[{"data":1,"prerenderedAt":627},["ShallowReactive",2],{"uc-home-wifi-experience-optimization":3,"uc-regulations":404},{"useCase":4,"evidence":188,"blitsAiDeployments":294,"benchmarks":295,"indicative":315,"related":317,"indexability":402,"includeUnpublished":194},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":41,"valueDrivers":42,"kpis":46,"indicativeValue":51,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":140,"blitsAi":166,"faq":168,"related":178,"datePublished":183,"dateModified":183,"lastVerified":183,"changelog":184,"slug":187},"AI for home Wi-Fi experience optimization","Home Wi-Fi optimization","AI home Wi-Fi optimization for broadband ISPs","AI tunes home Wi-Fi channels and traffic before customers call. Aprecomm reports 25% fewer calls at ACT Fibernet; Sky Italy saw 15 to 20% in a proof of concept.","published","Home Wi-Fi experience optimization is AI in a broadband operator's gateway and cloud that measures Wi-Fi quality per device and fixes interference and congestion automatically, before customers call. It switches channels, steers devices between bands and prioritizes latency sensitive traffic, and gives support staff the device history when a call does arrive.",[12,13,14,15,16],"smart Wi-Fi","managed Wi-Fi AI","in home Wi-Fi quality of experience","Wi-Fi self optimization","proactive Wi-Fi care",[18],"telecommunications",[20,21,22],"customer-service","network-operations","field-service",[24,25,26],"prediction-and-scoring","anomaly-detection","agentic-workflow",[28,29],"api","agent-desktop","back-office","autonomous","early-adopters","home broadband","A broadband line can test fine while the Wi-Fi inside the home is poor. Neighboring networks and\nextenders crowd the same channels, too many devices compete for airtime, and background traffic eats\nthe bandwidth a video call needs. To the customer the internet feels broken, and the traditional\nresponse is to wait for a call, run a diagnosis and sometimes send a technician. Aprecomm,\ndescribing its ACT Fibernet deployment, calls this gap between \"the internet is working\" and \"the\ninternet feels broken\" the place where trust was eroding and leading to churn.\n\nSky Italy's Head of Broadband Product Delivery made a related point at a conference: with more than\n80% of devices connecting over Wi-Fi, fixing Wi-Fi settles most connectivity problems, and speed\ntests no longer describe what customers experience. What matters is latency, jitter and whether a\ngiven application works on a given device.",[36],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"Sky Italy's Head of Broadband Product Delivery said at Network X that more than 80% of devices connect via Wi-Fi and about 90% of smartphone data travels over wireless networks, so resolving Wi-Fi issues addresses most customer connectivity problems.","From Speed Tests to Real Results: How Sky Italy Cut Support Calls by 20% Using AI","https:\u002F\u002Finformaconnect.com\u002Fsky-italys-wi-fi-experience-revolution-from-bandwidth-to-quality-of-outcome\u002F",2026,"1. **Measure.** Software on the gateway or router, plus a cloud service, collects Wi-Fi telemetry\n   such as channel use, signal strength, retries and latency, and scores the quality of experience\n   for each device and application. ACT Fibernet's system uses more than 15 parameters per score.\n2. **Detect.** The model spots interference from neighboring networks, congested channels, devices\n   stuck on a weak band and early signs of degradation, often before the customer notices.\n3. **Act.** The system changes the channel, steers a device to a better band or access point, or\n   prioritizes traffic that is sensitive to delay, for example a video call or a remote worker's\n   laptop on weekdays. Sky Italy's proofs of concept covered channel changes and band steering\n   (found by scanning off channel) and traffic prioritization for home workers.\n4. **Support.** When a customer does call, the agent sees device history, recent channel changes and\n   application performance, so the call starts from a diagnosis instead of questions.\n5. **Learn across homes.** Patterns that fix one household feed the model for similar homes, and\n   the operator's own telemetry becomes the training data.",[43,44,45],"cost-to-serve","customer-experience","revenue-growth",[47,48,49,50],"contact-deflection","churn-reduction","quality-score-uplift","cost-reduction",{"referenceOrg":52,"inputs":53,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A broadband operator with 1 million home subscribers",[54,59,66,72],{"key":55,"label":56,"low":57,"high":57,"unit":55,"note":58},"subscribers","Home broadband subscribers",1000000,"The reference operator.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"wifiCallsPerSub","Wi-Fi related support calls per subscriber per year",0.2,0.5,"calls per subscriber per year","Editorial assumption, replace with your own call reasons.",{"key":67,"label":68,"low":69,"high":62,"unit":70,"note":71,"sourceUrl":39},"reduction","Reduction in Wi-Fi related calls",0.1,"fraction of calls","Conservative against the evidence on this page. Sky Italy's Head of Broadband Product Delivery reported 15 to 20% in a two week proof of concept (per the Network X write up), and Aprecomm reports 25% for ACT Fibernet.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"costPerCall","Fully loaded cost of one support call",5,10,"USD per call","Editorial assumption, replace with your own cost per contact.","subscribers * wifiCallsPerSub * reduction * costPerCall","USD","per year","Support call cost avoided","Leaves out avoided technician visits, churn (Aprecomm for ACT Fibernet and Plume for Armstrong describe fewer departures, but Plume's 18.75% for Armstrong is a vendor figure with no baseline), router software and cloud costs, and any managed Wi-Fi revenue.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","The build needs gateways or mesh pods that can report telemetry and accept remote channel and band changes, which depends on the router software the operator controls (ACT Fibernet runs its own router operating system). It then needs a cloud service, a support dashboard connected to the ticketing system and a consent basis for collecting household device data.",[89,90,91,92],"Wi-Fi telemetry per access point and device, collected at short intervals","Support contacts tagged by reason, to find the Wi-Fi related share","Router make, model and firmware inventory","Customer consent and notice for collecting device level data in the home",[94,95,96,97],"Gateway or router management system with remote configuration rights","Cloud analytics platform for telemetry","Contact center agent desktop and ticketing","Customer app, if customers can see or control their Wi-Fi",{"steps":99,"guardrails":115,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":127},[100,103,106,109,112],{"title":101,"detail":102},"Baseline the Wi-Fi call share","Tag support contacts and technician visits by cause so you know how many are Wi-Fi quality, not a line fault, and set the measurement window before launch.",{"title":104,"detail":105},"Start with channel and band steering","Channel switching and band steering are low risk and visible in telemetry. Run them in a subset of homes with a holdout group so call reduction is measured against homes without the service.",{"title":107,"detail":108},"Add traffic prioritization carefully","Prioritizing a category such as remote work video calls needs a legal check against traffic management rules and a clear customer facing description.",{"title":110,"detail":111},"Give agents the device history","Show channel changes, device scores and application performance on the agent desktop, so the first call resolves more.",{"title":113,"detail":114},"Scale by device and home type","Widen to more homes after the holdout shows a result, then measure churn over a longer period.",[116,117,118,119],"Collect Wi-Fi and device data only for optimization and care, with notice and an opt out where the law requires it","Limit traffic prioritization to categories justified by technical needs and disclosed to customers","Rate limits on how often a home's settings change, so the optimizer cannot disconnect devices repeatedly","A remote switch to turn optimization off per home or per router model","The model changes settings by itself within rules that network engineers set. Engineers review changes that cause disconnects, support teams can switch optimization off for a home, and product owners approve any new prioritization category.",[122,123,124,125,126],"Wi-Fi related contacts per thousand subscribers, against a holdout group","Device quality of experience score distribution, including the worst decile","Technician visits and router swaps for Wi-Fi complaints","Churn of optimized homes against comparable homes","Disconnects and complaints caused by automatic changes",[128,131,134,137],{"title":129,"detail":130},"Averages hide the unhappy homes","A rise in the mean score can coexist with a group of homes that got worse. Track the worst decile and complaint rates per home.",{"title":132,"detail":133},"Automatic changes interrupt a call","Switching a channel mid call drops the session. Limit changes during active traffic and cap how often they occur.",{"title":135,"detail":136},"Results measured in the wrong window","Two weeks of data (Sky Italy's proof of concept) is too short to judge churn. Plan a longer read for retention.",{"title":138,"detail":139},"Router estate too mixed","Old or unmanaged routers cannot be steered. Start with equipment you can configure and plan replacement for the rest.",{"euAiAct":141,"regulations":144,"guidance":148,"controls":160,"incidents":165},{"tier":142,"basis":143},"minimal","Tuning radio channels and traffic priorities in a home network is not listed in Annex III. Point 2 covers safety components in critical digital infrastructure, and the in home LAN and gateway are not the public electronic communications network. Recital 55 limits safety components to systems that protect the physical integrity of that infrastructure, which channel tuning does not do. The system also does not interact with the customer as a conversational system. A chatbot or voice assistant that explains the changes to customers carries the Article 50 duty to say it is AI, unless that is obvious.",[145,146,147],"eu-ai-act","gdpr","telecom-consumer-rules",[149,155],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"BEREC Guidelines on the Implementation of the Open Internet Regulation","BEREC","europe","https:\u002F\u002Fberec.europa.eu:443\u002Fsites\u002Fdefault\u002Ffiles\u002Ffiles\u002Fdocument_register_store\u002F2022\u002F6\u002FBoR_%2822%29_81_Update_to_the_BEREC_Guidelines_on_the_Implementation_of_the_Open_Internet_Regulation.pdf","Sets how traffic management may differ between categories of traffic in the EU, which matters if the optimizer prioritizes some applications over others. The telecom consumer rules apply to this use case through the Open Internet Regulation's transparency and traffic management rules, not through contract, billing or switching rules. Check the current version.",{"title":156,"issuer":157,"region":152,"url":158,"note":159},"General Data Protection Regulation","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","Device lists and traffic patterns from a household are personal data, so purpose limitation and minimization apply.",[161,162,163,164],"Documented purpose and retention limits for in home telemetry","Legal review of any traffic prioritization categories","Holdout group that stays on the previous setup for measurement","Change log of automatic settings per home, available to support staff",[],{"howToBuild":167},"The optimization engine runs in the operator's gateway software and cloud, not on Blits.ai. What\nBlits.ai builds around it is the care side. An **AI agent** on the **web chat widget**,\n**WhatsApp** or **voice** uses **custom functions** to read a home's device scores, recent channel\nchanges and open tickets from the operator's systems, then tells the customer in plain words what\nwas changed and what to try next. **Human handover** passes the full context to a care agent when\nthe problem needs a person.\n\nFor the care team, a second agent on the agent desktop side summarizes the home's history through\nthe **REST API channel**. **Agentic workflows** with **human in the loop approval** can propose a\nrouter swap or a technician visit when scores stay low. **Guardrails** keep answers within the\noperator's policies, **test suites** replay real Wi-Fi complaints before every change, and\n**analytics** track interactions, satisfaction and handovers.",[169,172,175],{"question":170,"answer":171},"Does AI Wi-Fi optimization reduce support calls?","Operators and vendors report it does. Aprecomm says ACT Fibernet's inbound support calls dropped by 25% once the deployment was in place, and Sky Italy's head of broadband product delivery reported a 15 to 20% reduction within two weeks of a proof of concept. Both are the reporting parties' own figures and neither was independently audited. The Sky Italy write up ties the figure to the traffic prioritization step, which prioritized laptops of home workers on weekdays from 9 AM to 6 PM, and names Cujo AI agents only for the channel scanning work, so that step may follow fixed rules rather than a learned model.",{"question":173,"answer":174},"What does the AI change in the home?","Mainly the channel a device or access point uses, the band it connects on, and the priority of delay sensitive traffic. ACT Fibernet reports that over 80% of Smart TVs and over 70% of laptops were steered to better channels in its first launch.",{"question":176,"answer":177},"Is prioritizing some traffic allowed?","In the EU, traffic management must follow the Open Internet Regulation, and BEREC's guidelines say reasonable management should rest on objectively different technical requirements of categories of traffic. Get legal advice before you prioritize a category and describe it to customers.",[179,180,181,182],"device-and-connectivity-troubleshooting-agent","predictive-network-maintenance","field-technician-copilot-and-dispatch","churn-prediction-and-retention-offers","2026-10-14",[185],{"date":183,"note":186},"First published","home-wifi-experience-optimization",[189,235,262],{"title":190,"useCases":191,"organization":192,"vendors":198,"summary":202,"stage":203,"year":40,"channels":204,"languages":205,"metrics":207,"outcomeDisclosed":220,"sources":221,"verification":230,"grade":232,"id":233,"organizationSlug":234},"ACT Fibernet: ACT SmartWi-Fi, agentic AI that tunes home Wi-Fi",[187],{"name":193,"anonymized":194,"country":195,"region":196,"industry":18,"wikidata":197},"ACT Fibernet",false,"IN","asia-pacific","Q16826933",[199],{"name":200,"role":201},"Aprecomm","platform","ACT Fibernet, a large fixed line broadband provider in India, runs ACT SmartWi-Fi on its Zippy router operating system with Aprecomm's agentic AI. The software scans in home Wi-Fi, scores the quality of experience of each device from more than 15 parameters, and fixes interference and congestion by switching channels and steering devices between bands, with no action from the subscriber. The first launch covered 250,000 homes. Aprecomm's later account of the deployment reports 25% fewer inbound support calls, with the deployment complete in 1.35 million homes.","scaled",[28,29],[206],"en",[208,216],{"kpi":47,"value":209,"unit":210,"qualifier":211,"period":212,"claimant":213,"quote":214,"sourceUrl":215},25,"percent","exact","reported after about six months of deployment","vendor","With the AI catching and resolving issues before subscribers noticed them, the volume of inbound support calls dropped by 25%.","https:\u002F\u002Faprecomm.ai\u002F?p=4580",{"kpi":49,"value":217,"unit":210,"qualifier":211,"baseline":218,"claimant":213,"quote":219,"sourceUrl":215},20,"device quality of experience scores","Device QoE scores improved by an average of 20% over the 1.35m homes where deployment was complete.",true,[222,225],{"url":215,"title":223,"publisher":200,"date":224},"When AI Stops Talking and Starts Delivering","2026-06-04",{"url":226,"title":227,"publisher":228,"date":229},"https:\u002F\u002Ftele.net.in\u002Fact-fibernet-shares-early-results-of-its-act-smartwi-fi-launch\u002F","ACT Fibernet shares early results of its ACT SmartWi-Fi launch","tele.net","2025-03-03",{"level":231,"checkedAt":183},"source-verified","C","act-fibernet-aprecomm-smart-wifi",null,{"title":236,"useCases":237,"organization":238,"vendors":242,"summary":245,"stage":246,"year":40,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":220,"sources":256,"verification":260,"grade":232,"id":261,"organizationSlug":234},"Sky Italy: AI agents that tune home Wi-Fi and prioritize home workers",[187],{"name":239,"anonymized":194,"country":240,"region":152,"industry":18,"wikidata":241},"Sky Italy","IT","Q796719",[243],{"name":244,"role":201},"Cujo AI","Sky Italy ran two proofs of concept on home Wi-Fi. In the first, Cujo AI agents scanned off channel to find interference from neighboring networks and third party extenders and then changed channels or moved devices to another frequency. In the second, the operator identified customers working from home on weekdays and prioritized their laptop traffic. The Head of Broadband Product Delivery presented the work at a conference, and the organizer's write up reports that customer service calls fell by 15 to 20% within two weeks.","pilot",[28],[206],[250],{"kpi":47,"value":251,"unit":210,"qualifier":252,"period":253,"claimant":254,"quote":255,"sourceUrl":39},15,"at-least","within two weeks of the traffic prioritization proof of concept","organization","Sky Italy achieved a remarkable 15-20% reduction in customer service calls within just two weeks.",[257],{"url":39,"title":38,"publisher":258,"date":259},"Informa Connect (Network X)","2026-09-15",{"level":231,"checkedAt":183},"sky-italy-cujo-ai-home-wifi-optimization",{"title":263,"useCases":264,"organization":265,"vendors":269,"summary":272,"stage":273,"year":274,"channels":275,"languages":277,"metrics":278,"outcomeDisclosed":220,"sources":288,"verification":292,"grade":232,"id":293,"organizationSlug":234},"Armstrong: Plume's AI driven Wi-Fi experience platform for broadband subscribers",[187],{"name":266,"anonymized":194,"country":267,"region":268,"industry":18},"Armstrong","US","north-america",[270],{"name":271,"role":201},"Plume","Armstrong, a broadband service provider, was running an architecture that Plume's page says trailed retail Wi-Fi products while customer calls and truck rolls increased. It adopted Plume's cloud based experience management platform, which Plume describes as data centric and AI driven, to improve in home coverage, give customers control of their Wi-Fi and let support staff troubleshoot remotely. Plume's page, citing an Omdia case study, lists a drop of up to 30% in Wi-Fi related service calls and up to 18.75% lower churn.","production",2022,[276],"mobile-app",[206],[279,285],{"kpi":47,"value":280,"unit":210,"qualifier":281,"baseline":282,"claimant":213,"quote":283,"sourceUrl":284},30,"up-to","Wi-Fi related service calls","30% drop in WiFi-related service calls.","https:\u002F\u002Fplume.com\u002Fresources\u002Farmstrong-reduces-customer-churn-by-18-75-with-plumes-ai-driven-cloud-based-solution",{"kpi":48,"value":286,"unit":210,"qualifier":281,"claimant":213,"quote":287,"sourceUrl":284},18.75,"18.75% reduction in customer churn.",[289],{"url":284,"title":290,"publisher":271,"date":291},"Armstrong reduces customer churn by 18.75% with Plume's AI-driven, cloud-based solution","2022-04-25",{"level":231,"checkedAt":183},"armstrong-plume-ai-wifi-experience-management",0,[296,305,310],{"kpi":47,"label":297,"unit":210,"aggregate":220,"higherIsBetter":220,"n":298,"nUpTo":299,"median":217,"min":251,"max":209,"byClaimant":300,"vendorOnly":194,"points":301},"Contact deflection",2,1,{"organization":299,"vendor":299,"regulator":294,"independent":294},[302,303,304],{"evidenceId":293,"organization":266,"value":280,"qualifier":281,"claimant":213,"grade":232,"pooled":194},{"evidenceId":233,"organization":193,"value":209,"qualifier":211,"claimant":213,"grade":232,"pooled":220},{"evidenceId":261,"organization":239,"value":251,"qualifier":252,"claimant":254,"grade":232,"pooled":220},{"kpi":49,"label":306,"unit":210,"aggregate":220,"higherIsBetter":220,"n":299,"nUpTo":294,"median":217,"min":217,"max":217,"byClaimant":307,"vendorOnly":220,"points":308},"Quality score uplift",{"organization":294,"vendor":299,"regulator":294,"independent":294},[309],{"evidenceId":233,"organization":193,"value":217,"qualifier":211,"claimant":213,"grade":232,"pooled":220},{"kpi":48,"label":311,"unit":210,"aggregate":220,"higherIsBetter":220,"n":294,"nUpTo":299,"median":234,"min":234,"max":234,"byClaimant":312,"vendorOnly":194,"points":313},"Churn reduction",{"organization":294,"vendor":294,"regulator":294,"independent":294},[314],{"evidenceId":293,"organization":266,"value":286,"qualifier":281,"claimant":213,"grade":232,"pooled":194},{"low":316,"high":57},100000,[318,346,366,382],{"slug":179,"title":319,"shortTitle":320,"definition":321,"status":9,"industries":322,"functions":323,"patterns":324,"audience":329,"autonomy":330,"adoptionStage":32,"segment":331,"evidenceCount":332,"publicEvidenceCount":332,"organizations":333,"bestGrade":338,"headline":339,"lastVerified":345,"indexable":220},"AI agent for device and connectivity troubleshooting on voice and chat","Device and connectivity troubleshooting","An AI troubleshooting agent diagnoses and fixes a customer's broadband, mobile, TV or device problem by conversation on the phone or in chat. It runs line tests and remote resets through the operator's systems, guides the customer step by step, and books an engineer or hands over to a technician when the fault needs a person.",[18],[20,22],[325,326,26,327,328],"conversational-agent","voice-agent","rag-knowledge-assistant","computer-vision","customer-facing","supervised-agent","front-office",4,[334,335,336,337],"Singtel","Virgin Media O2","Vodafone Germany","Vodafone","B",{"kpi":340,"label":341,"unit":210,"n":342,"nUpTo":294,"kind":343,"value":344,"qualifier":211,"claimant":234,"organization":234,"vendorReported":194},"containment-rate","Containment rate",3,"median",70,"2026-09-26",{"slug":180,"title":347,"shortTitle":348,"definition":349,"status":9,"industries":350,"functions":351,"patterns":353,"audience":30,"autonomy":330,"adoptionStage":32,"segment":354,"evidenceCount":355,"publicEvidenceCount":355,"organizations":356,"bestGrade":338,"headline":234,"lastVerified":365,"indexable":220},"AI for predictive network maintenance in telecom","Predictive network maintenance","Predictive network maintenance is machine learning that spots early signs of network failure and triggers a fix before customers lose service, for telecom operators. It watches for degrading cells, faulty customer equipment, aging hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention.",[18],[21,22,352],"operations",[25,24,26],"network",9,[357,358,359,360,361,362,363,364,337],"Grameenphone","KDDI","Orange","Telefónica España","Telkomsel","Telstra","TIM Brasil","Verizon","2026-09-27",{"slug":181,"title":367,"shortTitle":368,"definition":369,"status":9,"industries":370,"functions":371,"patterns":372,"audience":374,"autonomy":375,"adoptionStage":376,"segment":354,"evidenceCount":75,"publicEvidenceCount":75,"organizations":377,"bestGrade":338,"headline":234,"lastVerified":365,"indexable":220},"AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","A field technician copilot is AI that decides whether a fault needs a site visit at all and predicts what work and parts the job will need. It also helps plan and update appointments, and gives technicians on site guided diagnosis and instant answers from manuals and past jobs, so more jobs are fixed on the first visit.",[18],[22,352,20],[24,327,325,373],"classification-and-routing","employee-facing","copilot","emerging",[378,379,380,381,337],"Advanced Info Service (AIS)","Bouygues Telecom","nbn","Openreach",{"slug":182,"title":383,"shortTitle":384,"definition":385,"status":9,"industries":386,"functions":387,"patterns":391,"audience":30,"autonomy":330,"adoptionStage":393,"segment":394,"evidenceCount":75,"publicEvidenceCount":75,"organizations":395,"bestGrade":338,"headline":400,"lastVerified":365,"indexable":220},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI churn prediction and retention is AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals. It explains the likely reason and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[18],[388,20,389,390],"marketing","sales","analytics-and-reporting",[24,392,325],"recommendation-and-personalization","mainstream","middle-office",[396,397,398,335,399],"BT Business","Etisalat","Telenet","Vodafone UK",{"kpi":48,"label":311,"unit":210,"n":298,"nUpTo":294,"kind":401,"value":217,"qualifier":211,"claimant":213,"organization":398,"vendorReported":220},"reported",{"indexable":220,"reasons":403},[],[405,410,414,422,429,436,442,449,456,463,469,475,481,487,494,499,505,511,517,524,529,534,539,546,553,558,563,568,575,580,588,594,599,605,611,616,621],{"id":145,"label":406,"issuer":157,"region":152,"url":407,"description":408,"useCases":409,"indexable":220},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",295,{"id":146,"label":411,"issuer":157,"region":152,"url":158,"description":412,"useCases":413,"indexable":220},"GDPR","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":415,"label":416,"issuer":417,"region":418,"url":419,"description":420,"useCases":421,"indexable":220},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",148,{"id":423,"label":424,"issuer":425,"region":268,"url":426,"description":427,"useCases":428,"indexable":220},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",115,{"id":430,"label":431,"issuer":432,"region":152,"url":433,"description":434,"useCases":435,"indexable":220},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",84,{"id":437,"label":438,"issuer":157,"region":152,"url":439,"description":440,"useCases":441,"indexable":220},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",73,{"id":443,"label":444,"issuer":445,"region":152,"url":446,"description":447,"useCases":448,"indexable":220},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",53,{"id":450,"label":451,"issuer":452,"region":196,"url":453,"description":454,"useCases":455,"indexable":220},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":457,"label":458,"issuer":459,"region":196,"url":460,"description":461,"useCases":462,"indexable":220},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":464,"label":465,"issuer":157,"region":152,"url":466,"description":467,"useCases":468,"indexable":220},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":470,"label":471,"issuer":472,"region":418,"url":473,"description":474,"useCases":468,"indexable":220},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":476,"label":477,"issuer":478,"region":268,"url":479,"description":480,"useCases":468,"indexable":220},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":482,"label":483,"issuer":484,"region":268,"url":485,"description":486,"useCases":217,"indexable":220},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",{"id":488,"label":489,"issuer":490,"region":152,"url":491,"description":492,"useCases":493,"indexable":220},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":495,"label":496,"issuer":157,"region":152,"url":497,"description":498,"useCases":251,"indexable":220},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":500,"label":501,"issuer":502,"region":418,"url":503,"description":504,"useCases":251,"indexable":220},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":506,"label":507,"issuer":508,"region":268,"url":509,"description":510,"useCases":251,"indexable":220},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":512,"label":513,"issuer":157,"region":152,"url":514,"description":515,"useCases":516,"indexable":220},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":518,"label":519,"issuer":520,"region":268,"url":521,"description":522,"useCases":523,"indexable":220},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":525,"label":526,"issuer":157,"region":152,"url":527,"description":528,"useCases":523,"indexable":220},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":530,"label":531,"issuer":157,"region":152,"url":532,"description":533,"useCases":523,"indexable":220},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":147,"label":535,"issuer":536,"region":418,"url":537,"description":538,"useCases":523,"indexable":220},"Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":540,"label":541,"issuer":542,"region":268,"url":543,"description":544,"useCases":545,"indexable":220},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":547,"label":548,"issuer":549,"region":152,"url":550,"description":551,"useCases":552,"indexable":220},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":554,"label":555,"issuer":157,"region":152,"url":556,"description":557,"useCases":552,"indexable":220},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":559,"label":560,"issuer":157,"region":152,"url":561,"description":562,"useCases":552,"indexable":220},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":564,"label":565,"issuer":452,"region":196,"url":566,"description":567,"useCases":76,"indexable":220},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":569,"label":570,"issuer":571,"region":268,"url":572,"description":573,"useCases":574,"indexable":220},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":576,"label":577,"issuer":157,"region":152,"url":578,"description":579,"useCases":574,"indexable":220},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":581,"label":582,"issuer":583,"region":584,"url":585,"description":586,"useCases":587,"indexable":220},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",6,{"id":589,"label":590,"issuer":591,"region":152,"url":592,"description":593,"useCases":75,"indexable":220},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":595,"label":596,"issuer":157,"region":152,"url":597,"description":598,"useCases":332,"indexable":220},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":600,"label":601,"issuer":602,"region":152,"url":603,"description":604,"useCases":332,"indexable":220},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":606,"label":607,"issuer":608,"region":196,"url":609,"description":610,"useCases":342,"indexable":220},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":612,"label":613,"issuer":157,"region":152,"url":614,"description":615,"useCases":342,"indexable":220},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":617,"label":618,"issuer":452,"region":196,"url":619,"description":620,"useCases":342,"indexable":220},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":622,"label":623,"issuer":624,"region":268,"url":625,"description":626,"useCases":342,"indexable":220},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1792049862207]