[{"data":1,"prerenderedAt":604},["ShallowReactive",2],{"uc-network-planning-and-capacity-optimization":3,"uc-regulations":394},{"useCase":4,"evidence":173,"blitsAiDeployments":317,"benchmarks":318,"indicative":325,"related":328,"indexability":392,"includeUnpublished":179},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":47,"macroEstimates":68,"feasibility":69,"implementation":83,"risk":125,"blitsAi":151,"faq":153,"related":163,"datePublished":168,"dateModified":168,"lastVerified":168,"changelog":169,"slug":172},"AI for mobile network planning and capacity optimization","Network planning and capacity","AI for mobile network capacity planning","AI forecasts where mobile networks will congest and tunes radio settings. See Nokia's deployments for NTT DOCOMO and stc, plus Vodafone's trials.","published","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.",[12,13,14,15,16],"AI network planning","capacity planning for mobile networks","cognitive SON","AI RF optimization","self optimizing networks",[18],"telecommunications",[20,21],"network-operations","analytics-and-reporting",[23,24,25,26],"prediction-and-scoring","recommendation-and-personalization","anomaly-detection","agentic-workflow",[28,29],"internal-tools","api","employee-facing","supervised-agent","early-adopters","network","Mobile data traffic does not grow evenly across a network. Some cells can congest at busy hours\nwhile others are rarely loaded, and new housing, offices and events can move demand around faster\nthan annual planning cycles follow. A new site or carrier in the wrong place ties up investment\nwhile customers a few streets away still see slow speeds.\n\nBetween investments, radio engineers tune thousands of parameters (antenna tilts, power, handover\nand load balancing settings) to squeeze more out of the existing network. That work is slow and\nmanual. Vodafone describes a trial in which a machine learning algorithm found optimal voice over\nLTE settings for 450 cells in four hours, a task that would have taken an engineer around two and a\nhalf months by hand. With 4G, 5G and several vendors in one network, manual tuning gets harder\nstill.",[],"1. **Forecast demand.** Models forecast traffic per cell and area from history, subscriber growth,\n   device mix and planned developments, and flag where congestion will appear.\n2. **Estimate capacity.** The system estimates how much more traffic each cell can carry with its\n   current configuration and where the limit is (spectrum, hardware, backhaul, interference).\n3. **Optimise before building.** Self optimizing network functions tune parameters such as tilt,\n   power and load balancing so neighbouring cells share load. They can act ahead of demand: in a\n   Vodafone trial in Ireland, algorithms predicted where 3G traffic would peak in the next hour so\n   the network could rebalance load in advance.\n4. **Recommend investments.** Where optimisation is not enough, the system simulates candidate\n   sites, carriers or hardware upgrades and ranks them by traffic served per unit of spend.\n5. **Close the loop.** Planners approve investments; after each change the system compares the\n   measured effect with its forecast and recalibrates.",[38,39,40,41],"cost-to-serve","customer-experience","employee-productivity","speed",[43,44,45,46],"productivity-gain","processing-time-reduction","cost-savings","cost-reduction",{"referenceOrg":48,"inputs":49,"formula":63,"currency":64,"period":65,"resultLabel":66,"caveat":67},"A mobile operator with a capacity driven radio investment budget of USD 200 million a year",[50,56],{"key":51,"label":52,"low":53,"high":53,"unit":54,"note":55},"capacityCapex","Annual capacity driven radio investment",200000000,"USD per year","The reference operator.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"capexEfficiency","Share of capacity investment avoided or deferred through better targeting and optimisation",0.02,0.06,"fraction of capacity investment","Editorial assumption. None of the evidence on this page publishes a verified investment saving; replace with your own post investment reviews.","capacityCapex * capexEfficiency","USD","per year","Capacity investment avoided or deferred","Investment only, and deferral is not the same as saving. It leaves out engineering time released, the revenue and churn effect of fewer congested cells, and the cost of the planning platform and data.",[],{"complexity":70,"complexityNote":71,"dataPrerequisites":72,"integrations":77},"high","Forecasting and optimisation need clean, granular performance data from every vendor and a trusted digital view of the network. Automated parameter changes touch live customers, so they need strong guardrails and radio engineering buy in.",[73,74,75,76],"Traffic, quality and utilisation counters per cell and carrier, with at least a year of history","Site, antenna and configuration inventory that matches the live network","Subscriber and device growth data by area","Planned developments, events and competitor coverage where available",[78,79,80,81,82],"Radio network management and configuration per vendor","Self organizing network (SON) platform","Planning and propagation tools","Network data lake or analytics platform","Capital planning and project tracking systems",{"steps":84,"guardrails":100,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":112},[85,88,91,94,97],{"title":86,"detail":87},"Clean the network view","Reconcile inventory with the live configuration. Forecasts and simulations on a wrong site database lead to wrong investments.",{"title":89,"detail":90},"Start with forecasting and ranking","Use models to rank congested and soon to be congested cells, and let planners compare the ranking with their own judgement for a planning cycle.",{"title":92,"detail":93},"Automate reversible optimisation","Let SON functions change parameters within bounds and with automatic rollback, starting in one cluster with a control area.",{"title":95,"detail":96},"Link to the investment process","Feed the ranked recommendations into capital planning, and review every investment afterwards against the forecast it was based on.",{"title":98,"detail":99},"Extend across vendors and technologies","Move from single vendor tools to a view that covers 4G, 5G and every vendor, so optimisation in one layer does not hurt another.",[101,102,103,104],"Parameter changes only within engineering defined bounds, with automatic rollback on quality loss","Investments above a set value always approved by planners and finance","Exclusion of critical sites and emergency coverage from automated changes","Every automated change logged with its reason and measured effect","Radio planners and engineers own investment decisions and the bounds for automated optimisation. They review recommendations each planning cycle, approve changes outside the bounds, and use post investment reviews to decide how much to trust the forecasts.",[107,108,109,110,111],"Share of congested cells, by hour and area","Forecast accuracy of traffic and congestion per planning cycle","Throughput and quality before and after each optimisation or investment","Engineering hours per optimisation task","Capacity investment per unit of traffic carried",[113,116,119,122],{"title":114,"detail":115},"Optimising the average, hurting the edge","Changes improve cell averages while users at cell edges or indoors lose service. Monitor distributions, not only averages.",{"title":117,"detail":118},"Forecasts built on the past only","Models miss new housing, venues or competitor moves. Add planners' local knowledge and external data.",{"title":120,"detail":121},"Oscillating parameters","Several automated functions fight each other and parameters flip back and forth. Coordinate SON functions and damp changes.",{"title":123,"detail":124},"Trusting a vendor's black box","Recommendations cannot be explained to finance or engineers. Require the reasoning and data behind each recommendation.",{"euAiAct":126,"regulations":129,"guidance":134,"controls":145,"incidents":150},{"tier":127,"basis":128},"context-dependent","Forecasting demand, ranking congested cells and recommending investments 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 Recital 55 ties this to the digital infrastructure in the Annex to Directive (EU) 2022/2557, which includes providers of public electronic communications networks. Recital 55 defines such safety components as systems that directly protect the physical integrity of the infrastructure or the health and safety of persons and property and that are not necessary for the system to function. Closed loop parameter optimisation on the live radio network is high risk only when it serves in that role, for example a loop whose purpose is to protect emergency call availability, so each automated loop should be assessed against point 2 and the outcome documented. Loops that only optimise performance or capacity are usually not safety components.",[130,131,132,133],"eu-ai-act","nist-ai-rmf","iso-42001","nis2",[135,141],{"title":136,"issuer":137,"region":138,"url":139,"note":140},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 2 is the relevant test if automated optimisation becomes a safety component of network operation.",{"title":142,"issuer":137,"region":138,"url":143,"note":144},"Recital 55, safety components of critical infrastructure","https://artificialintelligenceact.eu/recital/55/","Explains which critical digital infrastructure is meant and what counts as a safety component, and excludes components used solely for cybersecurity.",[146,147,148,149],"Documented bounds for automated parameter changes with an accountable owner","Post investment review comparing forecast and measured traffic","Audit log of automated configuration changes","Model validation of forecasts before each planning cycle",[],{"howToBuild":152},"Forecasting and SON optimisation run in specialist radio tools. Blits.ai adds the planning\nassistant around them: an **AI agent** with a **SQL knowledge base** over traffic, congestion and\ninvestment tables lets planners ask questions in plain language (\"which cells in this region will\ncongest by next summer, and what did we spend there last year?\"), and a **knowledge base** holds\nplanning guidelines and vendor documentation.\n\n**Agentic workflows** can assemble a recommendation pack for a planning cycle through **custom\nfunctions** that call the forecasting and planning APIs, with **human in the loop approval**\nbefore anything reaches capital planning. Answers are logged with full traces, **test suites**\ncheck answer quality, and the platform is model agnostic.",[154,157,160],{"question":155,"answer":156},"What does AI add to self optimizing networks?","Classic SON functions apply rules that engineers set. AI based SON uses models to choose and time parameter changes autonomously. Nokia reported in 2024 that its MantaRay Cognitive SON, deployed in stc's commercial network in Saudi Arabia, processed more than 10,000 actions in a high traffic period and raised the utilisation of loaded cells by about 30 percent.",{"question":158,"answer":159},"Can AI decide where to build new sites?","It can rank candidate locations. In 2022 Nokia deployed its AI capacity planning software for NTT DOCOMO to predict the capacity of 4G cells and simulate the best candidate locations for 5G cells and radio hardware, and DOCOMO said it expected the software to help its network capacity design work. The investment decision should stay with planners and finance, who can weigh cost, coverage and local knowledge.",{"question":161,"answer":162},"How fast does machine learning tune a network compared with engineers?","In a Vodafone Germany trial with Huawei, a machine learning algorithm found optimal voice over LTE settings for 450 cells in four hours, which Vodafone says would have taken an engineer around two and a half months.",[164,165,166,167],"ran-energy-optimization","autonomous-network-operations","predictive-network-maintenance","network-fault-triage-copilot","2026-09-27",[170],{"date":168,"note":171},"First published","network-planning-and-capacity-optimization",[174,228,253,274,296],{"title":175,"useCases":176,"organization":177,"vendors":181,"summary":188,"stage":189,"year":190,"channels":191,"languages":192,"metrics":193,"outcomeDisclosed":209,"sources":210,"verification":222,"grade":225,"id":226,"organizationSlug":227},"Deutsche Telekom: RAN Guardian and MINDR agents for self healing network operations",[165,167,172],{"name":178,"anonymized":179,"country":180,"region":138,"industry":18},"Deutsche Telekom",false,"DE",[182,185],{"name":183,"role":184},"Google Cloud","platform",{"name":186,"role":187},"Google (Gemini models)","model-provider","Deutsche Telekom's RAN Guardian Agent, built with Gemini models on Google Cloud, went live in its German mobile network in November 2025. It is a multi agent system: one agent finds upcoming public events from public sources, another assesses whether nearby cells can carry the expected traffic and monitors them live, and a third executes corrective actions such as reallocating resources or adjusting configuration, documenting every action. It is being extended to the Czech Republic and Croatia. In February 2026 Deutsche Telekom announced MINDR, which applies the same approach end to end across radio, transport and core domains, with first production releases planned for later in 2026.","production",2025,[28,29],[],[194,203],{"kpi":44,"value":195,"unit":196,"qualifier":197,"period":198,"baseline":199,"claimant":200,"quote":201,"sourceUrl":202},95,"percent","at-least","live operations, major events","Time needed to manage major events before the agent (hours)","organization","And in live operations it has reduced the time needed to manage major events from hours to around a minute, a more than 95% improvement.","https://www.telekom.com/en/newsroom/latest-updates/media-information/2026/2/mindr-ai-agents-in-telekom-network",{"kpi":204,"value":205,"unit":206,"qualifier":197,"period":207,"claimant":200,"quote":208,"sourceUrl":202},"interactions-handled",100,"count","first month after launch, Christmas market events","Since its launch in November 2025, RAN Guardian Agent has autonomously triggered over 100 remediation actions at Christmas market events during its first month.",true,[211,214,218],{"url":202,"title":212,"publisher":178,"date":213},"Deutsche Telekom and Google Cloud Collaborate for Superior Network Experience with Agentic AI","2026-02-25",{"url":215,"title":216,"publisher":178,"date":217},"https://www.telekom.com/en/newsroom/latest-updates/media-information/2025/11/deutsche-telekom-ai-agents-for-mobile-network","Deutsche Telekom: AI agents for mobile network","2025-11-11",{"url":219,"title":220,"publisher":178,"date":221},"https://www.telekom.com/en/newsroom/latest-updates/media-information/2025/2/agentic-ai-for-autonomous-networks","Deutsche Telekom and Google Cloud Partner on Agentic AI for Autonomous Networks","2025-02-25",{"level":223,"checkedAt":224},"source-verified","2026-09-26","B","deutsche-telekom-ran-guardian-and-mindr-agents",null,{"title":229,"useCases":230,"organization":231,"vendors":234,"summary":239,"stage":240,"year":241,"channels":242,"languages":243,"metrics":244,"outcomeDisclosed":209,"sources":245,"verification":250,"grade":225,"id":251,"organizationSlug":252},"Vodafone: machine learning trials in centralised self organizing networks",[172],{"name":232,"anonymized":179,"country":233,"region":138,"industry":18},"Vodafone","GB",[235,237],{"name":236,"role":184},"Huawei",{"name":238,"role":184},"Cisco","Vodafone ran early machine learning trials in centralised self organizing networks. In Germany, with Huawei, an algorithm found the optimal voice over LTE settings for 450 randomly chosen cells in four hours, a task Vodafone says would take an engineer around two and a half months. In Ireland, with Cisco, algorithms predicted where 3G traffic would peak in the following hour so the network could rebalance load between neighbouring cells; Vodafone reports an average 6 percent improvement in mobile download speed in initial results. Vodafone planned commercial use from its 2018/19 financial year.","pilot",2017,[29],[],[],[246],{"url":247,"title":248,"publisher":232,"date":249},"https://www.vodafone.com/news/newsroom/technology/ai-enabled-augmented-engineering-increases-network-optimisation","AI enabled engineering increases network optimisation speed","2017-09-26",{"level":223,"checkedAt":168},"vodafone-machine-learning-son-trials","vodafone",{"title":254,"useCases":255,"organization":256,"vendors":259,"summary":262,"stage":189,"year":190,"channels":263,"languages":264,"metrics":265,"outcomeDisclosed":179,"sources":266,"verification":271,"grade":272,"id":273,"organizationSlug":227},"Telefónica España: big data and AI for network anomaly detection and optimization",[172,166],{"name":257,"anonymized":179,"country":258,"region":138,"industry":18},"Telefónica España","ES",[260],{"name":261,"role":184},"Microsoft","Telefónica España built a network data platform on Microsoft Azure (Azure Data Explorer, Azure Databricks and Power BI) to store and analyse the large volumes of data its 4G and 5G mobile network produces. The team uses it for anomaly detection, to address issues before they affect customers, and for automated network optimization. Telefónica says the project is live with several use cases deployed and that results have been very positive; Microsoft's summary adds substantial savings in operating costs. No figures are published.",[28],[],[],[267],{"url":268,"title":269,"publisher":261,"date":270},"https://www.microsoft.com/en/customers/story/21150-telefonica-group-spain-azure-ai-and-machine-learning","Telefónica España's transformation with Microsoft Azure: Enhancing network performance through big data and AI","2025-02-26",{"level":223,"checkedAt":168},"C","telefonica-espana-network-analytics-optimization",{"title":275,"useCases":276,"organization":277,"vendors":281,"summary":284,"stage":189,"year":285,"channels":286,"languages":287,"metrics":288,"outcomeDisclosed":209,"sources":289,"verification":294,"grade":272,"id":295,"organizationSlug":227},"stc: AI powered cognitive SON for autonomous radio optimisation",[172,165],{"name":278,"anonymized":179,"country":279,"region":280,"industry":18},"stc Group","SA","middle-east",[282],{"name":283,"role":184},"Nokia","Nokia deployed its MantaRay Cognitive SON, an AI powered feature of its self organizing network platform, in stc's commercial network in Saudi Arabia for the first time. The system optimises radio parameters autonomously. Nokia reports that during a period of high traffic it processed more than 10,000 actions, raised the utilisation rate of loaded cells by about 30 percent and average user throughput by 10 percent while traffic rose 40 percent, and that it reduced manual work. The results are stated by the vendor.",2024,[29],[],[],[290],{"url":291,"title":292,"publisher":283,"date":293},"https://www.nokia.com/newsroom/nokia-and-stc-group-optimize-network-with-ai-powered-mantaray-cognitive-son-solution-in-saudi-arabia/","Nokia and stc Group optimize network with AI-powered MantaRay Cognitive SON solution in Saudi Arabia","2024-07-02",{"level":223,"checkedAt":168},"stc-nokia-cognitive-son",{"title":297,"useCases":298,"organization":299,"vendors":303,"summary":305,"stage":189,"year":306,"channels":307,"languages":308,"metrics":309,"outcomeDisclosed":179,"sources":310,"verification":315,"grade":272,"id":316,"organizationSlug":227},"NTT DOCOMO: AI radio capacity planning for its 5G rollout",[172],{"name":300,"anonymized":179,"country":301,"region":302,"industry":18},"NTT DOCOMO","JP","asia-pacific",[304],{"name":283,"role":184},"NTT DOCOMO deployed Nokia's AI radio frequency capacity planning software, customised to its requirements, to support its 5G rollout. The software predicts the capacity of 4G cells from base station performance data and simulates the best candidate locations for 5G cells and radio hardware to meet the capacity needed in an area, helping DOCOMO see where congestion is starting and where to plan upgrades. No results are published.",2022,[28],[],[],[311],{"url":312,"title":313,"publisher":283,"date":314},"https://www.nokia.com/newsroom/nokia-deploys-ava-ai-software-to-help-ntt-docomo-enhance-5g-network-planning/","Nokia deploys AVA AI software to help NTT DOCOMO enhance 5G network planning","2022-09-13",{"level":223,"checkedAt":168},"ntt-docomo-nokia-ai-capacity-planning",0,[319],{"kpi":44,"label":320,"unit":196,"aggregate":209,"higherIsBetter":209,"n":321,"nUpTo":317,"median":195,"min":195,"max":195,"byClaimant":322,"vendorOnly":179,"points":323},"Cycle time reduction",1,{"organization":321,"vendor":317,"regulator":317,"independent":317},[324],{"evidenceId":226,"organization":178,"value":195,"qualifier":197,"claimant":200,"grade":225,"pooled":209},{"low":326,"high":327},4000000,12000000,[329,351,367,379],{"slug":164,"title":330,"shortTitle":331,"definition":332,"status":9,"industries":333,"functions":334,"patterns":335,"audience":336,"autonomy":337,"adoptionStage":32,"segment":33,"evidenceCount":338,"publicEvidenceCount":338,"organizations":339,"bestGrade":225,"headline":345,"lastVerified":168,"indexable":209},"AI for radio access network energy optimization","RAN energy optimization","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.",[18],[20],[23],"back-office","autonomous",5,[340,341,342,343,344],"BT Group","Indosat Ooredoo Hutchison","O2 Telefónica Germany","Safaricom","Telefónica",{"kpi":346,"label":347,"unit":196,"n":317,"nUpTo":321,"kind":348,"value":349,"qualifier":350,"claimant":200,"organization":344,"vendorReported":179},"energy-savings","Energy savings","reported",8,"up-to",{"slug":165,"title":352,"shortTitle":353,"definition":354,"status":9,"industries":355,"functions":356,"patterns":358,"audience":336,"autonomy":31,"adoptionStage":360,"segment":33,"evidenceCount":361,"publicEvidenceCount":361,"organizations":362,"bestGrade":225,"headline":366,"lastVerified":224,"indexable":209},"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.",[18],[20,357],"it-and-engineering",[26,25,23,359],"classification-and-routing","emerging",6,[178,363,364,278,365],"du","KDDI","Telstra",{"kpi":44,"label":320,"unit":196,"n":321,"nUpTo":317,"kind":348,"value":195,"qualifier":197,"claimant":200,"organization":178,"vendorReported":179},{"slug":166,"title":368,"shortTitle":369,"definition":370,"status":9,"industries":371,"functions":372,"patterns":375,"audience":336,"autonomy":31,"adoptionStage":32,"segment":33,"evidenceCount":361,"publicEvidenceCount":361,"organizations":376,"bestGrade":225,"headline":227,"lastVerified":168,"indexable":209},"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.",[18],[20,373,374],"field-service","operations",[25,23,26],[364,377,257,365,378,232],"Orange","Verizon",{"slug":167,"title":380,"shortTitle":381,"definition":382,"status":9,"industries":383,"functions":384,"patterns":385,"audience":30,"autonomy":388,"adoptionStage":32,"segment":33,"evidenceCount":361,"publicEvidenceCount":361,"organizations":389,"bestGrade":225,"headline":391,"lastVerified":168,"indexable":209},"AI copilot for network operations centre fault triage","NOC fault triage copilot","AI in the network operations centre (NOC) that correlates alarms and performance data from radio, transport, core and fixed networks into a small number of probable faults, ranks them by customer impact, proposes the likely root cause and fix from runbooks, vendor documentation and past tickets, and routes the ticket to the right team, while an engineer decides what to change.",[18],[20,374],[25,359,386,387,26],"rag-knowledge-assistant","summarization","copilot",[390,178,364,377,365,232],"Bell Canada",{"kpi":44,"label":320,"unit":196,"n":321,"nUpTo":317,"kind":348,"value":195,"qualifier":197,"claimant":200,"organization":178,"vendorReported":179},{"indexable":209,"reasons":393},[],[395,400,406,413,420,426,433,440,447,454,461,467,474,481,487,491,498,504,510,516,522,528,534,539,544,551,557,562,567,573,580,586,593,598],{"id":130,"label":396,"issuer":137,"region":138,"url":397,"description":398,"useCases":399,"indexable":209},"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":137,"region":138,"url":403,"description":404,"useCases":405,"indexable":209},"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":132,"label":407,"issuer":408,"region":409,"url":410,"description":411,"useCases":412,"indexable":209},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":131,"label":414,"issuer":415,"region":416,"url":417,"description":418,"useCases":419,"indexable":209},"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":137,"region":138,"url":423,"description":424,"useCases":425,"indexable":209},"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":138,"url":430,"description":431,"useCases":432,"indexable":209},"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":138,"url":437,"description":438,"useCases":439,"indexable":209},"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":302,"url":444,"description":445,"useCases":446,"indexable":209},"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":302,"url":451,"description":452,"useCases":453,"indexable":209},"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":209},"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":209},"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":138,"url":471,"description":472,"useCases":473,"indexable":209},"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":209},"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":137,"region":138,"url":484,"description":485,"useCases":486,"indexable":209},"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":133,"label":488,"issuer":137,"region":138,"url":489,"description":490,"useCases":486,"indexable":209},"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":209},"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":137,"region":138,"url":501,"description":502,"useCases":503,"indexable":209},"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":209},"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":209},"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":137,"region":138,"url":519,"description":520,"useCases":521,"indexable":209},"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":209},"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":302,"url":531,"description":532,"useCases":533,"indexable":209},"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":137,"region":138,"url":537,"description":538,"useCases":533,"indexable":209},"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":137,"region":138,"url":542,"description":543,"useCases":533,"indexable":209},"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":138,"url":548,"description":549,"useCases":550,"indexable":209},"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":349,"indexable":209},"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":137,"region":138,"url":560,"description":561,"useCases":349,"indexable":209},"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":137,"region":138,"url":565,"description":566,"useCases":361,"indexable":209},"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":280,"url":571,"description":572,"useCases":338,"indexable":209},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":574,"label":575,"issuer":576,"region":138,"url":577,"description":578,"useCases":579,"indexable":209},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":581,"label":582,"issuer":583,"region":138,"url":584,"description":585,"useCases":579,"indexable":209},"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":302,"url":590,"description":591,"useCases":592,"indexable":209},"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":137,"region":138,"url":596,"description":597,"useCases":592,"indexable":209},"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":209},"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.",1790598300624]