[{"data":1,"prerenderedAt":509},["ShallowReactive",2],{"uc-smart-meter-analytics":3,"uc-regulations":294},{"useCase":4,"evidence":151,"blitsAiDeployments":206,"benchmarks":207,"indicative":208,"related":211,"indexability":292,"includeUnpublished":157},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":42,"macroEstimates":71,"feasibility":72,"implementation":84,"risk":117,"blitsAi":130,"faq":132,"related":145,"datePublished":146,"dateModified":146,"lastVerified":146,"changelog":147,"slug":150},"AI analytics for smart meter and AMI data","Smart meter analytics","AI analytics for smart meter data","Con Edison used C3 AI to monitor its 5.3 million smart meter deployment; Southern California Gas Company used Bidgely for digital home energy reports.","published","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.",[12,13,14,15],"AMI analytics","energy disaggregation","meter data analytics","advanced metering infrastructure analytics",[17],"energy-and-utilities",[19,20],"operations","analytics-and-reporting",[22,23],"anomaly-detection","prediction-and-scoring",[25,26],"internal-tools","api","back-office","assist","early-adopters","metering-and-billing","Con Edison's rollout of 5.3 million smart meters was expected to generate between 100 terabytes\nand 1 petabyte of data annually, far more than any team can review reading by reading. Buried in\nthat volume are the installation and configuration issues that leave a meter or its network module\nunhealthy, which is what Con Edison's deployment set out to find. More generally, the same kind of\ndata holds the individual appliances, such as electric vehicles, heat pumps and air conditioning,\nthat a utility needs to understand as adoption grows, and the customers who would benefit most\nfrom an efficiency programme but are hard to identify from billing data alone.",[],"1. **Ingest at scale.** Readings stream from millions of meters into a data platform, alongside\n   the utility's asset, billing and programme data.\n2. **Watch meter and network health.** Machine learning flags meters and communication modules\n   showing signs of a deployment or configuration problem, so field crews fix the ones that\n   actually need attention.\n3. **Disaggregate usage.** A separate model estimates which appliances, such as HVAC, water\n   heating, electric vehicle charging or pool pumps, are driving each home's consumption from the\n   meter signature alone, without a sensor on the appliance itself.\n4. **Segment and target.** The disaggregated data groups customers by what is actually happening\n   in their home, such as households with an electric vehicle or an ageing HVAC system, so an\n   efficiency, demand response or electrification programme can be targeted instead of broadcast\n   to everyone.\n5. **Feed operations and customer teams.** A prioritised list, a dashboard or a personalised\n   message reaches the team or the customer, closing the loop from raw meter data to action.",[35,36,37],"cost-to-serve","customer-experience","employee-productivity",[39,40,41],"energy-savings","error-reduction","customer-satisfaction",{"referenceOrg":43,"inputs":44,"formula":66,"currency":67,"period":68,"resultLabel":69,"caveat":70},"A utility with 2 million smart meters and an energy efficiency programme budget",[45,52,59],{"key":46,"label":47,"low":48,"high":49,"unit":50,"note":51},"meters","Smart meters in the analytics programme",1500000,2500000,"smart meters","Range set around the 2 million meter reference organization, well within Con Edison's 5.3 million meter deployment.",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"issueRate","Share of meters with a deployment or health issue found by analytics each year",0.005,0.02,"fraction of meters","Editorial assumption, replace with your own meter health data.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"costPerIssue","Cost saved per flagged issue by folding it into a planned visit instead of a repeat or emergency visit",80,200,"USD per issue","Editorial assumption for the difference between a planned and an emergency or repeat US utility field visit; replace with your own figure. Neither deployment on this page reports a per issue avoided cost.","meters * issueRate * costPerIssue","USD","per year","Avoided field visit and rework cost from early meter health detection","Meter health savings only, and it assumes a flagged issue can be folded into a planned visit rather than always triggering a separate site visit, which is an editorial assumption neither deployment on this page confirms. It also leaves out the analytics platform and integration cost, and any separate value from the efficiency and electrification programmes the same data supports, which neither deployment on this page reports as a single company wide figure.",[],{"complexity":73,"complexityNote":74,"dataPrerequisites":75,"integrations":79},"high","Con Edison's deployment aggregated two years of data from 13 source systems covering 5 million customer accounts into an integrated data image; that integration work is typically harder than the machine learning itself.",[76,77,78],"Interval meter reads at the frequency the advanced metering infrastructure supports","Meter and network asset data, such as install date, model and communication module","Customer and premise data linking a meter to a household or business",[80,81,82,83],"Meter data management system","Geospatial information system for network topology","Customer information system or CRM, for targeting and outreach","Demand side management or efficiency programme platforms",{"steps":85,"guardrails":101,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":110},[86,89,92,95,98],{"title":87,"detail":88},"Start with meter and network health, not customer analytics","Con Edison's first phase covered deployment and installation issues plus meter and network health, using two machine learning algorithms and 50 analytics; the company planned further customer insight and distribution and transmission automation applications for later phases.",{"title":90,"detail":91},"Unify the source systems before modelling","Con Edison's deployment integrated two years of data across 13 source systems before the machine learning models were configured; budget and staff for that integration effort, not just the model.",{"title":93,"detail":94},"Disaggregate before you personalise","Appliance level disaggregation from the meter signal is what lets a programme target the households with an electric vehicle or an ageing HVAC system, rather than mailing everyone the same offer.",{"title":96,"detail":97},"Close the loop into an actual programme","Southern California Gas Company's deployment fed a digital only home energy report programme for its medium consumption gas customers, which exceeded its savings goal, not a dashboard; without a programme on the receiving end, better analytics changes nothing for the customer.",{"title":99,"detail":100},"Scale meter by meter, programme by programme","Widen coverage and add new applications, such as electrification planning or peak forecast, once the foundational data platform is proven.",[102,103,104],"Customer usage data used for targeting is handled under the utility's own data privacy and retention rules","A person reviews the priority list before a field crew is dispatched based on a flagged meter","Model changes are tested against historical data before being applied to live operations","Operations analysts triage the prioritised list of flagged meters before a field crew is dispatched, and programme managers decide which segments an efficiency or electrification campaign actually targets based on the disaggregated data.",[107,108,109],"Meters flagged with a health or deployment issue, and the share confirmed correct on inspection","Programme enrollment and savings among AI targeted customers versus a general mailing","Time from a flagged issue to resolution",[111,114],{"title":112,"detail":113},"A flood of technically correct but unprioritised flags","Millions of meters can produce more anomalies than any team can act on; rank by impact and route only the highest priority batches to a person. Con Edison's application produces a prioritised list of meters that require attention.",{"title":115,"detail":116},"Disaggregation that does not hold up across meter and appliance types","Appliance signatures vary by region, climate and equipment age; validate disaggregation accuracy on a local sample before using it to target a programme at scale.",{"euAiAct":118,"regulations":121,"guidance":125,"controls":126,"incidents":129},{"tier":119,"basis":120},"context-dependent","Annex III point 2 covers AI systems intended to be used as a safety component in the management and operation of critical digital infrastructure and the supply of water, gas, heating or electricity. Meter health prioritisation and usage disaggregation for programme targeting are not intended as safety components, so they stay outside that scope regardless of whether a person reviews the output. The tier would instead be high risk if the same kind of analytics were intended as a safety component in network operation or supply, for example directly controlling grid or metering protection systems; a human in the loop is then an Article 14 obligation for that high risk system, not a way to fall outside the category.",[122,123,124],"eu-ai-act","gdpr","nis2",[],[127,128],"A documented separation between advisory analytics and any system that can act on grid protection or metering infrastructure directly","Data minimisation and retention limits on the household level usage data used for disaggregation and targeting",[],{"howToBuild":131},"The meter data platform, the anomaly detection and the appliance disaggregation models are\nspecialist utility analytics products; Blits.ai is not where you build an AMI operations or\ndisaggregation model. What Blits.ai adds is the layer operations and programme teams use to act\non the output: an SQL knowledge base over the meter health and disaggregation results lets an\nagent answer a programme manager's question, such as which customers in a service area show a\nnew electric vehicle signature, in plain language instead of a query the analytics team has to\nrun by hand.\n\nAgentic tasks can watch for a condition, such as a batch of meters newly flagged as unhealthy in\na service area, and draft the field work order with human in the loop confirmation before a crew\nis dispatched. Agentic workflow run history and custom analytics dashboard widgets track flagged\nvolumes and resolution over time, and the platform's model agnostic routing and EU and UAE data\nresidency fit a utility that must keep customer usage data inside a required region.",[133,136,139,142],{"question":134,"answer":135},"What does AI actually do with smart meter data?","The two deployments on this page show different jobs on the same underlying data. Con Edison used C3 AI to monitor the health of its 5.3 million meter rollout end to end, from an individual meter up to the whole system, while Southern California Gas Company used Bidgely's platform to disaggregate usage from smart meter data for digital energy efficiency reports for its medium consumption gas customers, a programme that exceeded its savings goal.",{"question":137,"answer":138},"How much data is involved?","Con Edison's smart meter deployment was expected to generate between 100 terabytes and 1 petabyte of data a year. Separately, and only once, building the analytics platform itself meant aggregating two years of data from 13 source systems covering 5 million customer accounts.",{"question":140,"answer":141},"Does this replace smart meters themselves?","No. It is the analytics layer on top of an existing advanced metering infrastructure rollout; both deployments on this page assume the meters are already reporting interval data.",{"question":143,"answer":144},"Can it detect theft or non technical losses?","Detecting unusual consumption patterns is a well documented research area for smart meter analytics, but neither deployment on this page reports a theft or loss detection result with a checked figure, so treat that specific claim as unproven until you have your own evidence.",[],"2026-09-28",[148],{"date":146,"note":149},"First published","smart-meter-analytics",[152,182],{"title":153,"useCases":154,"organization":155,"vendors":160,"summary":164,"stage":165,"year":166,"channels":167,"languages":168,"metrics":170,"outcomeDisclosed":171,"sources":172,"verification":177,"grade":179,"id":180,"organizationSlug":181},"Southern California Gas Company: digital energy efficiency reports with Bidgely",[150],{"name":156,"anonymized":157,"country":158,"region":159,"industry":17},"Southern California Gas Company (SoCalGas)",false,"US","north-america",[161],{"name":162,"role":163},"Bidgely","platform","Southern California Gas Company (SoCalGas) worked with Bidgely on a digital only home energy report programme for medium consumption residential gas customers, a segment that traditional paper based home energy reports, which target high consumption customers, do not reach. The reports were built on AMI meter disaggregation, and the programme exceeded its savings goal, saving 565,000 therms by December 2020, with more than 405,000 customers receiving the reports digitally at a 50 percent open rate.","production",2020,[],[169],"en",[],true,[173],{"url":174,"title":175,"publisher":162,"archivedUrl":176},"https://www.bidgely.com/resources/southern-california-gas-company-case-study-with-bidgely/","SoCalGas Case Study: Delivering Energy Efficiency","https://web.archive.org/web/20250115180354/https://www.bidgely.com/resources/southern-california-gas-company-case-study-with-bidgely/",{"level":178,"checkedAt":146},"source-verified","C","socalgas-bidgely-energy-efficiency",null,{"title":183,"useCases":184,"organization":185,"vendors":187,"summary":190,"stage":165,"year":191,"channels":192,"languages":193,"metrics":194,"outcomeDisclosed":157,"sources":195,"verification":204,"grade":179,"id":205,"organizationSlug":181},"Con Edison: enterprise data analytics and AMI operations with C3 AI",[150],{"name":186,"anonymized":157,"country":158,"region":159,"industry":17},"Consolidated Edison (Con Edison)",[188],{"name":189,"role":163},"C3 AI","Con Edison built an enterprise data analytics platform on C3 AI to run Advanced Metering Infrastructure operations for its 5.3 million meter smart meter rollout, aggregating two years of data from 13 source systems covering 5 million customer accounts and integrating over 180 billion rows of data a year across those systems. Two machine learning algorithms and 50 analytics identify deployment and installation issues and determine meter and network health, giving the utility a real time, prioritised view from an individual meter up to the whole system, in a 10 month project.",2019,[25],[169],[],[196,200],{"url":197,"title":198,"publisher":189,"archivedUrl":199},"https://c3.ai/customers/conedison/","ConEdison","https://web.archive.org/web/20190819211733/https://c3.ai/customers/conedison/",{"url":201,"title":202,"publisher":203},"https://www.sec.gov/Archives/edgar/data/1577526/000162828024028786/ai-20240430.htm","C3.ai, Inc. Form 10-K, fiscal year ended April 30, 2024","C3.ai, Inc. (U.S. Securities and Exchange Commission, EDGAR)",{"level":178,"checkedAt":146},"con-edison-c3-ai-smart-meter-analytics",0,[],{"low":209,"high":210},600000,10000000,[212,237,255,276],{"slug":213,"title":214,"shortTitle":215,"definition":216,"status":9,"industries":217,"functions":219,"patterns":220,"audience":222,"autonomy":28,"adoptionStage":29,"segment":223,"evidenceCount":224,"publicEvidenceCount":224,"organizations":225,"bestGrade":228,"headline":229,"lastVerified":146,"indexable":171},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI powered operations center that predicts patient admissions, discharges and transfers across a hospital or health system, and helps a team of coordinators sitting in one room sequence real time bed assignments, staffing levels and patient moves, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[218],"healthcare",[19,20],[23,221,22],"classification-and-routing","employee-facing","hospital operations",2,[226,227],"Humber River Health","Johns Hopkins Medicine","B",{"kpi":230,"label":231,"unit":232,"n":224,"nUpTo":206,"kind":233,"value":234,"qualifier":235,"claimant":236,"organization":227,"vendorReported":157},"processing-time-reduction","Cycle time reduction","percent","reported",38,"exact","organization",{"slug":238,"title":239,"shortTitle":240,"definition":241,"status":9,"industries":242,"functions":244,"patterns":245,"audience":27,"autonomy":246,"adoptionStage":247,"evidenceCount":248,"publicEvidenceCount":248,"organizations":249,"bestGrade":228,"headline":181,"lastVerified":254,"indexable":171},"retail-demand-forecasting-and-replenishment","AI demand forecasting and automated replenishment for retail","Demand forecasting and replenishment","Machine learning that forecasts demand for every item in every store or fulfillment center, day by day, from sales history, promotions, prices, weather and local events, and turns the forecast into automatic store and warehouse orders within limits set by planners, who handle the exceptions.",[243],"retail-and-ecommerce",[19,20],[23,22],"supervised-agent","mainstream",4,[250,251,252,253],"Albert Heijn","Morrisons","One Stop","Walmart","2026-09-27",{"slug":256,"title":257,"shortTitle":258,"definition":259,"status":9,"industries":260,"functions":263,"patterns":265,"audience":27,"autonomy":268,"adoptionStage":269,"segment":270,"evidenceCount":248,"publicEvidenceCount":271,"organizations":272,"bestGrade":228,"headline":181,"lastVerified":254,"indexable":171},"portfolio-drift-monitoring-and-rebalancing","AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[261,262],"wealth-and-asset-management","banking",[19,264,20],"risk-management",[22,266,23,267],"agentic-workflow","content-generation","copilot","emerging","middle-office",3,[273,274,275],"Morgan Stanley","SimCorp","Vanguard",{"slug":277,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":283,"patterns":284,"audience":222,"autonomy":268,"adoptionStage":29,"segment":285,"evidenceCount":224,"publicEvidenceCount":224,"organizations":286,"bestGrade":228,"headline":289,"lastVerified":146,"indexable":171},"ai-drug-discovery-platform","AI native platform for drug target discovery and molecule design","AI drug discovery platform","An AI native research platform that prioritizes disease targets from biological data, generates and optimizes candidate drug molecules computationally, and predicts their properties before a chemist synthesizes and tests them, so a pharmaceutical or biotech company reaches a validated preclinical candidate with far fewer molecules made and tested than a conventional medicinal chemistry program.",[282],"pharma-and-life-sciences",[19,20],[23,267],"drug discovery",[287,288],"Insilico Medicine","Recursion Pharmaceuticals",{"kpi":230,"label":231,"unit":232,"n":224,"nUpTo":206,"kind":233,"value":290,"qualifier":291,"claimant":236,"organization":287,"vendorReported":157},60,"approximately",{"indexable":171,"reasons":293},[],[295,302,307,315,322,328,335,342,350,357,364,370,377,384,390,394,401,407,413,419,425,431,437,442,447,454,461,466,472,480,486,492,498,503],{"id":122,"label":296,"issuer":297,"region":298,"url":299,"description":300,"useCases":301,"indexable":171},"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.",197,{"id":123,"label":303,"issuer":297,"region":298,"url":304,"description":305,"useCases":306,"indexable":171},"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":308,"label":309,"issuer":310,"region":311,"url":312,"description":313,"useCases":314,"indexable":171},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":316,"label":317,"issuer":318,"region":159,"url":319,"description":320,"useCases":321,"indexable":171},"nist-ai-rmf","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.",83,{"id":323,"label":324,"issuer":297,"region":298,"url":325,"description":326,"useCases":327,"indexable":171},"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":329,"label":330,"issuer":331,"region":298,"url":332,"description":333,"useCases":334,"indexable":171},"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":336,"label":337,"issuer":338,"region":298,"url":339,"description":340,"useCases":341,"indexable":171},"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":343,"label":344,"issuer":345,"region":346,"url":347,"description":348,"useCases":349,"indexable":171},"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":351,"label":352,"issuer":353,"region":346,"url":354,"description":355,"useCases":356,"indexable":171},"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":358,"label":359,"issuer":360,"region":311,"url":361,"description":362,"useCases":363,"indexable":171},"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":365,"label":366,"issuer":367,"region":159,"url":368,"description":369,"useCases":363,"indexable":171},"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":371,"label":372,"issuer":373,"region":298,"url":374,"description":375,"useCases":376,"indexable":171},"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":378,"label":379,"issuer":380,"region":311,"url":381,"description":382,"useCases":383,"indexable":171},"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":385,"label":386,"issuer":297,"region":298,"url":387,"description":388,"useCases":389,"indexable":171},"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":124,"label":391,"issuer":297,"region":298,"url":392,"description":393,"useCases":389,"indexable":171},"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":395,"label":396,"issuer":397,"region":159,"url":398,"description":399,"useCases":400,"indexable":171},"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":402,"label":403,"issuer":297,"region":298,"url":404,"description":405,"useCases":406,"indexable":171},"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":408,"label":409,"issuer":410,"region":159,"url":411,"description":412,"useCases":406,"indexable":171},"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":414,"label":415,"issuer":416,"region":311,"url":417,"description":418,"useCases":406,"indexable":171},"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":420,"label":421,"issuer":297,"region":298,"url":422,"description":423,"useCases":424,"indexable":171},"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":426,"label":427,"issuer":428,"region":159,"url":429,"description":430,"useCases":424,"indexable":171},"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":432,"label":433,"issuer":345,"region":346,"url":434,"description":435,"useCases":436,"indexable":171},"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":438,"label":439,"issuer":297,"region":298,"url":440,"description":441,"useCases":436,"indexable":171},"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":443,"label":444,"issuer":297,"region":298,"url":445,"description":446,"useCases":436,"indexable":171},"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":448,"label":449,"issuer":450,"region":298,"url":451,"description":452,"useCases":453,"indexable":171},"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":455,"label":456,"issuer":457,"region":159,"url":458,"description":459,"useCases":460,"indexable":171},"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":462,"label":463,"issuer":297,"region":298,"url":464,"description":465,"useCases":460,"indexable":171},"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":467,"label":468,"issuer":297,"region":298,"url":469,"description":470,"useCases":471,"indexable":171},"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.",6,{"id":473,"label":474,"issuer":475,"region":476,"url":477,"description":478,"useCases":479,"indexable":171},"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":481,"label":482,"issuer":483,"region":298,"url":484,"description":485,"useCases":248,"indexable":171},"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.",{"id":487,"label":488,"issuer":489,"region":298,"url":490,"description":491,"useCases":248,"indexable":171},"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":493,"label":494,"issuer":495,"region":346,"url":496,"description":497,"useCases":271,"indexable":171},"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":499,"label":500,"issuer":297,"region":298,"url":501,"description":502,"useCases":271,"indexable":171},"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":504,"label":505,"issuer":506,"region":159,"url":507,"description":508,"useCases":271,"indexable":171},"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.",1790598296246]