[{"data":1,"prerenderedAt":518},["ShallowReactive",2],{"uc-renewable-generation-forecasting":3,"uc-regulations":290},{"useCase":4,"evidence":139,"blitsAiDeployments":206,"benchmarks":207,"indicative":214,"related":217,"indexability":288,"includeUnpublished":145},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":23,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":36,"indicativeValue":39,"macroEstimates":61,"feasibility":62,"implementation":75,"risk":108,"blitsAi":121,"faq":123,"related":133,"datePublished":134,"dateModified":134,"lastVerified":134,"changelog":135,"slug":138},"AI forecasting of wind and solar generation for grid balancing and trading","Renewable generation forecasting","AI wind and solar generation forecasting","Google reports roughly 20 percent more value from its wind fleet; National Grid ESO reports a solar forecast 33 percent more accurate.","published","AI that predicts how much power a wind or solar asset will generate over the next hours to a day ahead, combining weather forecasts with historical turbine or panel output, so a grid operator or generator can commit to a delivery schedule instead of treating renewable output as unplannable.",[12,13,14,15],"wind power forecasting","solar generation forecasting","renewable output forecasting","day ahead renewable forecasting",[17],"energy-and-utilities",[19,20],"operations","analytics-and-reporting",[22],"prediction-and-scoring",[24,25],"internal-tools","api","back-office","assist","early-adopters","generation-and-trading","Wind and solar generation is hard to predict, and National Grid Electricity System Operator (the\noperator of the Great Britain grid) says this is because it is \"weather dependent and connected\nat a local rather than national level\". Google's own account of its wind fleet describes a\nrelated problem from the generator's side: wind's variable nature makes it an unpredictable\nsource of electricity, less useful than one that can reliably deliver power at a set time, so a\ngenerator that cannot commit to a schedule in advance captures less value from the same megawatt\nhours.",[],"1. **Train on weather and output history.** A model learns the relationship between weather\n   variables, such as wind speed and solar irradiance, and the asset's actual historical output.\n2. **Forecast output ahead of delivery.** The model runs against an updated weather forecast to\n   predict output over the commit horizon; Google's wind model, for example, forecasts output 36\n   hours ahead of actual generation.\n3. **Turn the forecast into a commitment or a balancing input.** For a generator, the forecast\n   feeds a recommendation for the delivery commitments to make to the grid; for a system\n   operator, it feeds the wider forecast used to balance supply and demand in real time.\n4. **Submit and monitor.** A trader, scheduler or the system operator's forecasting team reviews\n   and submits the resulting plan, then compares delivered output against both the forecast and\n   the commitment through the day.\n5. **Retrain on the error.** Forecast error by weather regime and season feeds back into the\n   model so it keeps improving as more storms, calm periods and seasons are observed.",[34,35],"revenue-growth","risk-reduction",[37,38],"forecast-accuracy","revenue-uplift",{"referenceOrg":40,"inputs":41,"formula":56,"currency":57,"period":58,"resultLabel":59,"caveat":60},"A 500 megawatt wind portfolio selling into a day ahead power market",[42,49],{"key":43,"label":44,"low":45,"high":46,"unit":47,"note":48},"annualRevenue","Annual wholesale revenue from the portfolio without time based commitments",30000000,80000000,"USD per year","Editorial assumption based on typical wind capture prices and load factors; replace with your own portfolio revenue.",{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"valueUplift","Share of revenue gained from forecasting and time based commitments",0.05,0.2,"fraction of revenue","The high end matches Google's own reported figure of roughly 20% more value from its wind fleet; the low end is an editorial assumption for a portfolio with a less mature forecasting and trading setup.","annualRevenue * valueUplift","USD","per year","Additional value captured from forecasting driven delivery commitments","Assumes the portfolio can act on the forecast with time based commitments, which needs a market that rewards scheduled delivery; it leaves out the cost of the weather data, the forecasting model and the trading desk time, and neither deployment on this page reports the cost side of that trade.",[],{"complexity":63,"complexityNote":64,"dataPrerequisites":65,"integrations":70},"medium","The forecasting model itself is well understood machine learning; the harder part is wiring it into the market and grid systems that turn a forecast into an actual commitment or balancing action, and getting enough clean historical output data per asset to train on.",[66,67,68,69],"Historical output per asset at a fine enough time resolution to match the commit horizon","Weather forecasts covering wind speed, solar irradiance and related variables at the asset's location","Asset metadata, such as turbine or panel specifications and site layout","Market rules for how a commitment or bid is scored and penalised",[71,72,73,74],"Weather data provider feed","Energy trading and risk management system, or the equivalent balancing system for a system operator","Asset monitoring or SCADA system for actual output","Market bidding or nomination platform",{"steps":76,"guardrails":92,"humanInTheLoop":96,"kpisToInstrument":97,"failureModes":101},[77,80,83,86,89],{"title":78,"detail":79},"Start on one asset class and one horizon","Google applied its model to 700 megawatts of wind capacity in the central United States; pick one asset class and one forecast horizon rather than every asset and every horizon at once.",{"title":81,"detail":82},"Build the forecast, not the trading decision, first","Get the output forecast accurate and measured against actuals before automating what the forecast changes about a commitment or a bid.",{"title":84,"detail":85},"Connect the forecast to a real commitment or balancing action","A forecast that never changes what gets submitted or scheduled produces no value regardless of its accuracy; wire its output into the actual commitment, bid or balancing decision, not just a dashboard that sits next to it.",{"title":87,"detail":88},"Compare against a clear baseline","Measure value against a stated baseline, such as flat or average delivery with no time based commitments, the same baseline Google used to report its own result.",{"title":90,"detail":91},"Keep a person on the commitment for as long as the stakes justify it","Early on, a trader or scheduler should review the recommended commitment before it is submitted, moving to closer to automatic submission only once forecast error is well understood by season and weather regime.",[93,94,95],"A person or a risk system checks a recommended commitment against exposure and penalty limits before it is submitted","Forecast confidence, not just a single point prediction, is shown so a scheduler can see how much to trust a specific window","Model changes are back tested against historical weather and output data before going live","Traders and schedulers review recommended delivery commitments before they are submitted, and a system operator's forecasting team checks the model's output against its own judgment during unusual weather before it feeds the live balancing process.",[98,99,100],"Forecast error against actual output, by asset, season and horizon","Value captured from time based commitments against a flat delivery baseline","Imbalance penalties or curtailment avoided per period",[102,105],{"title":103,"detail":104},"A weather regime the model has not seen","A model trained mostly on ordinary conditions can misjudge an unusual storm, heat wave or calm spell; monitor forecast error by weather regime and retrain as new extremes occur.",{"title":106,"detail":107},"Treating a point forecast as certain","A single predicted number hides real uncertainty; publish a confidence range and size commitments to that range rather than to the midpoint alone.",{"euAiAct":109,"regulations":112,"guidance":116,"controls":117,"incidents":120},{"tier":110,"basis":111},"minimal","A model that forecasts generation output to inform a trading commitment or a system operator's balancing input is not intended as a safety component in the management and operation of electricity supply under Annex III point 2; it informs a commercial or planning decision that a trader, scheduler or system operator makes, rather than directly protecting the physical integrity of the grid. It carries no specific obligation beyond the Article 4 AI literacy duty, though a system operator's own internal risk policies may still require testing and review before a forecast changes a live balancing action.",[113,114,115],"eu-ai-act","nist-ai-rmf","iso-42001",[],[118,119],"Documented intended purpose limiting the model to forecasting, with the commitment or balancing decision kept with a person or a separately governed system","Regular comparison of forecast against actual output, reviewed by the trading or forecasting team, not only by the model's own developers",[],{"howToBuild":122},"The weather and generation forecasting model itself is a specialist time series problem; Blits.ai\nis not where you build that model. What Blits.ai adds is the layer traders and schedulers use to\nact on its output: an **AI agent** with a **SQL knowledge base** over the forecast, the\nresulting commitment recommendations and actual delivered output lets a trader ask, in plain\nlanguage, how a specific asset's forecast has moved or how accurate last week's commitments\nwere, instead of writing a query by hand.\n\nAn **agentic task** can watch for a condition, such as a forecast that moves sharply close to\nthe commit deadline, and draft a note or an updated commitment for a trader to confirm through\n**human in the loop** approval rather than submitting anything on its own. **Custom analytics\ndashboard widgets** track forecast error and captured value over time, the platform is\n**model agnostic**, and **EU and UAE data residency** fit an operator or trading desk that must\nkeep market data inside a required region.",[124,127,130],{"question":125,"answer":126},"Does the AI decide how much power to commit to the grid?","The sources on this page do not say who signs off on the resulting commitment. Google says its model recommends optimal hourly delivery commitments; it does not say who approves them. National Grid ESO's page does not describe an approval step at all. As a matter of playbook advice rather than a reported fact, keep a person reviewing the recommended commitment until forecast error is well understood by season and weather regime.",{"question":128,"answer":129},"How far ahead do these forecasts look?","Google's wind forecasting system predicts output 36 hours ahead of actual generation to support delivery commitments made a full day in advance. National Grid ESO's own page about its solar forecasting improvement does not state what horizon the forecast covers.",{"question":131,"answer":132},"Does better forecasting reduce the need for conventional backup generation?","Neither deployment on this page reports a reduction in reserve or backup generation as a measured figure, so treat that as a plausible but unproven benefit rather than a checked result.",[],"2026-09-29",[136],{"date":134,"note":137},"First published","renewable-generation-forecasting",[140,178],{"title":141,"useCases":142,"organization":143,"vendors":149,"summary":153,"stage":154,"year":155,"channels":156,"languages":157,"metrics":159,"outcomeDisclosed":168,"sources":169,"verification":173,"grade":175,"id":176,"organizationSlug":177},"Google: DeepMind wind farm output forecasting for grid delivery commitments",[138],{"name":144,"anonymized":145,"country":146,"region":147,"industry":148},"Google",false,"US","north-america","technology",[150],{"name":151,"role":152},"Google DeepMind","in-house","Google and DeepMind applied a neural network, trained on historical turbine data and widely available weather forecasts, to 700 megawatts of wind power capacity across a group of wind farms in the central United States. The model predicts wind power output 36 hours ahead of actual generation, and recommends the optimal hourly delivery commitments to make to the power grid a full day in advance, so the wind fleet can be scheduled like a conventional generator instead of treated as unplannable.","production",2019,[24],[158],"en",[160],{"kpi":38,"value":161,"unit":162,"qualifier":163,"period":164,"claimant":165,"quote":166,"sourceUrl":167},20,"percent","approximately","to date, as reported February 2019","organization","To date, machine learning has boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to the grid.","https://blog.google/innovation-and-ai/products/machine-learning-can-boost-value-wind-energy/",true,[170],{"url":167,"title":171,"publisher":144,"date":172},"Machine learning can boost the value of wind energy","2019-02-26",{"level":174,"checkedAt":134},"source-verified","B","google-wind-farm-generation-forecasting","google",{"title":179,"useCases":180,"organization":181,"vendors":185,"summary":189,"stage":190,"year":155,"channels":191,"languages":192,"metrics":193,"outcomeDisclosed":168,"sources":194,"verification":203,"grade":175,"id":204,"organizationSlug":205},"National Grid ESO: machine learning solar forecasting with The Alan Turing Institute",[138],{"name":182,"anonymized":145,"country":183,"region":184,"industry":17},"National Grid Electricity System Operator","GB","europe",[186],{"name":187,"role":188},"The Alan Turing Institute","integrator","National Grid Electricity System Operator, the system operator for the Great Britain electricity grid, worked with The Alan Turing Institute to replace a simple two variable solar forecast with a random forest model trained on around 80 weather and irradiance variables, combined with other machine learning methods into a multi model ensemble. The project, funded through Ofgem's Network Innovation Allowance, produced a solar forecasting system the operator's own page describes as 33% more accurate; the page does not state which forecast horizon this covers.","pilot",[24],[158],[],[195,199],{"url":196,"title":197,"publisher":198},"https://www.neso.energy/news/eso-and-alan-turing-institute-use-machine-learning-help-balance-gb-electricity-grid","ESO and The Alan Turing Institute use machine learning to help balance the GB electricity grid","National Energy System Operator",{"url":200,"title":201,"publisher":202},"https://theenergyst.com/national-grid-and-alan-turing-institute-improve-solar-forecasting/","National Grid and Alan Turing Institute improve solar forecasting","The Energyst",{"level":174,"checkedAt":134},"national-grid-eso-solar-forecast-accuracy",null,0,[208],{"kpi":38,"label":209,"unit":162,"aggregate":168,"higherIsBetter":168,"n":210,"nUpTo":206,"median":161,"min":161,"max":161,"byClaimant":211,"vendorOnly":145,"points":212},"Revenue uplift",1,{"organization":210,"vendor":206,"regulator":206,"independent":206},[213],{"evidenceId":176,"organization":144,"value":161,"qualifier":163,"claimant":165,"grade":175,"pooled":168},{"low":215,"high":216},1500000,16000000,[218,234,255,273],{"slug":219,"title":220,"shortTitle":221,"definition":222,"status":9,"industries":223,"functions":224,"patterns":225,"audience":26,"autonomy":27,"adoptionStage":28,"segment":227,"evidenceCount":228,"publicEvidenceCount":228,"organizations":229,"bestGrade":232,"headline":205,"lastVerified":233,"indexable":168},"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.",[17],[19,20],[226,22],"anomaly-detection","metering-and-billing",2,[230,231],"Consolidated Edison (Con Edison)","Southern California Gas Company (SoCalGas)","C","2026-09-28",{"slug":235,"title":236,"shortTitle":237,"definition":238,"status":9,"industries":239,"functions":241,"patterns":242,"audience":244,"autonomy":27,"adoptionStage":28,"segment":245,"evidenceCount":228,"publicEvidenceCount":228,"organizations":246,"bestGrade":175,"headline":249,"lastVerified":233,"indexable":168},"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.",[240],"healthcare",[19,20],[22,243,226],"classification-and-routing","employee-facing","hospital operations",[247,248],"Humber River Health","Johns Hopkins Medicine",{"kpi":250,"label":251,"unit":162,"n":228,"nUpTo":206,"kind":252,"value":253,"qualifier":254,"claimant":165,"organization":248,"vendorReported":145},"processing-time-reduction","Cycle time reduction","reported",38,"exact",{"slug":256,"title":257,"shortTitle":258,"definition":259,"status":9,"industries":260,"functions":262,"patterns":263,"audience":26,"autonomy":264,"adoptionStage":265,"evidenceCount":266,"publicEvidenceCount":266,"organizations":267,"bestGrade":175,"headline":205,"lastVerified":272,"indexable":168},"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.",[261],"retail-and-ecommerce",[19,20],[22,226],"supervised-agent","mainstream",4,[268,269,270,271],"Albert Heijn","Morrisons","One Stop","Walmart","2026-09-27",{"slug":274,"title":275,"shortTitle":276,"definition":277,"status":9,"industries":278,"functions":280,"patterns":281,"audience":244,"autonomy":27,"adoptionStage":283,"segment":284,"evidenceCount":228,"publicEvidenceCount":228,"organizations":285,"bestGrade":175,"headline":205,"lastVerified":134,"indexable":168},"clinical-trial-site-selection-and-feasibility","AI for clinical trial site selection and feasibility","Clinical trial site selection","AI that scores and ranks candidate investigator sites and countries for a planned clinical trial by predicted enrollment speed, access to the eligible patient population and historical performance, so clinical operations teams choose and activate a shortlist of sites with a higher chance of meeting enrollment targets on time, instead of relying on which sites a study team happens to know.",[279],"pharma-and-life-sciences",[19,20],[22,282],"recommendation-and-personalization","emerging","clinical development",[286,287],"Amgen","Novartis",{"indexable":168,"reasons":289},[],[291,297,303,310,316,323,329,336,344,351,358,365,371,377,384,391,397,404,410,416,422,429,434,441,446,451,456,462,469,475,483,489,495,502,507,512],{"id":113,"label":292,"issuer":293,"region":184,"url":294,"description":295,"useCases":296,"indexable":168},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",230,{"id":298,"label":299,"issuer":293,"region":184,"url":300,"description":301,"useCases":302,"indexable":168},"gdpr","GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",207,{"id":115,"label":304,"issuer":305,"region":306,"url":307,"description":308,"useCases":309,"indexable":168},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":114,"label":311,"issuer":312,"region":147,"url":313,"description":314,"useCases":315,"indexable":168},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",92,{"id":317,"label":318,"issuer":319,"region":184,"url":320,"description":321,"useCases":322,"indexable":168},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":324,"label":325,"issuer":293,"region":184,"url":326,"description":327,"useCases":328,"indexable":168},"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":330,"label":331,"issuer":332,"region":184,"url":333,"description":334,"useCases":335,"indexable":168},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":337,"label":338,"issuer":339,"region":340,"url":341,"description":342,"useCases":343,"indexable":168},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":345,"label":346,"issuer":347,"region":340,"url":348,"description":349,"useCases":350,"indexable":168},"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":352,"label":353,"issuer":354,"region":147,"url":355,"description":356,"useCases":357,"indexable":168},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",22,{"id":359,"label":360,"issuer":361,"region":306,"url":362,"description":363,"useCases":364,"indexable":168},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":366,"label":367,"issuer":293,"region":184,"url":368,"description":369,"useCases":370,"indexable":168},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":372,"label":373,"issuer":374,"region":184,"url":375,"description":376,"useCases":370,"indexable":168},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":378,"label":379,"issuer":380,"region":147,"url":381,"description":382,"useCases":383,"indexable":168},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":385,"label":386,"issuer":387,"region":306,"url":388,"description":389,"useCases":390,"indexable":168},"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":392,"label":393,"issuer":293,"region":184,"url":394,"description":395,"useCases":396,"indexable":168},"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":398,"label":399,"issuer":400,"region":147,"url":401,"description":402,"useCases":403,"indexable":168},"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":405,"label":406,"issuer":407,"region":147,"url":408,"description":409,"useCases":403,"indexable":168},"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":411,"label":412,"issuer":293,"region":184,"url":413,"description":414,"useCases":415,"indexable":168},"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":417,"label":418,"issuer":419,"region":306,"url":420,"description":421,"useCases":415,"indexable":168},"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":423,"label":424,"issuer":425,"region":147,"url":426,"description":427,"useCases":428,"indexable":168},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":430,"label":431,"issuer":293,"region":184,"url":432,"description":433,"useCases":428,"indexable":168},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":435,"label":436,"issuer":437,"region":184,"url":438,"description":439,"useCases":440,"indexable":168},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",10,{"id":442,"label":443,"issuer":339,"region":340,"url":444,"description":445,"useCases":440,"indexable":168},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":447,"label":448,"issuer":293,"region":184,"url":449,"description":450,"useCases":440,"indexable":168},"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":452,"label":453,"issuer":293,"region":184,"url":454,"description":455,"useCases":440,"indexable":168},"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":457,"label":458,"issuer":293,"region":184,"url":459,"description":460,"useCases":461,"indexable":168},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",9,{"id":463,"label":464,"issuer":465,"region":147,"url":466,"description":467,"useCases":468,"indexable":168},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":470,"label":471,"issuer":293,"region":184,"url":472,"description":473,"useCases":474,"indexable":168},"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":476,"label":477,"issuer":478,"region":479,"url":480,"description":481,"useCases":482,"indexable":168},"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":484,"label":485,"issuer":486,"region":184,"url":487,"description":488,"useCases":266,"indexable":168},"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":490,"label":491,"issuer":492,"region":184,"url":493,"description":494,"useCases":266,"indexable":168},"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":496,"label":497,"issuer":498,"region":340,"url":499,"description":500,"useCases":501,"indexable":168},"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":503,"label":504,"issuer":293,"region":184,"url":505,"description":506,"useCases":501,"indexable":168},"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":508,"label":509,"issuer":293,"region":184,"url":510,"description":511,"useCases":501,"indexable":168},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":513,"label":514,"issuer":515,"region":147,"url":516,"description":517,"useCases":501,"indexable":168},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683491934]