents":163},{"tier":134,"basis":135},"context-dependent","A system that advises engineers on the condition of equipment is usually minimal risk. Annex III point 2 lists AI systems intended as safety components in the management and operation of critical digital infrastructure, road traffic and the supply of water, gas, heating or electricity; if predictive maintenance acts on protection or control in a utility network, it can become high risk. Article 6(1) can also apply when the AI is a safety component of machinery or another product covered by Annex I legislation and that product must undergo a third party conformity assessment.",[137,138,139,140],"eu-ai-act","nis2","iso-42001","nist-ai-rmf",[142,148,152],{"title":143,"issuer":144,"region":145,"url":146,"note":147},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 2 covers safety components in the management and operation of critical infrastructure, including the supply of water, gas, heating and electricity.",{"title":149,"issuer":144,"region":145,"url":150,"note":151},"Article 6, classification rules for high risk AI systems","https://artificialintelligenceact.eu/article/6/","Explains when an AI system that is a safety component of a product under Annex I legislation, such as machinery, is high risk.",{"title":153,"issuer":154,"region":155,"url":156,"note":157},"AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary framework to map, measure and manage the risks of AI systems, useful for documenting model limits and monitoring.",[159,160,161,162],"Asset and model inventory with an owner per model and the assets it covers","Documented separation between advisory models and certified protection systems","Written record of every confirmed catch and every missed failure","Cybersecurity controls on data flows from operational technology, in line with NIS2 where it applies",[],{"howToBuild":165},"The anomaly and failure models run on the manufacturer's or utility's asset analytics platform.\nBlits.ai adds the layer where people act on them. An **AI agent** with a **SQL knowledge base**\nover a PostgreSQL or SQLite copy of alerts, asset data and work orders lets engineers ask which\nassets are trending toward failure and why, and a **knowledge base** with hybrid retrieval over manuals and past failure\nreports explains the likely cause and repair.\n\n**Agentic tasks** run condition triggered checks (\"when a confirmed alert is older than two days\nwithout a work order, draft one\") and **human in the loop confirmation**, with the threshold set\nso that it covers work orders and escalations, lets an engineer approve or reject each one before\n**custom functions** create it through the maintenance system's API. Engineers use it in **Microsoft Teams**, **monitors** check its answers on a\nschedule, and the platform is model agnostic with EU and UAE data residency.",[167,170,173],{"question":168,"answer":169},"What results do companies report from predictive maintenance?","AVEVA reports that a single early catch by Duke Energy's monitoring and diagnostics centre in 2016 avoided more than 34 million US dollars in cost had the problem gone undetected; that is one event, not a yearly saving. AWS reports that Georgia-Pacific can predict failure of selected assets 60 to 90 days ahead, without an outcome figure. Shell writes that at one Dutch refinery its models flagged 65 control valves in need of repair that traditional methods would have missed. Published fleet wide downtime figures are rare, so measure your own avoided failures case by case.",{"question":171,"answer":172},"How many assets can one programme cover?","In a March 2022 press release, C3 AI said Shell's predictive maintenance programme on its platform monitors more than 10,000 pieces of equipment; Shell, quoted in the release, called monitoring 10,000 pieces of critical equipment a target set for 2021 and achieved. Coverage grows asset class by asset class, reusing models across identical equipment.",{"question":174,"answer":175},"Is predictive maintenance high risk under the EU AI Act?","Usually not while it advises engineers. It can become high risk if it acts as a safety component in the supply of water, gas, heating or electricity (Annex III point 2), or a safety component of machinery covered by Annex I legislation that is subject to third party conformity assessment (Article 6(1)).",[],"2026-09-27",[179],{"date":177,"note":180},"First published","industrial-asset-predictive-maintenance",[183,215,237],{"title":184,"useCases":185,"organization":186,"vendors":191,"summary":195,"stage":196,"year":197,"channels":198,"languages":199,"metrics":201,"outcomeDisclosed":188,"sources":202,"verification":210,"grade":212,"id":213,"organizationSlug":214},"Shell: AI predictive maintenance on the C3 AI platform scaled to 10,000 pieces of equipment",[181],{"name":187,"anonymized":188,"country":189,"region":190,"industry":18},"Shell",false,"GB","global",[192],{"name":193,"role":194},"C3 AI","platform","Shell runs a predictive maintenance programme built on the C3 AI platform. Machine learning models flag equipment degradation and likely failures early so operators can intervene before unplanned downtime, production interruptions or safety and environmental risks; C3 AI names control valves, pumps and compressors among the monitored equipment. In a March 2022 C3 AI press release, Shell's Dan Jeavons said that \"Monitoring 10,000 pieces of critical equipment\" with AI predictive maintenance was a target Shell had set for 2021 and achieved. The wording \"more than 10,000\", the asset scope and the technical figures in the release are C3 AI's. In its own TechXplorer Digest article, Shell describes the rollout asset by asset (a Dutch refinery in 2020, where the models flagged 65 control valves in need of repair that traditional methods would have missed, then Singapore, the USA and Canada in early 2021) and a process in which, for most assets, a remote engineer vets each alert before it reaches the asset engineers.","scaled",2022,[27],[200],"en",[],[203,207],{"url":204,"title":205,"publisher":193,"date":206},"https://c3.ai/shell-achieves-major-milestone-scales-artificial-intelligence-predictive-maintenance-to-10000-pieces-of-equipment-using-c3-ai/","Shell Achieves Major Milestone: Scales Artificial Intelligence Predictive Maintenance to 10,000 Pieces of Equipment Using C3 AI","2022-03-08",{"url":208,"title":209,"publisher":187},"https://www.shell.com/what-we-do/technology-and-innovation/shell-techxplorer-digest/shell-techxplorer-digest-2020/_jcr_content/root/main/section/list_copy_copy_copy/list_item_copy_98181_819446707/links/item0.stream/1669888451651/dabc9c17a2c9a00d39cb4f442e75d667920c8562/the-shell-journey-towards-global-predictive-maintenance-velthuis.pdf","The Shell journey towards global predictive maintenance",{"level":211,"checkedAt":177},"source-verified","B","shell-c3-ai-predictive-maintenance",null,{"title":216,"useCases":217,"organization":218,"vendors":221,"summary":224,"stage":225,"year":226,"channels":227,"languages":228,"metrics":229,"outcomeDisclosed":188,"sources":230,"verification":234,"grade":235,"id":236,"organizationSlug":214},"Georgia-Pacific: predictive analytics on streamed equipment data to predict equipment failure 60 to 90 days ahead",[181],{"name":219,"anonymized":188,"country":220,"region":155,"industry":19},"Georgia-Pacific","US",[222],{"name":223,"role":194},"Amazon Web Services","Georgia-Pacific, a pulp, paper and building products manufacturer, streams data from equipment at its North American facilities into an operations data lake on AWS and analyses it with an AWS based advanced analytics solution that includes Amazon SageMaker machine learning models. For selected assets the company can now predict equipment failure 60 to 90 days in advance, so it can plan equipment downtime instead of suffering unscheduled production stoppages. No outcome figure is published for the failure prediction; the same data platform also runs process optimization models for converting line speeds, which are outside this use case.","production",2019,[27],[200],[],[231],{"url":232,"title":233,"publisher":223},"https://aws.amazon.com/solutions/case-studies/georgia-pacific/","Georgia-Pacific Optimizes Processes, Saves Millions of Dollars Yearly Using AWS",{"level":211,"checkedAt":177},"C","georgia-pacific-predictive-asset-analytics",{"title":238,"useCases":239,"organization":240,"vendors":242,"summary":245,"stage":196,"year":246,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":258,"sources":259,"verification":262,"grade":235,"id":263,"organizationSlug":214},"Duke Energy: central monitoring and diagnostics centre with predictive asset analytics for its generation fleet",[181],{"name":241,"anonymized":188,"country":220,"region":155,"industry":18},"Duke Energy",[243],{"name":244,"role":194},"AVEVA","Duke Energy runs a central Monitoring and Diagnostics (M&D) centre that watches coal, gas, combined cycle and other generating units in several US states with predictive asset analytics software; the AVEVA page names PRiSM Predictive Asset Analytics among its tools. Early warning notifications of equipment problems let a small team of experienced analysts alert the plants before a failure. AVEVA reports that the centre covers over 87% of Duke's generating fleet with over 11,000 models, and that a single early catch in 2016 avoided more than 34 million US dollars in cost had the problem gone undetected.",2016,[27],[200],[250],{"kpi":42,"value":251,"unit":252,"currency":72,"qualifier":253,"period":254,"claimant":255,"quote":256,"sourceUrl":257},34000000,"currency","at-least","a single early catch event in 2016, counterfactual avoided cost","vendor","Savings of over $34 millions in a single early catch event in 2016.","https://www.aveva.com/en/perspectives/success-stories/duke-energy/",true,[260],{"url":257,"title":261,"publisher":244},"Duke Energy Predictive Analytic Success Story",{"level":211,"checkedAt":177},"duke-energy-monitoring-and-diagnostics-center",0,[266],{"kpi":42,"label":267,"unit":252,"currency":72,"aggregate":188,"higherIsBetter":258,"n":268,"nUpTo":264,"median":251,"min":251,"max":251,"byClaimant":269,"vendorOnly":258,"points":270},"Cost savings",1,{"organization":264,"vendor":268,"regulator":264,"independent":264},[271],{"evidenceId":263,"organization":241,"value":251,"qualifier":253,"claimant":255,"grade":235,"pooled":258},{"low":273,"high":274},200000,2700000,[276,298,314,327],{"slug":277,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":283,"patterns":285,"audience":287,"autonomy":288,"adoptionStage":31,"segment":289,"evidenceCount":290,"publicEvidenceCount":290,"organizations":291,"bestGrade":212,"headline":214,"lastVerified":177,"indexable":258},"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.",[282],"telecommunications",[284,22,21],"network-operations",[24,25,286],"agentic-workflow","back-office","supervised-agent","network",6,[292,293,294,295,296,297],"KDDI","Orange","Telefónica España","Telstra","Verizon","Vodafone",{"slug":299,"title":300,"shortTitle":301,"definition":302,"status":9,"industries":303,"functions":305,"patterns":306,"audience":29,"autonomy":30,"adoptionStage":31,"segment":308,"evidenceCount":309,"publicEvidenceCount":309,"organizations":310,"bestGrade":212,"headline":214,"lastVerified":313,"indexable":258},"freight-rail-rolling-stock-predictive-maintenance","AI predictive maintenance for freight rail rolling stock","Rail rolling stock predictive maintenance","Machine vision and machine learning that inspect freight railcar wheels, bearings and other running gear as trains pass wayside sensors and camera portals at track speed, learn what a healthy wheel or a healthy reading looks like, and flag the ones that need attention before a crack, an overheating bearing or a worn wheel causes a service failure or a derailment.",[304],"logistics-and-transportation",[21,22],[307,24,25],"computer-vision","mechanical-and-safety",2,[311,312],"BNSF Railway","Norfolk Southern","2026-09-28",{"slug":315,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":320,"patterns":322,"audience":287,"autonomy":30,"adoptionStage":31,"segment":323,"evidenceCount":309,"publicEvidenceCount":309,"organizations":324,"bestGrade":235,"headline":214,"lastVerified":313,"indexable":258},"smart-meter-analytics","AI analytics for smart meter and AMI data","Smart meter analytics","AI that turns the flood of readings from smart electricity, gas and water meters into usable information: it monitors meter and network health at scale, estimates which appliances drive a household's usage from the meter signal alone, flags unusual consumption, and targets efficiency and electrification programmes at the customers who will benefit most, instead of a utility treating every meter and every customer the same way.",[18],[21,321],"analytics-and-reporting",[24,25],"metering-and-billing",[325,326],"Consolidated Edison (Con Edison)","Southern California Gas Company (SoCalGas)",{"slug":328,"title":329,"shortTitle":330,"definition":331,"status":9,"industries":332,"functions":333,"patterns":335,"audience":29,"autonomy":339,"adoptionStage":340,"segment":289,"evidenceCount":309,"publicEvidenceCount":309,"organizations":341,"bestGrade":212,"headline":214,"lastVerified":177,"indexable":258},"field-technician-copilot-and-dispatch","AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","AI that decides whether a fault needs a site visit at all, predicts what work and parts a job will need, helps plan and update appointments, and gives technicians on site guided diagnosis and instant answers from manuals and past jobs, so more jobs are fixed on the first visit.",[282],[22,21,334],"customer-service",[25,336,337,338],"rag-knowledge-assistant","conversational-agent","classification-and-routing","copilot","emerging",[342,343],"nbn","Openreach",{"indexable":258,"reasons":345},[],[347,352,358,364,368,374,381,388,396,403,410,416,423,430,436,440,447,453,459,465,471,477,483,488,493,500,507,512,517,525,532,538,545,550],{"id":137,"label":348,"issuer":144,"region":145,"url":349,"description":350,"useCases":351,"indexable":258},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":353,"label":354,"issuer":144,"region":145,"url":355,"description":356,"useCases":357,"indexable":258},"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.",180,{"id":139,"label":359,"issuer":360,"region":190,"url":361,"description":362,"useCases":363,"indexable":258},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":140,"label":365,"issuer":154,"region":155,"url":156,"description":366,"useCases":367,"indexable":258},"NIST AI Risk Management Framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":369,"label":370,"issuer":144,"region":145,"url":371,"description":372,"useCases":373,"indexable":258},"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":375,"label":376,"issuer":377,"region":145,"url":378,"description":379,"useCases":380,"indexable":258},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":382,"label":383,"issuer":384,"region":145,"url":385,"description":386,"useCases":387,"indexable":258},"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.",47,{"id":389,"label":390,"issuer":391,"region":392,"url":393,"description":394,"useCases":395,"indexable":258},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":397,"label":398,"issuer":399,"region":392,"url":400,"description":401,"useCases":402,"indexable":258},"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":404,"label":405,"issuer":406,"region":190,"url":407,"description":408,"useCases":409,"indexable":258},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":411,"label":412,"issuer":413,"region":155,"url":414,"description":415,"useCases":409,"indexable":258},"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":417,"label":418,"issuer":419,"region":145,"url":420,"description":421,"useCases":422,"indexable":258},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":424,"label":425,"issuer":426,"region":190,"url":427,"description":428,"useCases":429,"indexable":258},"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":431,"label":432,"issuer":144,"region":145,"url":433,"description":434,"useCases":435,"indexable":258},"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":138,"label":437,"issuer":144,"region":145,"url":438,"description":439,"useCases":435,"indexable":258},"NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":441,"label":442,"issuer":443,"region":155,"url":444,"description":445,"useCases":446,"indexable":258},"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":448,"label":449,"issuer":144,"region":145,"url":450,"description":451,"useCases":452,"indexable":258},"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":454,"label":455,"issuer":456,"region":155,"url":457,"description":458,"useCases":452,"indexable":258},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":460,"label":461,"issuer":462,"region":190,"url":463,"description":464,"useCases":452,"indexable":258},"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":466,"label":467,"issuer":144,"region":145,"url":468,"description":469,"useCases":470,"indexable":258},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":472,"label":473,"issuer":474,"region":155,"url":475,"description":476,"useCases":470,"indexable":258},"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":478,"label":479,"issuer":391,"region":392,"url":480,"description":481,"useCases":482,"indexable":258},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":484,"label":485,"issuer":144,"region":145,"url":486,"description":487,"useCases":482,"indexable":258},"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":489,"label":490,"issuer":144,"region":145,"url":491,"description":492,"useCases":482,"indexable":258},"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":494,"label":495,"issuer":496,"region":145,"url":497,"description":498,"useCases":499,"indexable":258},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":501,"label":502,"issuer":503,"region":155,"url":504,"description":505,"useCases":506,"indexable":258},"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.",8,{"id":508,"label":509,"issuer":144,"region":145,"url":510,"description":511,"useCases":506,"indexable":258},"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.",{"id":513,"label":514,"issuer":144,"region":145,"url":515,"description":516,"useCases":290,"indexable":258},"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":518,"label":519,"issuer":520,"region":521,"url":522,"description":523,"useCases":524,"indexable":258},"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":526,"label":527,"issuer":528,"region":145,"url":529,"description":530,"useCases":531,"indexable":258},"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":533,"label":534,"issuer":535,"region":145,"url":536,"description":537,"useCases":531,"indexable":258},"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":539,"label":540,"issuer":541,"region":392,"url":542,"description":543,"useCases":544,"indexable":258},"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.",3,{"id":546,"label":547,"issuer":144,"region":145,"url":548,"description":549,"useCases":544,"indexable":258},"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":551,"label":552,"issuer":553,"region":155,"url":554,"description":555,"useCases":544,"indexable":258},"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.",1790598303173]