[{"data":1,"prerenderedAt":603},["ShallowReactive",2],{"uc-water-network-leak-detection":3,"uc-regulations":381},{"useCase":4,"evidence":190,"blitsAiDeployments":285,"benchmarks":286,"indicative":287,"related":290,"indexability":379,"includeUnpublished":196},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":40,"indicativeValue":44,"macroEstimates":78,"feasibility":79,"implementation":93,"risk":138,"blitsAi":170,"faq":172,"related":182,"datePublished":185,"dateModified":185,"lastVerified":185,"changelog":186,"slug":189},"AI leak detection for water distribution networks","Water network leak detection","AI water leak detection for utility networks","AI leak detection reads acoustic, flow and satellite data. Thames Water says an Origin Orbit trial helped it identify over 800 leaks in 19 weeks.","published","AI water leak detection is machine learning that analyses acoustic sensor recordings, flow and pressure data or satellite imagery to find leaks in a water utility's pipe network. It points crews to likely leak locations, and it learns to tell a leak from background noise.",[12,13,14,15,16],"AI leak detection for water mains","acoustic leak detection with machine learning","non revenue water reduction with AI","satellite leak detection","water loss detection",[18],"energy-and-utilities",[20,21],"network-operations","field-service",[23,24],"anomaly-detection","prediction-and-scoring",[26,27],"internal-tools","api","employee-facing","assist","early-adopters","water","Most leaks in a water network are underground and never seen by customers. Thames Water says a huge\nproportion of its leaks are underground and hard to find. Northumbrian Water says it has traditionally\nrelied on visible signs such as pooling water or pressure changes, and often on customers to report\nthem. Siemens quotes a VA SYD engineer who says that, until recently, the utility could detect only the\nlargest leaks that disrupted service, while the smaller, slower leaks, often from small service pipes,\naccount for the lion's share of non revenue water.\n\nListening devices find more, but they add a second problem. Yorkshire Water has nearly 40,000 acoustic\nloggers that listen each morning; when one flags a suspected leak an analyst confirms it and an\ninspector goes to site, and some alarms are background noise mistaken for a leak. Each false alarm\ncosts a site visit, so the cost of detection rises with the number of sensors unless the software\nseparates leaks from noise.",[],"1. **Collect signals.** Acoustic loggers or sensors on fittings record the sound of water escaping,\n   flow meters and smart meters report flow and pressure by zone, and satellites provide ground\n   moisture, temperature and soil disturbance data.\n2. **Classify the signal.** A model learns what normal operation looks like and what a leak sounds or\n   looks like. Yorkshire Water's trial taught an engine to distinguish a leak from background noise in\n   audio recordings. Siemens' SIWA Leak Finder is trained on historical flow and pressure data to\n   separate anomalies from regular operation and assign a cause to each one.\n3. **Locate.** Northumbrian Water says FIDO Tech's AI gives technicians accurate areas of interest to\n   investigate, even when no water shows at the surface.\n4. **Confirm.** An analyst reviews the alert and an inspector goes to site to pinpoint the leak.\n5. **Repair and feed back.** Close each work order with the result, a confirmed leak or a false alarm.\n   The reports on this page do not describe retraining, but those labels are what a team needs to tune\n   or retrain a model and to track its false alarm rate.",[36,37,38,39],"cost-to-serve","risk-reduction","employee-productivity","customer-experience",[41,42,43],"detection-rate-improvement","false-positive-reduction","mttr-reduction",{"referenceOrg":45,"inputs":46,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"A water utility that loses 50 megalitres of treated water a day to leakage",[47,53,60,66],{"key":48,"label":49,"low":50,"high":50,"unit":51,"note":52},"dailyLossMl","Water lost to leakage",50,"megalitres per day","The reference utility. Editorial assumption, replace with your own leakage figure.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"recoveredShare","Additional share of that loss found and repaired",0.02,0.08,"fraction of leakage","Editorial assumption. Replace with the share your own trial or zone data supports.",{"key":61,"label":62,"low":63,"high":63,"unit":64,"note":65},"daysPerYear","Days in the year",365,"days","Constant.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"valuePerMl","Value of one megalitre of treated water",300,700,"USD per megalitre","Editorial assumption for the production and treatment cost of water. Replace with your own marginal cost.","dailyLossMl * recoveredShare * daysPerYear * valuePerMl","USD","per year","Treated water cost avoided","Counts the production cost of water not lost. It leaves out the cost of sensors, software and inspection and repair crews, the value of water in a drought, regulatory leakage targets and penalties, and the avoided damage from bursts.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":87},"medium","Hardware and models come as a service from the sensor or analytics vendor. The utility needs a network model with pipe, zone and meter data, a sensor roll out plan, data links from the SCADA or metering systems, and an inspection team that records the outcome of every alert. VA SYD had to run its software on its own servers, which needed its IT and operational technology teams and Siemens.",[83,84,85,86],"A hydraulic model or at least a zone map of the network with pipe materials and ages","Flow, pressure and meter data by zone, with history to train on","Acoustic recordings or sensor readings, with outcomes of past investigations","Work order history that records whether each alert was a leak",[88,89,90,91,92],"SCADA and telemetry for flow and pressure","Smart meter or advanced metering data","Geographic information system for the pipe network","Work order and field service system for inspectors","Sensor or satellite data provider",{"steps":94,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[95,98,101,104,107,110],{"title":96,"detail":97},"Pick a zone and fix the baseline","Choose a zone with reliable district metering and measure its night flow, leakage and the number of investigations per leak found before any change. VA SYD began with a proof of concept area of about 5,000 consumers.",{"title":99,"detail":100},"Connect the data","Link flow, pressure and meter data and the hydraulic model to the analytics platform, and settle whether it can run in the cloud or has to run on the utility's own servers.",{"title":102,"detail":103},"Test on known leaks","Check the system against past leaks and, where possible, simulated ones. VA SYD simulated leaks and reports detecting some as small as 0.5 litres per second.",{"title":105,"detail":106},"Put analysts on confirmation","Route every alert to an analyst who confirms or rejects it, and record the reason. This builds the labeled data that lets the model improve and shows the false alarm rate.",{"title":108,"detail":109},"Change how crews are dispatched","Agree which alerts inspectors visit first, using the confirmation record, and track repair time from alert to fix.",{"title":111,"detail":112},"Widen coverage","Extend to more zones once the false alarm rate and the cost per leak found are known, and mix sensor types where they find different leaks.",[114,115,116,117],"A person confirms an alert before an inspection is raised, until false alarm rates are known","Location estimates are shown with a confidence level, never as exact values","No automatic valve operation or pressure change is triggered by the model without an operator","Smart meter data tied to an address is handled as personal data under data protection law","Analysts confirm alerts and decide which to investigate. Inspectors locate the leak on site, and network engineers decide on pressure management and repair priority. Leakage managers review the false alarm rate and the leaks found per inspection every month.",[120,121,122,123,124],"Leaks found per month and per 100 km of mains, against the same period in earlier years","Share of alerts that turn out to be leaks, and false alarms per leak found","Time from alert to repair","Night flow or estimated volume of leakage by zone","Cost per leak found, including sensors and crew time",[126,129,132,135],{"title":127,"detail":128},"More leaks found mistaken for better detection","Finding more leaks can mean better detection, but also a drier summer or more sensors. Thames Water's 88% rise came in a record dry July and with a larger sensor network, and the release does not split it by method. Report leaks found alongside leakage volume.",{"title":130,"detail":131},"Alert volume outruns crews","More sensors and more alerts than the repair team can handle leave known leaks open. Size crews and priorities before adding coverage.",{"title":133,"detail":134},"Models that do not travel","A model tuned on one pipe material or soil type misreads another. Test in each new zone and keep the analyst confirmation step.",{"title":136,"detail":137},"Savings that cannot be traced","Company reports of litres saved rarely state a baseline. Fix the baseline and measure leakage volume before and after, not only leaks found.",{"euAiAct":139,"regulations":142,"guidance":149,"controls":164,"incidents":169},{"tier":140,"basis":141},"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 water, and Recital 55 defines such components as systems that directly protect the physical integrity of critical infrastructure. Recital 55 names systems for monitoring water pressure as examples of safety components, so a model that monitors flow and pressure may fall under Annex III point 2 when its failure could directly risk the network's physical integrity or people's safety, even without automatic valve control. Analysis of acoustic or satellite data used only to plan inspections is the likelier case outside Annex III. Assess each design on its own. Smart meter data tied to a household is personal data under the GDPR.",[143,144,145,146,147,148],"eu-ai-act","nis2","gdpr","uk-gdpr","nist-ai-rmf","iso-42001",[150,156,160],{"title":151,"issuer":152,"region":153,"url":154,"note":155},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 2 lists safety components in the management and operation of critical infrastructure, including the supply of water.",{"title":157,"issuer":152,"region":153,"url":158,"note":159},"Recital 55, safety components of critical infrastructure","https:\u002F\u002Fartificialintelligenceact.eu\u002Frecital\u002F55\u002F","Defines safety components as systems that directly protect the physical integrity of critical infrastructure and names systems for monitoring water pressure as examples, the test that decides the tier here.",{"title":161,"issuer":152,"region":153,"url":162,"note":163},"Directive (EU) 2020\u002F2184 on the quality of water intended for human consumption","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2020\u002F2184\u002Foj","Article 4(3) requires Member States to assess water leakage levels and the potential for reducing them, which is the reporting context for any leak detection program in the EU.",[165,166,167,168],"Inventory entry for each model with an accountable owner and a description of what it may trigger","Records of every alert, its confirmation or rejection and the outcome of the inspection","Quarterly review of false alarm rate and missed leaks found by other means","Access control and security monitoring on the telemetry and sensor networks",[],{"howToBuild":171},"The detection models come from the sensor, satellite or analytics vendor. Blits.ai adds the\nconversations around the work. An **AI agent** with a **SQL knowledge base** over alerts, work orders\nand zone leakage figures lets leakage managers and analysts ask which zones have the most open alerts\nand how old they are, and a **knowledge base** with the utility's investigation procedures and repair\nstandards gives crews consistent answers. **Custom functions** read alert status and create work\norders in the field service system through its API.\n\nFor customers, an agent on **web chat, WhatsApp, SMS and voice** takes reports of a visible leak or low\npressure, checks whether a known incident is open, and raises a case, with **human handover** to the\ncontact center for anything outside the agent's scope. An **agentic workflow** can draft the\nnotice to affected households for approval before it is sent. **Guardrails** and **PII masking**\nprotect customer data, **monitors** and **test suites** check the agent after each change, and the\nplatform is model agnostic with EU and UAE data residency.",[173,176,179],{"question":174,"answer":175},"What results do water companies report from AI leak detection?","Thames Water says a 19 week satellite trial with Origin Orbit helped it identify over 800 leaks, saving an estimated 8.7 million litres a day. It also found 88% more leaks in July 2026 than in July 2025, but that count covers its whole leak finding work in a record dry month, not AI alone. Yorkshire Water reported in 2021 that false alarms had the potential to fall by up to 60% in a machine learning trial. Northumbrian Water says early results from a FIDO Tech trial saved nearly 6.4 megalitres a day. All are company reports. None gives a leakage volume baseline to check the savings against, and the Thames 88% is a count of leaks found for the whole program.",{"question":177,"answer":178},"Does AI replace acoustic loggers and field inspection?","No. The reported systems sit on top of sensors and inspections. Yorkshire Water's engine learns to separate leaks from noise in recordings from loggers it already has, Thames Water uses AI on satellite data alongside more than 75,000 acoustic sensors, Yorkshire Water has an analyst confirm each flagged leak before an inspector visits, and Northumbrian Water's technicians investigate the areas of interest the AI gives them.",{"question":180,"answer":181},"Is AI leak detection high risk under the EU AI Act?","It depends on the design. Recital 55 names systems for monitoring water pressure as examples of safety components of critical infrastructure, so a model that monitors flow and pressure may fall under Annex III point 2 when its failure could directly risk the network's physical integrity or people's safety, even without automatic valve control. Analysis of acoustic or satellite data used only to plan inspections is the likelier case outside Annex III. Assess each design on its own.",[183,184],"smart-meter-analytics","industrial-asset-predictive-maintenance","2026-10-13",[187],{"date":185,"note":188},"First published","water-network-leak-detection",[191,221,241,265],{"title":192,"useCases":193,"organization":194,"vendors":199,"summary":203,"stage":204,"year":205,"channels":206,"languages":207,"metrics":209,"outcomeDisclosed":210,"sources":211,"verification":216,"grade":218,"id":219,"organizationSlug":220},"Thames Water: acoustic sensors and satellite AI to find underground leaks",[189],{"name":195,"anonymized":196,"country":197,"region":153,"industry":18,"wikidata":198},"Thames Water",false,"GB","Q2408951",[200],{"name":201,"role":202},"Origin Orbit","platform","Thames Water, which the release calls the UK's biggest water company, says it now has more than 75,000 acoustic sensors on its network, up from 21,000 in 2025, and that they have nearly doubled leak detection accuracy. It also uses an AI driven service from Origin Orbit that reads satellite data for moisture, temperature and soil changes that point to underground leaks. In a 19 week trial the company says Origin Orbit helped it identify over 800 leaks, saving an estimated 8.7 million litres of water a day. Thames Water also reports that it found 88% more leaks in July 2026 than in July 2025. That count is for the company's whole leak finding work, which the release describes as AI, satellite data and acoustic sensors together, in the driest July in England in 190 years, so it shows more leaks found, not what the AI contributed.","pilot",2026,[26],[208],"en",[],true,[212],{"url":213,"title":214,"publisher":195,"date":215},"https:\u002F\u002Fwww.thameswater.co.uk\u002Fnews\u002F2026\u002Faug\u002Fthames-water-uses-satellites-ai-and-thousands-of-acoustic-sensors-to-hunt-down-leaks-as-south-east-drought-pressure-intensifies","Thames Water uses satellites, AI and thousands of acoustic sensors to hunt down leaks as South East drought pressure intensifies","2026-08-24",{"level":217,"checkedAt":185},"source-verified","B","thames-water-ai-leak-detection",null,{"title":222,"useCases":223,"organization":224,"vendors":227,"summary":230,"stage":204,"year":231,"channels":232,"languages":233,"metrics":234,"outcomeDisclosed":210,"sources":235,"verification":239,"grade":218,"id":240,"organizationSlug":220},"Northumbrian Water: FIDO Tech AI acoustic monitors on water fittings",[189],{"name":225,"anonymized":196,"country":197,"region":153,"industry":18,"wikidata":226},"Northumbrian Water","Q7059853",[228],{"name":229,"role":202},"FIDO Tech","Northumbrian Water began a six month trial in November 2025 with FIDO Tech, whose acoustic monitors attach to fittings on water pipes and use AI to analyse the sound of water escaping underground. The AI tells engineers which sounds are leaks and which are not, and gives technicians an area of interest to investigate even when no water shows at the surface. The sensors can stay in place or be moved daily to sweep a wider area. The company reports that early results from the trial had already helped it save nearly 6.4 megalitres of water a day.",2025,[26],[208],[],[236],{"url":237,"title":238,"publisher":225},"https:\u002F\u002Fwww.nwg.co.uk\u002Fnews-and-media\u002Fnews-releases\u002Fai-technology-helps-to-hunt-down-leaks\u002F","AI technology helps to hunt down leaks",{"level":217,"checkedAt":185},"northumbrian-water-fido-tech-leak-detection",{"title":242,"useCases":243,"organization":244,"vendors":247,"summary":253,"stage":204,"year":254,"channels":255,"languages":256,"metrics":257,"outcomeDisclosed":210,"sources":258,"verification":263,"grade":218,"id":264,"organizationSlug":220},"Yorkshire Water: machine learning on acoustic logger recordings to cut false leak alarms",[189],{"name":245,"anonymized":196,"country":197,"region":153,"industry":18,"wikidata":246},"Yorkshire Water","Q8055763",[248,250],{"name":249,"role":202},"Siemens",{"name":251,"role":252},"Artesia Consulting","integrator","Yorkshire Water has nearly 40,000 acoustic loggers on its clean water network that listen for leaks each morning. When a logger flags a suspected leak an analyst confirms it and an inspector visits the site, and some alarms turn out to be background noise. In 2021 the utility trialled a machine learning engine from Siemens and Artesia Consulting that learns to tell a leak from noise in the audio recordings. The utility reported that false alarms had the potential to fall by up to 60% and that the system could find more true leaks at the same time. The 2021 release does not give a later rollout result.",2021,[26],[208],[],[259],{"url":260,"title":261,"publisher":245,"date":262},"https:\u002F\u002Fwww.yorkshirewater.com\u002Fnews-media\u002Fnews-articles\u002F2021\u002Fyorkshire-water-sees-promising-results-in-ai-leak-detection-trial","Promising results for AI leak detection trial","2021-07-19",{"level":217,"checkedAt":185},"yorkshire-water-siemens-ai-leak-detection",{"title":266,"useCases":267,"organization":268,"vendors":272,"summary":274,"stage":204,"year":254,"channels":275,"languages":276,"metrics":277,"outcomeDisclosed":210,"sources":278,"verification":282,"grade":283,"id":284,"organizationSlug":220},"VA SYD: Siemens SIWA Leak Finder on flow and pressure data",[189],{"name":269,"anonymized":196,"country":270,"region":153,"industry":18,"wikidata":271},"VA SYD","SE","Q18334489",[273],{"name":249,"role":202},"VA SYD supplies drinking water to more than 546,000 customers around Lund and Malmö in southern Sweden. It tested Siemens' SIWA Leak Finder, an AI based algorithm trained on historical flow and pressure data that identifies and classifies anomalies in the pipe network, in a proof of concept area with 5,000 consumers. Siemens reports that the utility detected simulated leaks as small as 0.5 litres per second, and that non revenue water in its drinking water network could be brought down from 10 percent to 8 percent. VA SYD ran the software in its own data centres, and Siemens says other Swedish municipalities have adopted the same solution.",[26],[208],[],[279],{"url":280,"title":281,"publisher":249},"https:\u002F\u002Fwww.siemens.com\u002Fen-us\u002Fcompany\u002Finsights\u002Fva-syd-water-artificial-intelligence\u002F","A case for AI: Water pipeline leaks",{"level":217,"checkedAt":185},"C","va-syd-siemens-siwa-leak-finder",0,[],{"low":288,"high":289},109500,1022000,[291,310,332,355],{"slug":183,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":296,"patterns":299,"audience":300,"autonomy":29,"adoptionStage":30,"segment":301,"evidenceCount":302,"publicEvidenceCount":302,"organizations":303,"bestGrade":283,"headline":220,"lastVerified":309,"indexable":210},"AI analytics for smart meter and AMI data","Smart meter analytics","Smart meter analytics is AI that turns readings from smart electricity, gas and water meters into usable information for utilities, instead of treating every meter and customer the same way. 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.",[18],[297,298],"operations","analytics-and-reporting",[23,24],"back-office","metering-and-billing",5,[304,305,306,307,308],"Arizona Public Service (APS)","Consolidated Edison (Con Edison)","Fluvius","NV Energy","Southern California Gas Company (SoCalGas)","2026-09-28",{"slug":184,"title":311,"shortTitle":312,"definition":313,"status":9,"industries":314,"functions":316,"patterns":317,"audience":28,"autonomy":29,"adoptionStage":30,"segment":318,"evidenceCount":319,"publicEvidenceCount":319,"organizations":320,"bestGrade":218,"headline":220,"lastVerified":331,"indexable":210},"AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","AI predictive maintenance is machine learning that learns the normal behaviour of industrial and energy equipment and flags early signs of degradation before a failure. It works from sensor and process data, flags issues weeks or months ahead, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[18,315],"manufacturing",[297,21],[23,24],"asset-management",10,[321,322,323,324,325,326,327,328,329,330],"ADNOC","Colgate-Palmolive","Duke Energy","DuPont","Equinor","Georgia-Pacific","Holcim","Shell","Tenaga Nasional Berhad (TNB)","Tokyo Electric Power Grid (TEPCO Power Grid)","2026-09-27",{"slug":333,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":339,"patterns":340,"audience":300,"autonomy":342,"adoptionStage":30,"segment":343,"evidenceCount":344,"publicEvidenceCount":344,"organizations":345,"bestGrade":218,"headline":220,"lastVerified":331,"indexable":210},"predictive-network-maintenance","AI for predictive network maintenance in telecom","Predictive network maintenance","Predictive network maintenance is machine learning that spots early signs of network failure and triggers a fix before customers lose service, for telecom operators. It watches for degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention.",[338],"telecommunications",[20,21,297],[23,24,341],"agentic-workflow","supervised-agent","network",9,[346,347,348,349,350,351,352,353,354],"Grameenphone","KDDI","Orange","Telefónica España","Telkomsel","Telstra","TIM Brasil","Verizon","Vodafone",{"slug":356,"title":357,"shortTitle":358,"definition":359,"status":9,"industries":360,"functions":361,"patterns":362,"audience":28,"autonomy":363,"adoptionStage":30,"segment":364,"evidenceCount":365,"publicEvidenceCount":365,"organizations":366,"bestGrade":218,"headline":370,"lastVerified":378,"indexable":210},"storm-outage-prediction-and-restoration","AI storm outage prediction and restoration staging for power grids","Storm outage prediction and restoration","AI storm outage prediction is AI that predicts how many outages a coming storm will cause, where, and how severe restoration will be, for utility storm response teams. It combines weather forecasts, historical outage records and network data, so a utility can stage crews and mutual aid before the storm arrives instead of assessing damage after it hits.",[18],[20,21],[24],"copilot","grid",3,[367,368,369],"CenterPoint Energy","Hydro One","New Brunswick Power (NB Power)",{"kpi":43,"label":371,"unit":372,"n":373,"nUpTo":285,"kind":374,"value":375,"qualifier":376,"claimant":377,"organization":368,"vendorReported":210},"Time to repair reduction","percent",1,"reported",33,"approximately","vendor","2026-09-29",{"indexable":210,"reasons":380},[],[382,387,392,399,406,412,418,425,433,440,447,452,458,465,472,479,485,491,498,503,508,515,521,528,533,538,543,548,555,561,568,574,580,586,592,597],{"id":143,"label":383,"issuer":152,"region":153,"url":384,"description":385,"useCases":386,"indexable":210},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":145,"label":388,"issuer":152,"region":153,"url":389,"description":390,"useCases":391,"indexable":210},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":148,"label":393,"issuer":394,"region":395,"url":396,"description":397,"useCases":398,"indexable":210},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":147,"label":400,"issuer":401,"region":402,"url":403,"description":404,"useCases":405,"indexable":210},"NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":146,"label":407,"issuer":408,"region":153,"url":409,"description":410,"useCases":411,"indexable":210},"UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":413,"label":414,"issuer":152,"region":153,"url":415,"description":416,"useCases":417,"indexable":210},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":419,"label":420,"issuer":421,"region":153,"url":422,"description":423,"useCases":424,"indexable":210},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":426,"label":427,"issuer":428,"region":429,"url":430,"description":431,"useCases":432,"indexable":210},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":434,"label":435,"issuer":436,"region":429,"url":437,"description":438,"useCases":439,"indexable":210},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":441,"label":442,"issuer":443,"region":402,"url":444,"description":445,"useCases":446,"indexable":210},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":144,"label":448,"issuer":152,"region":153,"url":449,"description":450,"useCases":451,"indexable":210},"NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":453,"label":454,"issuer":455,"region":395,"url":456,"description":457,"useCases":451,"indexable":210},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":459,"label":460,"issuer":461,"region":153,"url":462,"description":463,"useCases":464,"indexable":210},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":466,"label":467,"issuer":468,"region":402,"url":469,"description":470,"useCases":471,"indexable":210},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":473,"label":474,"issuer":475,"region":395,"url":476,"description":477,"useCases":478,"indexable":210},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":480,"label":481,"issuer":152,"region":153,"url":482,"description":483,"useCases":484,"indexable":210},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":486,"label":487,"issuer":488,"region":402,"url":489,"description":490,"useCases":484,"indexable":210},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":492,"label":493,"issuer":494,"region":402,"url":495,"description":496,"useCases":497,"indexable":210},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":499,"label":500,"issuer":152,"region":153,"url":501,"description":502,"useCases":497,"indexable":210},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":504,"label":505,"issuer":152,"region":153,"url":506,"description":507,"useCases":497,"indexable":210},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":509,"label":510,"issuer":511,"region":402,"url":512,"description":513,"useCases":514,"indexable":210},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":516,"label":517,"issuer":518,"region":395,"url":519,"description":520,"useCases":514,"indexable":210},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":522,"label":523,"issuer":524,"region":153,"url":525,"description":526,"useCases":527,"indexable":210},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":529,"label":530,"issuer":152,"region":153,"url":531,"description":532,"useCases":527,"indexable":210},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":534,"label":535,"issuer":152,"region":153,"url":536,"description":537,"useCases":527,"indexable":210},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":539,"label":540,"issuer":428,"region":429,"url":541,"description":542,"useCases":319,"indexable":210},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":544,"label":545,"issuer":152,"region":153,"url":546,"description":547,"useCases":319,"indexable":210},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":549,"label":550,"issuer":551,"region":402,"url":552,"description":553,"useCases":554,"indexable":210},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":556,"label":557,"issuer":152,"region":153,"url":558,"description":559,"useCases":560,"indexable":210},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":562,"label":563,"issuer":564,"region":565,"url":566,"description":567,"useCases":302,"indexable":210},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":569,"label":570,"issuer":152,"region":153,"url":571,"description":572,"useCases":573,"indexable":210},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":575,"label":576,"issuer":577,"region":153,"url":578,"description":579,"useCases":573,"indexable":210},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":581,"label":582,"issuer":583,"region":153,"url":584,"description":585,"useCases":573,"indexable":210},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":587,"label":588,"issuer":589,"region":429,"url":590,"description":591,"useCases":365,"indexable":210},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":593,"label":594,"issuer":152,"region":153,"url":595,"description":596,"useCases":365,"indexable":210},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":598,"label":599,"issuer":600,"region":402,"url":601,"description":602,"useCases":365,"indexable":210},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904359916]