[{"data":1,"prerenderedAt":604},["ShallowReactive",2],{"uc-ran-energy-optimization":3,"uc-regulations":394},{"useCase":4,"evidence":188,"blitsAiDeployments":307,"benchmarks":308,"indicative":315,"related":318,"indexability":392,"includeUnpublished":194},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":20,"channels":22,"audience":25,"autonomy":26,"adoptionStage":27,"segment":28,"problem":29,"problemStats":30,"howItWorks":41,"valueDrivers":42,"kpis":44,"indicativeValue":48,"macroEstimates":82,"feasibility":83,"implementation":96,"risk":137,"blitsAi":167,"faq":169,"related":179,"datePublished":183,"dateModified":183,"lastVerified":183,"changelog":184,"slug":187},"AI for radio access network energy optimization","RAN energy optimization","AI for RAN energy optimization and cell sleep","AI puts idle radio carriers to sleep in quiet hours. Telefónica saved up to 8% of a 5G test site's energy, and BT Group runs cell sleep on over 19,500 EE sites.","published","Machine learning that predicts traffic per cell and puts radio carriers, cells and hardware components into sleep modes when demand is low, then wakes them before users notice, so a mobile network uses less electricity without losing coverage or quality.",[12,13,14,15],"AI energy saving for mobile networks","cell sleep optimization","RAN power saving","green RAN AI",[17],"telecommunications",[19],"network-operations",[21],"prediction-and-scoring",[23,24],"api","internal-tools","back-office","autonomous","early-adopters","network","For operators such as BT Group and Orange, the network uses most of the energy. BT Group says its\nnetworks account for around 89 per cent of its total energy consumption, and Orange puts IT and\nnetworks at around 85% of its energy requirements. In a mobile network, part of that power keeps radio capacity switched\non in periods when it is not needed, such as quiet nights: this is the capacity that cell sleep\nfeatures switch off.\n\nVendors ship power saving features (carrier shutdown, micro sleep, deep sleep), but switching them\non with fixed schedules leaves savings on the table in quiet cells and risks quality in busy ones.\nThe settings differ per cell, traffic patterns change with events, holidays and new sites, and\nnobody can tune tens of thousands of cells by hand. Energy prices and net zero targets make it a\ncost and climate priority: Orange stepped up its energy saving measures during the 2022 energy\ncrisis, and BT Group calls network energy efficiency integral to its net zero ambition.",[31,36],{"statement":32,"sourceTitle":33,"sourceUrl":34,"year":35},"BT Group states that its networks account for around 89 per cent of its total energy consumption.","BT Group rolls-out energy-saving ‘cell sleep’ technology to EE mobile sites nationwide","https://newsroom.bt.com/bt-group-rolls-out-energy-saving-cell-sleep-technology-to-ee-mobile-sites-nationwide/",2024,{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"Orange states that IT and networks represent around 85% of the group's energy requirements.","Orange steps up efforts to reduce energy consumption across Europe","https://newsroom.orange.com/orange-steps-up-efforts-to-reduce-energy-consumption-across-europe/",2022,"1. **Learn each cell's rhythm.** Models forecast traffic per cell and carrier from history,\n   calendar effects and local events.\n2. **Choose the saving action.** For each forecast quiet period the system picks the deepest\n   power saving mode that the cell can use safely: switching off capacity carriers, micro sleep,\n   deep sleep of radio units or shutdown of idle components.\n3. **Protect quality.** Coverage layers stay on, neighbouring cells absorb the remaining traffic,\n   and the system watches live load so sleeping capacity wakes within seconds if demand rises.\n4. **Tune per cell.** Thresholds are adjusted per cell from measured quality and savings, rather\n   than one setting for the whole network.\n5. **Report.** Energy saved, quality indicators and wake up events are reported per site and\n   cluster, so engineers can see where savings cost quality and adjust.",[43],"cost-to-serve",[45,46,47],"energy-savings","cost-reduction","cost-savings",{"referenceOrg":49,"inputs":50,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A mobile operator with 10,000 radio sites",[51,56,63,70],{"key":52,"label":53,"low":54,"high":54,"unit":52,"note":55},"sites","Radio sites in scope",10000,"The reference operator.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"kwhPerSite","Electricity use per radio site per year",30000,50000,"kWh per site per year","Editorial assumption for a multi band macro site, including radio, power and cooling equipment. Replace with metered data.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"savingShare","Share of site electricity saved by AI driven sleep modes",0.005,0.024,"fraction of site energy","BT Group expects 4.5m kWh a year across EE's more than 19,500 sites, about 0.5 to 0.8% of the assumed site consumption estate wide, with a per site ceiling of up to 2 kWh a day (about 730 kWh a year, 1.5 to 2.4% of the assumed site consumption). The range spans that estate wide expectation up to the per site ceiling. Telefónica reports savings of up to 8% of a 5G site's 24 hour consumption, but in a single site test, and Nokia expects planned network energy cost savings of 8 to 10% for Safaricom; both are above this range and are not used to set it.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"pricePerKwh","Electricity price",0.1,0.25,"USD per kWh","Editorial assumption. Replace with your own contracted price.","sites * kwhPerSite * savingShare * pricePerKwh","USD","per year","Radio network electricity cost avoided","Site electricity cost only. It leaves out carbon value, longer battery backup during grid outages, software licence and integration costs, and any quality impact that has to be compensated.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":91},"medium","The power saving features usually exist in the radio equipment already. The work is in reliable per cell traffic data, safe automation across vendors and convincing radio engineers that quality will hold.",[87,88,89,90],"Traffic and quality counters per cell and carrier at 15 minute or finer granularity","Site energy metering, ideally per site rather than estimated","Configuration and capability data for the power saving features per vendor and software release","Calendar of events and planned works",[92,93,94,95],"Radio network management and configuration systems per vendor","Performance management and network data platform","Energy metering and site management systems","Self organizing network (SON) platform where one exists",{"steps":97,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":124},[98,101,104,107,110],{"title":99,"detail":100},"Measure the baseline","Meter energy per site and record quality indicators before any change, so savings and quality impact can be proven rather than estimated.",{"title":102,"detail":103},"Switch on vendor features with guardrails","Activate the available sleep features with conservative thresholds in a cluster, and compare with a control cluster.",{"title":105,"detail":106},"Add prediction and per cell tuning","Replace fixed schedules with traffic forecasts and per cell thresholds, and let the system tune them from measured quality.",{"title":108,"detail":109},"Scale by cluster, not by country","Roll out region by region with quality checks at each step, and keep special sites (hospitals, stadiums, transport hubs) under manual rules.",{"title":111,"detail":112},"Report savings finance can trust","Agree the measurement method with finance and sustainability teams up front, so the savings count in budgets and emissions reporting.",[114,115,116,117],"Coverage layers and emergency service capability are never switched off","Automatic wake up on load thresholds, with a maximum wake up time per mode","Exclusion lists for critical sites and events, maintained by radio engineering","Quality key performance indicators monitored per cell, with automatic rollback when they degrade","Radio engineers set the guardrails, exclusion lists and quality thresholds, and review weekly reports of savings against quality per cluster. The system acts on its own within those limits, because sleep and wake decisions across thousands of cells happen too often to approve one by one.",[120,121,122,123],"Energy saved per site and per cluster against a metered baseline or control group","Accessibility, retainability and throughput per cell during sleep periods","Number and duration of wake up events","Customer complaints about coverage in optimized areas",[125,128,131,134],{"title":126,"detail":127},"Savings that exist only in the model","Savings are estimated from switch off time rather than metered. Use metered energy and control clusters.",{"title":129,"detail":130},"Quality loss at the edges","Neighbouring cells cannot absorb the traffic and users at the cell edge lose throughput. Monitor edge quality, not just averages.",{"title":132,"detail":133},"Events the forecast did not know","A match, a concert or an emergency brings traffic to a sleeping area. Feed event calendars and keep fast wake up paths.",{"title":135,"detail":136},"Vendor lock in of the optimizer","A rollout can end up covering only one vendor's part of the network (O2 Telefónica Germany uses Nokia's software on the Nokia part of its radio network, and Indosat Ooredoo Hutchison's rollout covers its Nokia radio footprint in four regions). Check multi vendor support and plan for a view across vendors where networks are mixed.",{"euAiAct":138,"regulations":141,"guidance":146,"controls":161,"incidents":166},{"tier":139,"basis":140},"context-dependent","Optimizing energy use is normally minimal risk. Under Article 6(2), Annex III point 2 lists AI systems intended as safety components in the management and operation of critical digital infrastructure as high risk, and public electronic communications networks fall under that infrastructure. Recital 55 limits safety components to systems that directly protect the infrastructure or the health and safety of persons, so an optimizer is not high risk by default, but a design in which it could affect emergency service availability should be assessed against point 2.",[142,143,144,145],"eu-ai-act","nis2","nist-ai-rmf","iso-42001",[147,153,157],{"title":148,"issuer":149,"region":150,"url":151,"note":152},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 2 covers AI systems intended as safety components in the management and operation of critical digital infrastructure.",{"title":154,"issuer":149,"region":150,"url":155,"note":156},"Recital 55, safety components of critical infrastructure","https://artificialintelligenceact.eu/recital/55/","Defines safety components as systems that directly protect the physical integrity of critical infrastructure or the health and safety of persons and property, and refers to the digital infrastructure listed in point 8 of the Annex to Directive (EU) 2022/2557.",{"title":158,"issuer":149,"region":150,"url":159,"note":160},"Directive (EU) 2022/2557 on the resilience of critical entities","https://eur-lex.europa.eu/eli/dir/2022/2557/oj","Point 8 of the Annex (digital infrastructure) lists providers of public electronic communications networks, which is why mobile radio networks count as critical digital infrastructure for Annex III point 2.",[162,163,164,165],"Documented guardrails and exclusion lists with an accountable radio engineering owner","Change control for thresholds and new power saving modes","Monitoring of quality indicators with automatic rollback","Metered measurement method agreed with finance and sustainability reporting",[],{"howToBuild":168},"The sleep decisions themselves run in the radio vendors' software or a SON platform, close to the\nnetwork. Blits.ai fits around that loop: an **AI agent** with a **SQL knowledge base** over energy\nand quality tables lets engineers and sustainability teams ask where savings and quality moved,\nand a **knowledge base** holds the vendor feature documentation.\n\nAn **agentic task** or a scheduled **agentic workflow** can watch for quality drops in optimized\nclusters and propose a threshold change or an exclusion through **custom functions**, with\n**human in the loop approval** before anything is applied. Workflows can run on a schedule to\nproduce the daily savings and quality summary, **monitors** run recurring health checks on the\nagents themselves, and the platform is model agnostic with EU and UAE data residency.",[170,173,176],{"question":171,"answer":172},"How much energy can AI save in a radio network?","The results on this page are site or trial level. Telefónica reports that Ericsson's Radio Deep Sleep Mode, supported by AI and machine learning, saved up to 8% of a 5G site's 24 hour consumption and up to 26% in low traffic hours in a Madrid test. The operators and vendors cited here give network wide figures only as expectations: Nokia expects its software to deliver planned network energy cost savings of 8 to 10% across about 30,000 Safaricom cells, and BT Group expects up to 2 kWh per site per day.",{"question":174,"answer":175},"Is this different from the power saving features vendors already ship?","The features are the same; the difference is when and where they are used. BT Group puts capacity carriers to sleep based on quiet periods predicted for each site through machine learning, instead of one fixed schedule for the whole network.",{"question":177,"answer":178},"Does it hurt network quality?","It should not if coverage layers stay on, capacity wakes within seconds and quality is monitored per cell with automatic rollback. BT Group says its sleeping carriers wake within seconds without interruption to customers, and Telefónica's platforms periodically review quality so as not to affect network performance or user experience.",[180,181,182],"network-planning-and-capacity-optimization","autonomous-network-operations","predictive-network-maintenance","2026-09-27",[185],{"date":183,"note":186},"First published","ran-energy-optimization",[189,210,241,265,286],{"title":190,"useCases":191,"organization":192,"vendors":196,"summary":197,"stage":198,"year":35,"channels":199,"languages":200,"metrics":201,"outcomeDisclosed":194,"sources":202,"verification":205,"grade":207,"id":208,"organizationSlug":209},"BT Group: machine learning cell sleep across EE mobile sites",[187],{"name":193,"anonymized":194,"country":195,"region":150,"industry":17},"BT Group",false,"GB",[],"After trials in each of the UK's home nations, BT Group rolled out cell sleep software to more than 19,500 EE mobile sites. It puts 4G capacity carriers to sleep during quiet periods that machine learning has predicted for each site, wakes them automatically at busy times and within seconds when traffic surges unexpectedly, and can use a deeper sleep mode overnight. The sleep functions come from the radio equipment suppliers; BT Group's site data drives the statistical algorithms that control them. BT Group published an expected annual energy saving rather than a measured result.","scaled",[23],[],[],[203],{"url":34,"title":33,"publisher":193,"date":204},"2024-06-24",{"level":206,"checkedAt":183},"source-verified","B","bt-ee-cell-sleep-energy-saving","bt-group",{"title":211,"useCases":212,"organization":213,"vendors":216,"summary":220,"stage":221,"year":40,"channels":222,"languages":223,"metrics":224,"outcomeDisclosed":233,"sources":234,"verification":238,"grade":207,"id":239,"organizationSlug":240},"Telefónica: AI and machine learning to steer radio power saving features",[187],{"name":214,"anonymized":194,"country":215,"region":150,"industry":17},"Telefónica","ES",[217],{"name":218,"role":219},"Ericsson","platform","Telefónica has activated power saving features in its mobile networks for more than a decade. The early ones for 2G and 3G used static parameters; the current ones for 4G and 5G use AI and machine learning to predict traffic, set thresholds, shut down cells in low traffic hours and check quality. The platforms were first tested in O2 Germany in 2021. Telefónica Spain was the first operator to test Ericsson's Radio Deep Sleep Mode at a 5G site in Madrid, where the company reports savings of up to 8% of the site's 24 hour consumption and up to 26% in low traffic hours.","production",[23],[],[225],{"kpi":45,"value":226,"unit":227,"qualifier":228,"period":229,"claimant":230,"quote":231,"sourceUrl":232},8,"percent","up-to","total 24 hour consumption of one 5G test site in Madrid, Radio Deep Sleep Mode","organization","Supported by Artificial Intelligence and Machine Learning algorithms, the company achieved savings of up to 8%, considering the site’s total 24-hour consumption, and up to 26% in low traffic hours.","https://www.telefonica.com/en/communication-room/press-room/telefonica-drives-energy-consumption-optimisation-through-solutions-based-on-artificial-intelligence-and-machine-learning/",true,[235],{"url":232,"title":236,"publisher":214,"date":237},"Telefónica drives energy consumption optimisation through solutions based on Artificial Intelligence and Machine Learning","2022-03-02",{"level":206,"checkedAt":183},"telefonica-ai-radio-power-saving-features",null,{"title":242,"useCases":243,"organization":244,"vendors":248,"summary":251,"stage":221,"year":252,"channels":253,"languages":254,"metrics":255,"outcomeDisclosed":194,"sources":256,"verification":262,"grade":263,"id":264,"organizationSlug":240},"Indosat Ooredoo Hutchison: AI driven energy efficiency across its radio network",[187],{"name":245,"anonymized":194,"country":246,"region":247,"industry":17},"Indosat Ooredoo Hutchison","ID","asia-pacific",[249],{"name":250,"role":219},"Nokia","After a pilot in the live network, Indosat Ooredoo Hutchison deployed Nokia Energy Efficiency, part of Nokia's Autonomous Networks portfolio, across its entire Nokia radio access network footprint in Sumatra, Kalimantan, Central and East Java. The software uses AI and machine learning on real time traffic patterns to adjust or shut idle radio equipment during low demand and includes thermal management to cut cooling energy. It is multi vendor and delivered as a service. No measured savings are published.",2025,[23],[],[],[257],{"url":258,"title":259,"publisher":250,"date":260,"archivedUrl":261},"https://www.nokia.com/newsroom/indosat-ooredoo-hutchison-and-nokia-partner-to-reduce-energy-demand-and-support-ai-powered-sustainable-operations/","Indosat Ooredoo Hutchison and Nokia partner to reduce energy demand and support AI-powered, sustainable operations","2025-07-07","https://web.archive.org/web/20260103112934/https://www.nokia.com/newsroom/indosat-ooredoo-hutchison-and-nokia-partner-to-reduce-energy-demand-and-support-ai-powered-sustainable-operations/",{"level":206,"checkedAt":183},"C","indosat-ooredoo-hutchison-nokia-energy-efficiency",{"title":266,"useCases":267,"organization":268,"vendors":271,"summary":273,"stage":221,"year":274,"channels":275,"languages":276,"metrics":277,"outcomeDisclosed":194,"sources":278,"verification":284,"grade":263,"id":285,"organizationSlug":240},"O2 Telefónica Germany: AI energy saving on its Nokia radio network",[187],{"name":269,"anonymized":194,"country":270,"region":150,"industry":17},"O2 Telefónica Germany","DE",[272],{"name":250,"role":219},"O2 Telefónica Germany chose Nokia's AVA for Energy software, delivered as a service, for the parts of its radio network built on Nokia equipment. The software monitors traffic patterns and throttles back resources such as base stations during low usage, while monitoring quality so that customers do not notice the change. In its test the operator switched off unused radio resources automatically and saw significant savings, but no figure specific to O2 Telefónica Germany is published.",2023,[23],[],[],[279],{"url":280,"title":281,"publisher":250,"date":282,"archivedUrl":283},"https://www.nokia.com/newsroom/nokia-ava-for-energy-saas-chosen-by-o2-telefonica-germany-to-curb-energy-use-mwc23/","Nokia AVA for Energy SaaS chosen by O2 Telefónica Germany to curb energy use #MWC23","2023-02-24","https://web.archive.org/web/20250124103611/https://www.nokia.com/about-us/news/releases/2023/02/24/nokia-ava-for-energy-saas-chosen-by-o2-telefonica-germany-to-curb-energy-use-mwc23/",{"level":206,"checkedAt":183},"o2-telefonica-germany-nokia-energy-saas",{"title":287,"useCases":288,"organization":289,"vendors":293,"summary":295,"stage":198,"year":274,"channels":296,"languages":297,"metrics":298,"outcomeDisclosed":194,"sources":299,"verification":305,"grade":263,"id":306,"organizationSlug":240},"Safaricom: AI energy efficiency software across about 30,000 radio cells",[187],{"name":290,"anonymized":194,"country":291,"region":292,"industry":17},"Safaricom","KE","africa",[294],{"name":250,"role":219},"After a pilot, Safaricom Kenya rolled out Nokia's AVA Energy Efficiency software across approximately 30,000 5G, 4G and 3G cells. The software uses AI and machine learning to switch off idle and unused equipment automatically during low usage periods, together with Nokia's radio energy efficiency features, while maintaining network quality. The release gives planned energy cost savings, not measured results.",[23],[],[],[300],{"url":301,"title":302,"publisher":250,"date":303,"archivedUrl":304},"https://www.nokia.com/newsroom/nokia-deploys-ava-energy-efficiency-for-safaricom-kenya-to-drive-network-energy-savings/","Nokia deploys AVA Energy Efficiency for Safaricom Kenya to drive network energy savings","2023-11-27","https://web.archive.org/web/20250809193611/https://www.nokia.com/newsroom/nokia-deploys-ava-energy-efficiency-for-safaricom-kenya-to-drive-network-energy-savings/",{"level":206,"checkedAt":183},"safaricom-nokia-ava-energy-efficiency",0,[309],{"kpi":45,"label":310,"unit":227,"aggregate":233,"higherIsBetter":233,"n":307,"nUpTo":311,"median":240,"min":240,"max":240,"byClaimant":312,"vendorOnly":194,"points":313},"Energy savings",1,{"organization":307,"vendor":307,"regulator":307,"independent":307},[314],{"evidenceId":239,"organization":214,"value":226,"qualifier":228,"claimant":230,"grade":207,"pooled":194},{"low":316,"high":317},150000,3000000,[319,345,362,374],{"slug":180,"title":320,"shortTitle":321,"definition":322,"status":9,"industries":323,"functions":324,"patterns":326,"audience":330,"autonomy":331,"adoptionStage":27,"segment":28,"evidenceCount":332,"publicEvidenceCount":332,"organizations":333,"bestGrade":207,"headline":339,"lastVerified":183,"indexable":233},"AI for mobile network planning and capacity optimization","Network planning and capacity","Machine learning that forecasts where and when a mobile network will run out of capacity, recommends where to add cells, spectrum or hardware, and continuously tunes radio parameters so existing capacity carries more traffic, with planners approving investments and major changes.",[17],[19,325],"analytics-and-reporting",[21,327,328,329],"recommendation-and-personalization","anomaly-detection","agentic-workflow","employee-facing","supervised-agent",5,[334,335,336,337,338],"Deutsche Telekom","NTT DOCOMO","stc Group","Telefónica España","Vodafone",{"kpi":340,"label":341,"unit":227,"n":311,"nUpTo":307,"kind":342,"value":343,"qualifier":344,"claimant":230,"organization":334,"vendorReported":194},"processing-time-reduction","Cycle time reduction","reported",95,"at-least",{"slug":181,"title":346,"shortTitle":347,"definition":348,"status":9,"industries":349,"functions":350,"patterns":352,"audience":25,"autonomy":331,"adoptionStage":354,"segment":28,"evidenceCount":355,"publicEvidenceCount":355,"organizations":356,"bestGrade":207,"headline":360,"lastVerified":361,"indexable":233},"Agentic AI for autonomous, intent based network operations","Autonomous network operations","AI agents that run closed loops over a telecom network: they take an intent from the operator (for example a latency or availability target for a service), observe the network, diagnose deviations and execute corrective actions across radio, transport and core, within guardrails set by engineers and with human approval for major changes.",[17],[19,351],"it-and-engineering",[329,328,21,353],"classification-and-routing","emerging",6,[334,357,358,336,359],"du","KDDI","Telstra",{"kpi":340,"label":341,"unit":227,"n":311,"nUpTo":307,"kind":342,"value":343,"qualifier":344,"claimant":230,"organization":334,"vendorReported":194},"2026-09-26",{"slug":182,"title":363,"shortTitle":364,"definition":365,"status":9,"industries":366,"functions":367,"patterns":370,"audience":25,"autonomy":331,"adoptionStage":27,"segment":28,"evidenceCount":355,"publicEvidenceCount":355,"organizations":371,"bestGrade":207,"headline":240,"lastVerified":183,"indexable":233},"AI for predictive network maintenance in telecom","Predictive network maintenance","Machine learning that spots the early signs of network failure, such as degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention before customers lose service.",[17],[19,368,369],"field-service","operations",[328,21,329],[358,372,337,359,373,338],"Orange","Verizon",{"slug":375,"title":376,"shortTitle":377,"definition":378,"status":9,"industries":379,"functions":380,"patterns":383,"audience":25,"autonomy":331,"adoptionStage":27,"segment":384,"evidenceCount":385,"publicEvidenceCount":385,"organizations":386,"bestGrade":207,"headline":387,"lastVerified":183,"indexable":233},"telecom-fraud-detection","AI for telecom fraud detection (SIM swap, IRSF and Wangiri)","Telecom fraud detection","AI that protects the operator's own network, revenue and numbers from fraud: it watches call, messaging, roaming and account activity to detect SIM swap and port out takeovers, international revenue share fraud (IRSF) and Wangiri one ring scams, blocks or flags them in real time, and shares risk signals with banks and other businesses that rely on the phone number for security. Scam calls aimed at subscribers are handled by call blocking.",[17],[381,19,382],"fraud-prevention","security-operations",[328,21,353],"customer-protection",4,[359,338],{"kpi":388,"label":389,"unit":227,"n":311,"nUpTo":307,"kind":342,"value":390,"qualifier":391,"claimant":230,"organization":338,"vendorReported":194},"detection-rate-improvement","Detection improvement",30,"exact",{"indexable":233,"reasons":393},[],[395,400,406,413,420,426,433,440,447,454,461,467,474,481,487,491,498,504,510,516,522,528,534,539,544,551,557,562,567,574,580,586,593,598],{"id":142,"label":396,"issuer":149,"region":150,"url":397,"description":398,"useCases":399,"indexable":233},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":401,"label":402,"issuer":149,"region":150,"url":403,"description":404,"useCases":405,"indexable":233},"gdpr","GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":145,"label":407,"issuer":408,"region":409,"url":410,"description":411,"useCases":412,"indexable":233},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":144,"label":414,"issuer":415,"region":416,"url":417,"description":418,"useCases":419,"indexable":233},"NIST AI Risk Management Framework","NIST","north-america","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":421,"label":422,"issuer":149,"region":150,"url":423,"description":424,"useCases":425,"indexable":233},"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":427,"label":428,"issuer":429,"region":150,"url":430,"description":431,"useCases":432,"indexable":233},"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":434,"label":435,"issuer":436,"region":150,"url":437,"description":438,"useCases":439,"indexable":233},"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":441,"label":442,"issuer":443,"region":247,"url":444,"description":445,"useCases":446,"indexable":233},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":448,"label":449,"issuer":450,"region":247,"url":451,"description":452,"useCases":453,"indexable":233},"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":455,"label":456,"issuer":457,"region":409,"url":458,"description":459,"useCases":460,"indexable":233},"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":462,"label":463,"issuer":464,"region":416,"url":465,"description":466,"useCases":460,"indexable":233},"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":468,"label":469,"issuer":470,"region":150,"url":471,"description":472,"useCases":473,"indexable":233},"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":475,"label":476,"issuer":477,"region":409,"url":478,"description":479,"useCases":480,"indexable":233},"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":482,"label":483,"issuer":149,"region":150,"url":484,"description":485,"useCases":486,"indexable":233},"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":143,"label":488,"issuer":149,"region":150,"url":489,"description":490,"useCases":486,"indexable":233},"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":492,"label":493,"issuer":494,"region":416,"url":495,"description":496,"useCases":497,"indexable":233},"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":499,"label":500,"issuer":149,"region":150,"url":501,"description":502,"useCases":503,"indexable":233},"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":505,"label":506,"issuer":507,"region":416,"url":508,"description":509,"useCases":503,"indexable":233},"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":511,"label":512,"issuer":513,"region":409,"url":514,"description":515,"useCases":503,"indexable":233},"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":517,"label":518,"issuer":149,"region":150,"url":519,"description":520,"useCases":521,"indexable":233},"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":523,"label":524,"issuer":525,"region":416,"url":526,"description":527,"useCases":521,"indexable":233},"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":529,"label":530,"issuer":443,"region":247,"url":531,"description":532,"useCases":533,"indexable":233},"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":535,"label":536,"issuer":149,"region":150,"url":537,"description":538,"useCases":533,"indexable":233},"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":540,"label":541,"issuer":149,"region":150,"url":542,"description":543,"useCases":533,"indexable":233},"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":545,"label":546,"issuer":547,"region":150,"url":548,"description":549,"useCases":550,"indexable":233},"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":552,"label":553,"issuer":554,"region":416,"url":555,"description":556,"useCases":226,"indexable":233},"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.",{"id":558,"label":559,"issuer":149,"region":150,"url":560,"description":561,"useCases":226,"indexable":233},"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":563,"label":564,"issuer":149,"region":150,"url":565,"description":566,"useCases":355,"indexable":233},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":568,"label":569,"issuer":570,"region":571,"url":572,"description":573,"useCases":332,"indexable":233},"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.",{"id":575,"label":576,"issuer":577,"region":150,"url":578,"description":579,"useCases":385,"indexable":233},"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":581,"label":582,"issuer":583,"region":150,"url":584,"description":585,"useCases":385,"indexable":233},"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":587,"label":588,"issuer":589,"region":247,"url":590,"description":591,"useCases":592,"indexable":233},"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":594,"label":595,"issuer":149,"region":150,"url":596,"description":597,"useCases":592,"indexable":233},"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":599,"label":600,"issuer":601,"region":416,"url":602,"description":603,"useCases":592,"indexable":233},"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.",1790598301263]