[{"data":1,"prerenderedAt":579},["ShallowReactive",2],{"uc-fibre-network-route-and-build-planning":3,"uc-regulations":354},{"useCase":4,"evidence":178,"blitsAiDeployments":275,"benchmarks":276,"indicative":283,"related":286,"indexability":352,"includeUnpublished":184},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":44,"macroEstimates":78,"feasibility":79,"implementation":92,"risk":127,"blitsAi":154,"faq":156,"related":169,"datePublished":173,"dateModified":173,"lastVerified":173,"changelog":174,"slug":177},"AI for fibre network route and build planning","Fibre route and build planning","AI fibre network route planning","AI designs and costs fibre routes. Telefónica uses it in planning; Deutsche Telekom's data partner reports planning and approval about 75% faster.","published","AI that turns geospatial survey data, existing infrastructure records and historical civil works costs into a proposed fibre route and a cost estimate, so planners spend less time drawing routes by hand and get a more consistent, defensible estimate before construction starts.",[12,13,14,15,16],"AI fibre planning","FTTH network design automation","fibre route optimization","automated fibre rollout planning","geospatial network planning AI",[18],"telecommunications",[20,21],"network-operations","operations",[23,24],"computer-vision","prediction-and-scoring",[26,27],"internal-tools","api","employee-facing","copilot","emerging","network","Building a fibre network means choosing, street by street, where cable goes: whether to reuse an\nexisting duct, dig a new trench, or string cable along poles, and whether to route around a river,\na protected tree or an area where paving would be expensive to dig up and restore. Telefónica\nSpain describes the traditional process as complex, with many variables that were difficult to\npredict, and says conventional tools were not always able to identify physical obstacles such as\nrivers or hard to reach areas, which increased execution costs.\n\nDeutsche Telekom makes the trade off explicit: \"the shortest route to the customer is not always\nthe most economical.\" Paving stones take longer to dig up and restore than a dirt road, and effort\nand cost depend on what is already there; roots near a tree change what a crew can safely do.\nWorking this out by hand, area by area, from survey notes and a\nplanner's experience, does not scale to a national rollout that has to pass millions of homes on a\ntight timeline, and inconsistent estimates lead to budget overruns and rework once construction\nstarts.",[],"1. **Collect the ground truth.** A survey vehicle or aerial and satellite imagery captures the\n   terrain: 360 degree panoramas, laser scan point clouds and existing duct, pole and cabinet\n   locations, referenced to GPS coordinates.\n2. **Classify what is in the data.** A computer vision model trained on the imagery and point\n   clouds recognizes surfaces and obstacles (asphalt, cobblestones, gravel, trees, buildings), so\n   the planning step does not start from raw pixels.\n3. **Generate and cost candidate routes.** An optimization model proposes one or more routes\n   between the network and the homes to connect, reusing existing infrastructure where possible\n   and minimizing new civil works, and estimates the cost from the route's characteristics and\n   the cost of comparable past projects in similar terrain.\n4. **Rank against the rollout plan.** Where many areas compete for the same construction crews and\n   budget, the system can also weigh which routes bring homes online soonest for the investment.\n5. **A planner reviews and approves.** The proposed route and cost estimate go to a human planner,\n   who checks it against local knowledge, adjusts where needed and approves it before it becomes a\n   construction work order.",[36,37,38],"speed","employee-productivity","risk-reduction",[40,41,42,43],"processing-time-reduction","cost-reduction","error-reduction","forecast-accuracy",{"referenceOrg":45,"inputs":46,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"A fibre operator planning 500,000 new homes passed a year",[47,53,60,67],{"key":48,"label":49,"low":50,"high":50,"unit":51,"note":52},"homesPerYear","New homes passed planned per year",500000,"homes per year","The reference operator.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"plannerHoursPerThousand","Planner hours to design routes for 1,000 homes, without AI assistance",40,80,"planner hours per 1,000 homes","Editorial assumption, replace with your own time studies. Steep's case study for Deutsche Telekom says planning and approval processes used to take several months.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"timeSaved","Share of planning and approval time the assistance saves",0.3,0.6,"fraction of planning time","Conservative against the deployment evidence on this page (Deutsche Telekom's technology partner reports planning and approval about 75% faster once its geospatial data pipeline was in place), because that figure covers the whole data platform at scale, not automated route determination on its own, and this range should also fit an early rollout.",{"key":68,"label":69,"low":56,"high":70,"unit":71,"note":72},"costPerPlannerHour","Fully loaded cost of a network planning engineer",70,"USD per hour","Editorial assumption for a mid to senior network planning role. Replace with your own cost.","(homesPerYear / 1000) * plannerHoursPerThousand * timeSaved * costPerPlannerHour","USD","per year","Planner time cost avoided","Gross planner time saved only. It leaves out the cost of building and running the AI pipeline (survey vehicles or imagery, model training, integration with GIS and costing systems), any change in civil works cost accuracy, and the value of passing homes sooner.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":87},"high","The hard part is not the routing algorithm, it is the data: accurate, current geospatial imagery or point clouds, a reliable existing infrastructure and asset inventory, and enough historical project cost data by terrain type to make estimates trustworthy.",[83,84,85,86],"Geospatial survey data (aerial or satellite imagery, or vehicle mounted panoramas and laser scans) covering the planning area","An existing infrastructure and asset inventory (ducts, poles, cabinets, exchanges) that is kept current","Historical civil works costs by area and terrain type, with enough volume to estimate from","Local exclusion rules (protected sites, environmentally sensitive land, heritage paving)",[88,89,90,91],"GIS or geospatial mapping platform","Civil works costing or ERP system","Network asset inventory database","Permitting and municipal approval workflow",{"steps":93,"guardrails":109,"humanInTheLoop":114,"kpisToInstrument":115,"failureModes":120},[94,97,100,103,106],{"title":95,"detail":96},"Start with one region and one clean data source","Pick a bounded rollout area and one reliable data source (an existing GIS layer or a single survey pass) before trying to cover a whole country, so data quality problems surface early and small.",{"title":98,"detail":99},"Ground routes in real infrastructure and real costs","Feed the model the actual duct, pole and cabinet inventory and historical civil works costs for comparable terrain, not just a map. A route that looks efficient on a satellite image can still be the expensive one to build.",{"title":101,"detail":102},"Keep an editable obstacle and exclusion list","Rivers, protected trees, heritage paving and other no go or extra cost areas should be a list a planner can add to, not something buried in model weights, so local knowledge fixes the model's blind spots quickly.",{"title":104,"detail":105},"Require planner sign off before any route becomes a work order","The system proposes; a named planner approves. Deutsche Telekom's own account of its pilot describes exactly this: a planner double checks and approves the route before it is used.",{"title":107,"detail":108},"Track estimate against actual, per job","Compare the AI's route length and cost estimate against what construction actually spent and built, and use the gap to improve the model and to flag terrain types it handles poorly.",[110,111,112,113],"Planner approval required before a recommended route becomes a construction work order","An exclusion and obstacle list a human owns and can edit, covering protected and environmentally sensitive areas","Reconciliation of the infrastructure data the model uses against a physical audit sample, so stale records do not drive routes into ducts or poles that no longer exist","Version control on the routing model and the data it was trained on, so a bad estimate can be traced back","A named planner reviews and approves every recommended route and cost estimate before it becomes a construction work order, and can override it with local knowledge the model does not have. On a portfolio of routes, planners also review a sample of accepted recommendations against the obstacle list to catch model blind spots before they reach many jobs.",[116,117,118,119],"Estimated versus actual civil works cost, per job","Planning and approval cycle time, from data collection to approved route","Share of recommended routes a planner approves without changing","Change orders raised during construction that the plan did not anticipate",[121,124],{"title":122,"detail":123},"Stale infrastructure data","The model routes through a duct, pole or cabinet that was never recorded or has since been removed, causing rework once construction starts. Reconcile the asset inventory against a physical audit sample before trusting it at scale.",{"title":125,"detail":126},"Cost estimates that do not transfer across terrain","A model trained mostly on urban routes underestimates rural terrain costs, or the other way round. Segment training data by area type and monitor estimate error by region before widening the rollout.",{"euAiAct":128,"regulations":131,"guidance":136,"controls":147,"incidents":153},{"tier":129,"basis":130},"minimal","Designing and costing a proposed fibre route is planning for new construction, not the management or operation of an existing network, and it does not decide anything about an individual person. It is not a use listed in Annex III. Annex III point 2 (safety components in the management and operation of critical digital infrastructure) targets systems that are safety components in running live infrastructure; a route and cost planning assistant for new construction is not part of managing or operating that infrastructure, so it does not fall under it. Because it plans investment in a public electronic communications network, general obligations for network operators under the NIS2 Directive still apply to the operator, separately from the AI Act.",[132,133,134,135],"eu-ai-act","nis2","iso-42001","gdpr",[137,143],{"title":138,"issuer":139,"region":140,"url":141,"note":142},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 2 covers safety components in the management and operation of critical digital infrastructure; planning and costing new construction, rather than a component that manages or operates live infrastructure, is outside that test.",{"title":144,"issuer":139,"region":140,"url":145,"note":146},"Directive (EU) 2022/2555 (NIS2)","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Sets cybersecurity risk management obligations for essential and important entities, including public electronic communications network operators, covering the systems and data they run, separately from any AI specific duty.",[148,149,150,151,152],"Planner sign off recorded against every route before it becomes a work order","An accountable engineering owner for the exclusion and obstacle list","A documented review cadence comparing estimated and actual cost and route length","Change control and version tracking for the model and its training data","Automated anonymization of people and vehicles in survey imagery and point clouds before storage or review, under GDPR",[],{"howToBuild":155},"On Blits.ai this is an **agentic workflow** rather than a chat agent: a run triggered on a\nschedule or by API pulls survey and asset data through **custom functions** (REST or SQL calls)\nagainst the operator's own GIS, terrain classification and route optimization services, since\nthe terrain classification and route optimization themselves run in the operator's own\nspecialist models, called as tools rather than rebuilt on Blits.ai. Local exclusion rules and\npermitting notes live in a **knowledge base** with **hybrid retrieval**, so the workflow grounds\nits recommendation in the operator's own rules rather than general knowledge, and a **SQL\nknowledge base** connection lets it query structured cost and asset tables directly instead of\nrelying on exports.\n\nThe workflow stops for **human in the loop approval** before a proposed route reaches a work\norder system, with a **tool execution policy** controlling which of the operator's systems it\nmay call. Every run is kept in **run history** with a full **audit trail**, and **guardrails**\napply input and output content checks to what the workflow reads and proposes. For operators\nwith EU or Gulf data residency requirements, the workflow can run in the matching **EU or UAE\nregion**.",[157,160,163,166],{"question":158,"answer":159},"How much faster is AI assisted fibre route planning?","It depends heavily on how mature the data pipeline is. Deutsche Telekom's technology partner reports that once its geospatial data platform was in place, planning and approval that used to take several months came down to about 75% faster, finishing within a few weeks, after planning more than 10 million households and connecting more than 8 million of them to FTTH. That figure covers the whole data platform, not AI route determination alone. Telefónica Spain, which said in December 2025 the tool was already being used by its planning teams, describes its deployment as reducing human error and accelerating service availability, without publishing a percentage.",{"question":161,"answer":162},"Does this replace network planners?","Deutsche Telekom's account of its pilot says a planner double checks and approves the route before it is used. Telefónica says the algorithm is already being used by its planning teams and, in its own words, \"multiplies the capacity of our teams\". The AI narrows a large search space and produces a consistent first estimate; the planner still owns the decision.",{"question":164,"answer":165},"Is this the same as mobile network capacity planning?","No. This use case plans where to build new fixed line, mostly fibre, infrastructure and what it will cost. Forecasting mobile data traffic and tuning radio parameters on an existing network is a related but separate job, covered on the network planning and capacity optimization page.",{"question":167,"answer":168},"What data does an operator need before starting?","Reasonably current geospatial imagery or point clouds of the planning area, an existing infrastructure inventory that is kept up to date, and a useful history of past project costs by terrain type. Without the last two, the AI can still draw a plausible route but its cost estimate will not be trustworthy.",[170,171,172],"network-planning-and-capacity-optimization","predictive-network-maintenance","field-technician-copilot-and-dispatch","2026-09-29",[175],{"date":173,"note":176},"First published","fibre-network-route-and-build-planning",[179,205,231],{"title":180,"useCases":181,"organization":182,"vendors":186,"summary":187,"stage":188,"year":189,"channels":190,"languages":191,"metrics":193,"outcomeDisclosed":184,"sources":194,"verification":200,"grade":202,"id":203,"organizationSlug":204},"Telefónica Spain: AI genetic algorithm for FTTH route planning",[177],{"name":183,"anonymized":184,"country":185,"region":140,"industry":18},"Telefónica España",false,"ES",[],"Telefónica Spain built an AI based on genetic algorithms that designs fibre deployment routes from geographical coordinates, estimating cost and reusing existing infrastructure where possible. The company says the tool is already being used by its planning teams and has reached what it calls level 4 autonomy in the fibre planning process, under its Autonomous Network Journey program. No quantified time or cost saving is published.","production",2025,[26],[192],"es",[],[195],{"url":196,"title":197,"publisher":198,"date":199},"https://www.telefonica.com/en/communication-room/blog/artificial-intelligence-revolutionizing-network-fiber-planning/","How Artificial Intelligence Is Revolutionizing Network Fiber Planning","Telefónica","2025-12-09",{"level":201,"checkedAt":173},"source-verified","B","telefonica-espana-ai-fibre-route-planning","telefonica-espana",{"title":206,"useCases":207,"organization":208,"vendors":211,"summary":215,"stage":188,"year":216,"channels":217,"languages":218,"metrics":220,"outcomeDisclosed":184,"sources":221,"verification":227,"grade":228,"id":229,"organizationSlug":230},"Openreach: Google Cloud digital twin for full fibre rollout planning",[177],{"name":209,"anonymized":184,"country":210,"region":140,"industry":18},"Openreach","GB",[212],{"name":213,"role":214},"Google Cloud","platform","Openreach has built a digital replica of the UK's transportation corridors on Google Cloud's Vertex AI, combining data on 35 million homes and businesses with road, rail and waterway networks and its existing broadband infrastructure, so planners can see where full fibre can be extended soonest. The same announcement names a reduction of upwards of 50% in \"time to insight\" from using Gemini Enterprise to convert legacy data queries, but that figure is about cloud engineering work, not the route planning itself, so it is not recorded as a metric for this use case. Openreach's managing director for fibre first, James Tappenden, said the collaboration brings practical, measurable benefits, from \"connecting more families to gigabit broadband faster\" to cutting vehicle emissions across its workforce.",2026,[26],[219],"en",[],[222],{"url":223,"title":224,"publisher":225,"date":226},"https://www.prnewswire.com/news-releases/openreach-taps-google-cloud-ai-to-accelerate-high-speed-internet-access-and-cut-carbon-302724333.html","Openreach Taps Google Cloud AI to Accelerate High-Speed Internet Access and Cut Carbon","Google Cloud (PR Newswire)","2026-03-25",{"level":201,"checkedAt":173},"C","openreach-google-cloud-fibre-digital-twin",null,{"title":232,"useCases":233,"organization":234,"vendors":237,"summary":243,"stage":244,"year":245,"channels":246,"languages":247,"metrics":249,"outcomeDisclosed":258,"sources":259,"verification":272,"grade":228,"id":273,"organizationSlug":274},"Deutsche Telekom: AI route planning for the German fibre rollout",[177],{"name":235,"anonymized":184,"country":236,"region":140,"industry":18},"Deutsche Telekom","DE",[238,240],{"name":239,"role":214},"Steep (Fraunhofer IGD)",{"name":241,"role":242},"Fraunhofer IPM","integrator","Deutsche Telekom's own newsroom describes a pilot, first run in Bornheim near Bonn, in which a measuring vehicle with 360 degree cameras and laser scanners collects environmental data for its FTTH rollout, and software developed with Fraunhofer IPM automatically classifies around 30 categories of surface and obstacle (pavement type, trees, root structure) to determine the optimal cable route, which a Deutsche Telekom planner then checks and approves. A separate account from Fraunhofer IGD's Steep workflow platform, which has processed this imagery and point cloud data at scale in Deutsche Telekom's cloud since the collaboration began in 2017, says the program has since planned more than 10 million households and connected more than 8 million of them to FTTH, and that planning and approval work which used to take several months is now finished within a few weeks, on average about 75% faster. Steep credits that speedup to the whole geospatial data infrastructure it built for Deutsche Telekom, including large scale point cloud classification and the Fibre3D tool that lets planners present plans to municipalities with fewer site visits, not to automated AI route determination alone: Steep says the classification output \"can later be used to automatically determine optimal routes\", describing that as a future use rather than a capability it says is running at this scale today.","pilot",2018,[26],[248],"de",[250],{"kpi":40,"value":251,"unit":252,"qualifier":253,"baseline":254,"claimant":255,"quote":256,"sourceUrl":257},75,"percent","approximately","Planning and approval for a fibre rollout area used to take several months. Steep attributes the improvement to its whole geospatial data infrastructure and Fibre3D visualization tool for Deutsche Telekom, not to automated AI route determination alone.","vendor","Now they are on average about 75% faster and can be finished within a few weeks.","https://steep-wms.github.io/showcase/telekom/",true,[260,264,267],{"url":261,"title":262,"publisher":235,"archivedUrl":263},"https://www.telekom.com/en/media/media-information/archive/dt-uses-artificial-intelligence-for-fiber-optic-roll-out-544552","Deutsche Telekom uses artificial intelligence for fiber-optic roll-out","https://web.archive.org/web/2026/https://www.telekom.com/en/media/media-information/archive/dt-uses-artificial-intelligence-for-fiber-optic-roll-out-544552",{"url":257,"title":265,"publisher":266},"How Telekom made their fibre planning processes 75% faster with Steep","Fraunhofer IGD (Steep)",{"url":268,"title":269,"publisher":270,"date":271},"https://doi.org/10.1016/j.jss.2024.112008","A cloud based data processing and visualization pipeline for the fibre roll out in Germany","Journal of Systems and Software (Fraunhofer IGD team)","2024-05-01",{"level":201,"checkedAt":173},"deutsche-telekom-ai-fibre-route-planning","deutsche-telekom",0,[277],{"kpi":40,"label":278,"unit":252,"aggregate":258,"higherIsBetter":258,"n":279,"nUpTo":275,"median":251,"min":251,"max":251,"byClaimant":280,"vendorOnly":258,"points":281},"Cycle time reduction",1,{"organization":275,"vendor":279,"regulator":275,"independent":275},[282],{"evidenceId":273,"organization":235,"value":251,"qualifier":253,"claimant":255,"grade":228,"pooled":258},{"low":284,"high":285},240000,1680000,[287,311,326,340],{"slug":170,"title":288,"shortTitle":289,"definition":290,"status":9,"industries":291,"functions":292,"patterns":294,"audience":28,"autonomy":298,"adoptionStage":299,"segment":31,"evidenceCount":300,"publicEvidenceCount":300,"organizations":301,"bestGrade":202,"headline":305,"lastVerified":310,"indexable":258},"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.",[18],[20,293],"analytics-and-reporting",[24,295,296,297],"recommendation-and-personalization","anomaly-detection","agentic-workflow","supervised-agent","early-adopters",5,[235,302,303,183,304],"NTT DOCOMO","stc Group","Vodafone",{"kpi":40,"label":278,"unit":252,"n":279,"nUpTo":275,"kind":306,"value":307,"qualifier":308,"claimant":309,"organization":235,"vendorReported":184},"reported",95,"at-least","organization","2026-09-27",{"slug":171,"title":312,"shortTitle":313,"definition":314,"status":9,"industries":315,"functions":316,"patterns":318,"audience":319,"autonomy":298,"adoptionStage":299,"segment":31,"evidenceCount":320,"publicEvidenceCount":320,"organizations":321,"bestGrade":202,"headline":230,"lastVerified":310,"indexable":258},"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],[20,317,21],"field-service",[296,24,297],"back-office",6,[322,323,183,324,325,304],"KDDI","Orange","Telstra","Verizon",{"slug":172,"title":327,"shortTitle":328,"definition":329,"status":9,"industries":330,"functions":331,"patterns":333,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"evidenceCount":337,"publicEvidenceCount":337,"organizations":338,"bestGrade":202,"headline":230,"lastVerified":310,"indexable":258},"AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","AI that decides whether a fault needs a site visit at all, predicts what work and parts a job will need, 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],[317,21,332],"customer-service",[24,334,335,336],"rag-knowledge-assistant","conversational-agent","classification-and-routing",2,[339,209],"nbn",{"slug":341,"title":342,"shortTitle":343,"definition":344,"status":9,"industries":345,"functions":346,"patterns":347,"audience":28,"autonomy":29,"adoptionStage":299,"segment":31,"evidenceCount":320,"publicEvidenceCount":320,"organizations":349,"bestGrade":202,"headline":351,"lastVerified":310,"indexable":258},"network-fault-triage-copilot","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],[20,21],[296,336,334,348,297],"summarization",[350,235,322,323,324,304],"Bell Canada",{"kpi":40,"label":278,"unit":252,"n":279,"nUpTo":275,"kind":306,"value":307,"qualifier":308,"claimant":309,"organization":235,"vendorReported":184},{"indexable":258,"reasons":353},[],[355,360,365,372,380,387,393,400,408,415,422,429,433,439,446,453,459,466,472,478,484,491,496,503,508,513,518,524,531,536,543,550,556,563,568,573],{"id":132,"label":356,"issuer":139,"region":140,"url":357,"description":358,"useCases":359,"indexable":258},"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.",230,{"id":135,"label":361,"issuer":139,"region":140,"url":362,"description":363,"useCases":364,"indexable":258},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",207,{"id":134,"label":366,"issuer":367,"region":368,"url":369,"description":370,"useCases":371,"indexable":258},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":373,"label":374,"issuer":375,"region":376,"url":377,"description":378,"useCases":379,"indexable":258},"nist-ai-rmf","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.",92,{"id":381,"label":382,"issuer":383,"region":140,"url":384,"description":385,"useCases":386,"indexable":258},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":388,"label":389,"issuer":139,"region":140,"url":390,"description":391,"useCases":392,"indexable":258},"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":394,"label":395,"issuer":396,"region":140,"url":397,"description":398,"useCases":399,"indexable":258},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":401,"label":402,"issuer":403,"region":404,"url":405,"description":406,"useCases":407,"indexable":258},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":409,"label":410,"issuer":411,"region":404,"url":412,"description":413,"useCases":414,"indexable":258},"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":416,"label":417,"issuer":418,"region":376,"url":419,"description":420,"useCases":421,"indexable":258},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",22,{"id":423,"label":424,"issuer":425,"region":368,"url":426,"description":427,"useCases":428,"indexable":258},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":133,"label":430,"issuer":139,"region":140,"url":145,"description":431,"useCases":432,"indexable":258},"NIS2 Directive","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":434,"label":435,"issuer":436,"region":140,"url":437,"description":438,"useCases":432,"indexable":258},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":440,"label":441,"issuer":442,"region":376,"url":443,"description":444,"useCases":445,"indexable":258},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":447,"label":448,"issuer":449,"region":368,"url":450,"description":451,"useCases":452,"indexable":258},"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":454,"label":455,"issuer":139,"region":140,"url":456,"description":457,"useCases":458,"indexable":258},"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":460,"label":461,"issuer":462,"region":376,"url":463,"description":464,"useCases":465,"indexable":258},"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":467,"label":468,"issuer":469,"region":376,"url":470,"description":471,"useCases":465,"indexable":258},"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":473,"label":474,"issuer":139,"region":140,"url":475,"description":476,"useCases":477,"indexable":258},"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":479,"label":480,"issuer":481,"region":368,"url":482,"description":483,"useCases":477,"indexable":258},"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":485,"label":486,"issuer":487,"region":376,"url":488,"description":489,"useCases":490,"indexable":258},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":492,"label":493,"issuer":139,"region":140,"url":494,"description":495,"useCases":490,"indexable":258},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":497,"label":498,"issuer":499,"region":140,"url":500,"description":501,"useCases":502,"indexable":258},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",10,{"id":504,"label":505,"issuer":403,"region":404,"url":506,"description":507,"useCases":502,"indexable":258},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":509,"label":510,"issuer":139,"region":140,"url":511,"description":512,"useCases":502,"indexable":258},"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":514,"label":515,"issuer":139,"region":140,"url":516,"description":517,"useCases":502,"indexable":258},"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":519,"label":520,"issuer":139,"region":140,"url":521,"description":522,"useCases":523,"indexable":258},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",9,{"id":525,"label":526,"issuer":527,"region":376,"url":528,"description":529,"useCases":530,"indexable":258},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":532,"label":533,"issuer":139,"region":140,"url":534,"description":535,"useCases":320,"indexable":258},"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":537,"label":538,"issuer":539,"region":540,"url":541,"description":542,"useCases":300,"indexable":258},"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":544,"label":545,"issuer":546,"region":140,"url":547,"description":548,"useCases":549,"indexable":258},"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":551,"label":552,"issuer":553,"region":140,"url":554,"description":555,"useCases":549,"indexable":258},"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":557,"label":558,"issuer":559,"region":404,"url":560,"description":561,"useCases":562,"indexable":258},"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":564,"label":565,"issuer":139,"region":140,"url":566,"description":567,"useCases":562,"indexable":258},"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":569,"label":570,"issuer":139,"region":140,"url":571,"description":572,"useCases":562,"indexable":258},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":574,"label":575,"issuer":576,"region":376,"url":577,"description":578,"useCases":562,"indexable":258},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683490550]