[{"data":1,"prerenderedAt":578},["ShallowReactive",2],{"uc-construction-schedule-and-progress-tracking":3,"uc-regulations":359},{"useCase":4,"evidence":180,"blitsAiDeployments":248,"benchmarks":249,"indicative":264,"related":267,"indexability":357,"includeUnpublished":186},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":26,"autonomy":27,"adoptionStage":28,"problem":29,"problemStats":30,"howItWorks":31,"valueDrivers":32,"kpis":36,"indicativeValue":41,"macroEstimates":84,"feasibility":89,"implementation":101,"risk":145,"blitsAi":158,"faq":160,"related":173,"datePublished":174,"dateModified":174,"lastVerified":175,"changelog":176,"slug":179},"AI for construction schedule and progress tracking","Construction progress tracking","AI construction progress tracking software","AI matches site photos to the BIM model to track construction progress. NCC cut manual reporting time by 70 percent, Doxel by 95 percent at Layton Construction.","published","AI that tracks construction progress against the schedule and the BIM model by matching site photos, captured with a hardhat mounted camera on regular site walks, to the planned tasks and quantities, so site teams and owners see what is actually built, where trades are behind and where installed work does not match the design, without walking the site to check it by hand.",[12,13,14,15,16],"construction progress tracking software","AI site progress monitoring","automated construction progress reporting","BIM based progress tracking","percent complete tracking",[18],"real-estate",[20,21],"operations","analytics-and-reporting",[23,24],"computer-vision","anomaly-detection",[],"employee-facing","assist","early-adopters","A general contractor knows what should be built from the schedule and the BIM model, but not\nreliably what is actually built. The standard method is manual: a superintendent or project\nengineer walks the site, estimates the percentage complete of each trade by eye, and writes it up\nfor the weekly meeting. That estimate is subjective, it takes site staff away from managing the\nwork itself, and it usually surfaces a schedule slip or a quality issue only after it has already\ncost time to fix.\n\nThe effect compounds on a multi trade, multi floor project. Different superintendents judge\n\"80 percent complete\" differently, subcontractors have an incentive to round their own progress\nup, and nobody has a single, dated record of what a wall or a duct run looked like on a given day\nif a dispute over payment or defects comes up later.",[],"1. **Capture the site.** Workers walk their normal routes wearing a hardhat mounted camera, so\n   the site is photographed at a set cadence without anyone doing an extra task.\n2. **Build a digital twin.** The images are stitched and located in the building, then matched\n   automatically to the BIM model, element by element: which duct, partition, or fixture is\n   visible, and whether it is there yet.\n3. **Compare to the plan.** The detected state of each element is compared with the schedule and\n   the design, producing an objective percent complete per trade, per area and per task, plus a\n   list of items that were meant to be installed and are not, or that do not match the design.\n4. **Surface deviations.** The system flags trades falling behind pace, sequencing conflicts (for\n   example ceiling work starting before the ducts behind it are in), and installed work that\n   differs from the BIM model.\n5. **Act on it.** Superintendents and project managers use the dashboard in the weekly trade\n   meeting to compare each trade's measured pace of installed work against the schedule, and\n   owners use it to verify progress claims behind payment applications instead of relying on self\n   reported percentages. Keep the measurement at the level of the trade's output: using the same\n   pace data to rank or discipline a named individual worker moves the system toward the Annex\n   III employment category described under risk below.",[33,34,35],"employee-productivity","speed","risk-reduction",[37,38,39,40],"handling-time-reduction","error-reduction","productivity-gain","hours-saved",{"referenceOrg":42,"inputs":43,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A general contractor running 10 active projects averaging 40,000 square feet each",[44,50,57,65,72],{"key":45,"label":46,"low":47,"high":48,"unit":45,"note":49},"projects","Active projects tracked",5,15,"Editorial assumption for a mid size general contractor, replace with your own project count.",{"key":51,"label":52,"low":48,"high":53,"unit":54,"note":55,"sourceUrl":56},"hoursPerProjectPerWeek","Superintendent and engineer hours spent on manual progress tracking, per project per week",30,"hours per project per week","Doxel reports six superintendents and project engineers on one 82,000 square foot healthcare project spent a combined 60 hours a week on manual progress tracking before automation; scaled down to this reference project's average 40,000 square feet (60 times 40,000 divided by 82,000, about 29 hours, rounded to 30).","https://doxel.ai/blog/how-owners-can-drive-construction-forward",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63,"sourceUrl":64},"reductionShare","Share of manual tracking time removed",0.5,0.7,"fraction of tracking hours","Capped at NCC's reported 70% reduction in manual reporting time, the lower of the two reductions the evidence on this page reports; Doxel's higher reported 95% reduction at Layton Construction is not used, to keep the range conservative.","https://buildots.com/case-studies/ncc-partners-with-buildots/",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"costPerHour","Fully loaded cost of a superintendent or project engineer hour",60,100,"USD per hour","Editorial assumption, replace with your own fully loaded labor cost.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"weeksPerYear","Weeks worked per year",46,50,"weeks","Editorial assumption for an active construction calendar.","projects * hoursPerProjectPerWeek * reductionShare * costPerHour * weeksPerYear","USD","per year","Superintendent and engineer time cost avoided on manual progress tracking","Gross tracking labor cost avoided only. It leaves out the cost of the platform, the camera hardware, the one time BIM and schedule integration work, and any separate gains from fewer overbilling disputes or less rework, which the evidence on this page reports separately.",[85],{"statement":86,"sourceTitle":87,"sourceUrl":56,"year":88},"Doxel, citing McKinsey, states that using digital tools could improve overall construction productivity by 14 to 15 percent and reduce project costs by 4 to 6 percent.","How Owners Can Drive Construction Forward",2024,{"complexity":90,"complexityNote":91,"dataPrerequisites":92,"integrations":96},"medium","Capturing images is simple once site staff have the hardhat mounted camera. The work is in getting a BIM model that is actually kept current, mapping it cleanly to the schedule's tasks, and agreeing with subcontractors that the automated percent complete is the number the weekly meeting and payment applications will use.",[93,94,95],"A BIM model broken into elements the AI can track against, kept reasonably current","A construction schedule with tasks the detected progress can be matched to","A fixed cadence of site photo capture, by staff wearing a hardhat mounted camera",[97,98,99,100],"BIM authoring or viewing platform (for example Autodesk Revit or Navisworks)","Scheduling tool (for example Primavera P6, Microsoft Project or the contractor's own tool)","Site capture hardware and its upload pipeline (hardhat mounted camera)","Document control or project management platform for routing flagged issues and RFIs",{"steps":102,"guardrails":121,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":132},[103,106,109,112,115,118],{"title":104,"detail":105},"Start with one project and a clean BIM model","Pick a project where the BIM model is genuinely kept up to date and the trades are willing to be measured, and agree up front which trades and floors are in scope for the pilot.",{"title":107,"detail":108},"Map the schedule to trackable elements","Break the schedule's tasks down to the same level as the BIM elements the vision system detects (a duct run, a partition, a fixture), so a detected element maps to one task.",{"title":110,"detail":111},"Set the capture cadence and route","Fix how often and by what route the site is captured, so every area gets covered on a predictable schedule and gaps in coverage do not look like missing progress.",{"title":113,"detail":114},"Put the dashboard in the weekly trade meeting","Replace the subjective percent complete slide with the measured one, and agree with subcontractors in advance how disagreements between measured and self reported progress get resolved.",{"title":116,"detail":117},"Route deviations to an owner","Send sequencing conflicts and design mismatches to the trade or engineer who owns the area, with the photo evidence attached, rather than only surfacing them on a dashboard nobody acts on.",{"title":119,"detail":120},"Extend to more projects once the workflow sticks","Add projects once one team has changed how it runs its trade meetings around the data, not before, so the second project benefits from a working playbook rather than a repeated pilot.",[122,123,124,125],"Treat the automated percent complete as the primary number only after a defined period of running it alongside the manual estimate and reconciling differences","Keep a photo dated record of the evidence behind every reported percent complete, for payment application and dispute support","Mask or restrict access to imagery that incidentally captures workers, beyond what is needed to verify installed work","Require a human review before a flagged design mismatch is escalated to a stop work or rework instruction","Superintendents and project engineers decide what to do with a flagged deviation: the system measures and flags it, it does not instruct a trade to redo work or change the schedule. Owners and their quantity surveyors use the record as evidence, not as an automatic approval or rejection of a payment application.",[128,129,130,131],"Time site staff spend on manual progress tracking, before and after","Share of tasks completed on the plan (Percent Plan Complete or equivalent), before and after","Overbilling or billing disputes tied to disagreements over percent complete","Coverage, meaning the share of the site captured on the agreed cadence, so gaps are visible",[133,136,139,142],{"title":134,"detail":135},"Stale BIM model","When the model is not kept current, the AI reports a mismatch that is really a documentation gap, not a site problem. Require model updates as part of the change process, not as an afterthought.",{"title":137,"detail":138},"Capture gaps read as missing work","A missed walk or a blocked camera route can look like work has not started. Track and display capture coverage alongside progress, not just the progress number.",{"title":140,"detail":141},"Dashboard nobody acts on","Data with no owner and no place in the weekly meeting gets ignored. Assign an owner for every flagged deviation and review coverage and open flags every week.",{"title":143,"detail":144},"Disputed percent complete undermines adoption","If subcontractors are not told in advance how the measured number will be used, a lower automated figure than their own estimate becomes a fight instead of a fix. Agree the reconciliation process before go live.",{"euAiAct":146,"regulations":149,"guidance":152,"controls":153,"incidents":157},{"tier":147,"basis":148},"context-dependent","Tracking built quantities against a BIM model and schedule is not itself an Annex III use. Annex III point 4 covers employment, the management of workers and access to work as a self employed person, and brings a system into that category under point 4(b): AI used to make decisions affecting work related relationships, or, quoting the regulation, \"to monitor and evaluate the performance and behaviour of persons in such relationships\", meaning employees, subcontracted individuals and others in a work related relationship with the organization. Measuring the quantities and pace of the work itself, without naming or ranking a crew or an individual, stays out of that category. This page's own weekly meeting step (howItWorks, step 5) keeps the measurement at the level of the trade's output for exactly this reason: using the same per trade pace data to evaluate or discipline a named crew member or subcontracted individual would stretch the system into that high risk category. Treat any such individual level use as high risk and apply the controls below to it, rather than relying on element level scoping alone.",[150,151],"gdpr","eu-ai-act",[],[154,155,156],"Written scope statement that the system measures installed work, not individual worker performance","Data retention and access limits on site imagery, since workers appear in it incidentally","Change control on the BIM to schedule mapping, since it decides what \"on plan\" means for a trade",[],{"howToBuild":159},"Blits.ai does not do the computer vision matching of site photos to a BIM model; that stays\nwith a specialist platform. What Blits.ai adds is the layer that turns the detected deviations\ninto something a project team acts on. A **custom function** pulls the day's flagged\ndeviations and coverage data from the progress tracking platform's API, and an **agentic\nworkflow**, triggered on a schedule, turns each one into a plain English note (what element,\nwhat trade, how far behind or how it differs from the model) with the evidence attached, then\nroutes it with **human in the loop approval** to the superintendent or engineer who owns that\narea before anything is escalated further.\n\nA **knowledge base** holding the project's schedule, specifications and past RFIs lets the\nagent explain why a sequence matters (\"ceiling cannot close until this duct is signed off\") when\nit routes a flag. An **agentic task**, condition triggered with a scheduled recheck, watches\nfor capture coverage gaps and stalled trades and alerts the project manager through the send\nemail flow block or a custom function, and **analytics** dashboards show the workflow's run\nhistory and flag resolution counts over time, so the team can see whether it is actually\nreducing the weekly reporting burden it targets.",[161,164,167,170],{"question":162,"answer":163},"Does this replace the superintendent's judgment?","No. It replaces the manual estimate of percent complete with a measured one, and flags deviations for a human to act on. NCC's own account is that it freed time from arguing with subcontractors so staff could spend it actually fixing problems.",{"question":165,"answer":166},"What has to be true before this works?","A BIM model that is genuinely kept current, and a schedule broken down to the same level of detail as the elements the system tracks. Without both, the flagged mismatches mostly reflect a stale model, not a real site problem.",{"question":168,"answer":169},"Is this only for the general contractor, or does the owner get value too?","The evidence on this page comes from contractor deployments: Doxel's Layton Construction case study reports a 10 percent reduction in overbilling, credited to precise progress tracking that eliminated disputes over the percent complete, and Buildots' NCC case study reports fewer conflicts that could have caused an overspend of tens of thousands of euros. Neither source says which party was overbilling or on which side an averted overspend would have landed. An owner still gets a related benefit from the same measured percent complete: this page's howItWorks step 5 describes owners using the dashboard to verify progress claims behind payment applications instead of relying on self reported percentages, though no evidence record on this page quantifies that owner side use.",{"question":171,"answer":172},"Does it also check safety and quality, or only schedule progress?","Some platforms extend the same site imagery to installation quality checks against the BIM model as a byproduct, as NCC Finland reported. Treat that as a bonus of the same capture, not a substitute for a dedicated safety monitoring program.",[],"2026-09-30","2026-09-29",[177],{"date":174,"note":178},"First published","construction-schedule-and-progress-tracking",[181,219],{"title":182,"useCases":183,"organization":184,"vendors":189,"summary":193,"stage":194,"year":88,"channels":195,"languages":196,"metrics":198,"outcomeDisclosed":209,"sources":210,"verification":214,"grade":216,"id":217,"organizationSlug":218},"Layton Construction: Doxel AI progress tracking on a healthcare project",[179],{"name":185,"anonymized":186,"country":187,"region":188,"industry":18},"Layton Construction",false,"US","north-america",[190],{"name":191,"role":192},"Doxel","platform","Layton Construction, a US general contractor, used Doxel's AI progress tracking platform on an 82,000 square foot healthcare facility project. Before Doxel, six superintendents and project engineers spent a combined 60 hours a week manually tracking progress; Doxel cut that to 3 hours and reduced overbilling by 10 percent through precise progress tracking that eliminated disputes over the percent complete.","production",[],[197],"en",[199,206],{"kpi":39,"value":200,"unit":201,"qualifier":202,"period":203,"claimant":204,"quote":205,"sourceUrl":56},95,"percent","exact","On an 82,000 square foot healthcare facility project","vendor","On a recent 82,000 SqFt healthcare facility project, six superintendents and project engineers were initially spending a combined 60 hours per week manually tracking progress. With Doxel's technology, this time was slashed by 95%, reducing the task to just 3 hours total.",{"kpi":38,"value":207,"unit":201,"qualifier":202,"claimant":204,"quote":208,"sourceUrl":56},10,"Doxel enabled a 10% reduction in overbilling by providing precise progress tracking, simplifying billing processes, and eliminating disputes over the percent complete.",true,[211],{"url":56,"title":87,"publisher":191,"date":212,"archivedUrl":213},"2024-08-05","https://web.archive.org/web/20251005115615/https://doxel.ai/blog/how-owners-can-drive-construction-forward",{"level":215,"checkedAt":175},"source-verified","C","layton-construction-doxel-progress-tracking",null,{"title":220,"useCases":221,"organization":222,"vendors":226,"summary":229,"stage":194,"year":230,"channels":231,"languages":232,"metrics":233,"outcomeDisclosed":209,"sources":237,"verification":246,"grade":216,"id":247,"organizationSlug":218},"NCC Finland: AI progress tracking against BIM with Buildots",[179],{"name":223,"anonymized":186,"country":224,"region":225,"industry":18},"NCC","FI","europe",[227],{"name":228,"role":192},"Buildots","NCC, one of the largest construction companies in the Nordic region, piloted Buildots' hardhat mounted camera and AI platform on Helsinki building projects, in a pilot announced in February 2022, to replace manual, subjective percent complete tracking with a measured comparison against its BIM model. The partnership expanded to four projects totalling 68,500 square metres, and Buildots reports large cuts in NCC's manual reporting time and more tasks completed on plan.",2022,[],[197],[234],{"kpi":39,"value":235,"unit":201,"qualifier":202,"claimant":204,"quote":236,"sourceUrl":64},70,"70% reduction in manual reporting.",[238,241],{"url":64,"title":239,"publisher":228,"archivedUrl":240},"NCC sees 230% boost to site performance via AI progress tracking","https://web.archive.org/web/20260415215424/https://buildots.com/case-studies/ncc-partners-with-buildots/",{"url":242,"title":243,"publisher":244,"date":245},"https://www.prnewswire.com/il/news-releases/buildots-ai-models-piloted-by-nordic-construction-giant-nccs-finnish-operations-in-two-helsinki-building-projects-301484594.html","Buildots' AI Models Piloted by Nordic Construction Giant NCC's Finnish Operations in Two Helsinki Building Projects","PR Newswire","2022-02-17",{"level":215,"checkedAt":175},"ncc-buildots-progress-tracking",0,[250,258],{"kpi":39,"label":251,"unit":201,"aggregate":209,"higherIsBetter":209,"n":252,"nUpTo":248,"median":253,"min":235,"max":200,"byClaimant":254,"vendorOnly":209,"points":255},"Productivity gain",2,82.5,{"organization":248,"vendor":252,"regulator":248,"independent":248},[256,257],{"evidenceId":217,"organization":185,"value":200,"qualifier":202,"claimant":204,"grade":216,"pooled":209},{"evidenceId":247,"organization":223,"value":235,"qualifier":202,"claimant":204,"grade":216,"pooled":209},{"kpi":38,"label":259,"unit":201,"aggregate":209,"higherIsBetter":209,"n":260,"nUpTo":248,"median":207,"min":207,"max":207,"byClaimant":261,"vendorOnly":209,"points":262},"Error reduction",1,{"organization":248,"vendor":260,"regulator":248,"independent":248},[263],{"evidenceId":217,"organization":185,"value":207,"qualifier":202,"claimant":204,"grade":216,"pooled":209},{"low":265,"high":266},103500,1575000,[268,287,314,336],{"slug":269,"title":270,"shortTitle":271,"definition":272,"status":9,"industries":273,"functions":275,"patterns":276,"audience":278,"autonomy":27,"adoptionStage":28,"segment":279,"evidenceCount":280,"publicEvidenceCount":280,"organizations":281,"bestGrade":216,"headline":218,"lastVerified":286,"indexable":209},"smart-meter-analytics","AI analytics for smart meter and AMI data","Smart meter analytics","AI that turns the flood of readings from smart electricity, gas and water meters into usable information: it monitors meter and network health at scale, estimates which appliances drive a household's usage from the meter signal alone, flags unusual consumption, and targets efficiency and electrification programmes at the customers who will benefit most, instead of a utility treating every meter and every customer the same way.",[274],"energy-and-utilities",[20,21],[24,277],"prediction-and-scoring","back-office","metering-and-billing",4,[282,283,284,285],"Arizona Public Service (APS)","Consolidated Edison (Con Edison)","NV Energy","Southern California Gas Company (SoCalGas)","2026-09-28",{"slug":288,"title":289,"shortTitle":290,"definition":291,"status":9,"industries":292,"functions":294,"patterns":295,"audience":26,"autonomy":27,"adoptionStage":28,"segment":297,"evidenceCount":298,"publicEvidenceCount":298,"organizations":299,"bestGrade":307,"headline":308,"lastVerified":286,"indexable":209},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI powered operations center that predicts patient admissions, discharges and transfers across a hospital or health system, and helps a team of coordinators sitting in one room sequence real time bed assignments, staffing levels and patient moves, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[293],"healthcare",[20,21],[277,296,24],"classification-and-routing","hospital operations",7,[300,301,302,303,304,305,306],"Children's Mercy Kansas City","Humber River Health","Johns Hopkins Medicine","King Faisal Specialist Hospital and Research Centre (KFSHRC)","Providence Swedish","The Queen's Health Systems","Tampa General Hospital","B",{"kpi":309,"label":310,"unit":201,"n":298,"nUpTo":248,"kind":311,"value":312,"qualifier":202,"claimant":313,"organization":218,"vendorReported":186},"processing-time-reduction","Cycle time reduction","median",34,"organization",{"slug":315,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":321,"patterns":322,"audience":278,"autonomy":323,"adoptionStage":324,"evidenceCount":325,"publicEvidenceCount":325,"organizations":326,"bestGrade":307,"headline":218,"lastVerified":335,"indexable":209},"retail-demand-forecasting-and-replenishment","AI demand forecasting and automated replenishment for retail","Demand forecasting and replenishment","Machine learning that forecasts demand for every item in every store or fulfillment center, day by day, from sales history, promotions, prices, weather and local events, and turns the forecast into automatic store and warehouse orders within limits set by planners, who handle the exceptions.",[320],"retail-and-ecommerce",[20,21],[277,24],"supervised-agent","mainstream",8,[327,328,329,330,331,332,333,334],"Albert Heijn","Best Buy Canada","Dollar General","Morrisons","One Stop","OXXO","Philippine Seven Corporation","Walmart","2026-09-27",{"slug":337,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":344,"patterns":346,"audience":278,"autonomy":349,"adoptionStage":350,"segment":351,"evidenceCount":280,"publicEvidenceCount":352,"organizations":353,"bestGrade":307,"headline":218,"lastVerified":335,"indexable":209},"portfolio-drift-monitoring-and-rebalancing","AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[342,343],"wealth-and-asset-management","banking",[20,345,21],"risk-management",[24,347,277,348],"agentic-workflow","content-generation","copilot","emerging","middle-office",3,[354,355,356],"Morgan Stanley","SimCorp","Vanguard",{"indexable":209,"reasons":358},[],[360,366,371,379,385,392,398,404,412,419,426,432,438,444,451,457,463,470,475,481,488,495,500,505,510,516,521,526,532,537,544,550,556,562,567,572],{"id":151,"label":361,"issuer":362,"region":225,"url":363,"description":364,"useCases":365,"indexable":209},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",250,{"id":150,"label":367,"issuer":362,"region":225,"url":368,"description":369,"useCases":370,"indexable":209},"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.",223,{"id":372,"label":373,"issuer":374,"region":375,"url":376,"description":377,"useCases":378,"indexable":209},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":380,"label":381,"issuer":382,"region":188,"url":383,"description":384,"useCases":200,"indexable":209},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",{"id":386,"label":387,"issuer":388,"region":225,"url":389,"description":390,"useCases":391,"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.",73,{"id":393,"label":394,"issuer":362,"region":225,"url":395,"description":396,"useCases":397,"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.",67,{"id":399,"label":400,"issuer":401,"region":225,"url":402,"description":403,"useCases":76,"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.",{"id":405,"label":406,"issuer":407,"region":408,"url":409,"description":410,"useCases":411,"indexable":209},"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":413,"label":414,"issuer":415,"region":408,"url":416,"description":417,"useCases":418,"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":420,"label":421,"issuer":422,"region":375,"url":423,"description":424,"useCases":425,"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.",22,{"id":427,"label":428,"issuer":429,"region":188,"url":430,"description":431,"useCases":425,"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":433,"label":434,"issuer":362,"region":225,"url":435,"description":436,"useCases":437,"indexable":209},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":439,"label":440,"issuer":441,"region":225,"url":442,"description":443,"useCases":437,"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.",{"id":445,"label":446,"issuer":447,"region":188,"url":448,"description":449,"useCases":450,"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.",16,{"id":452,"label":453,"issuer":454,"region":375,"url":455,"description":456,"useCases":48,"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.",{"id":458,"label":459,"issuer":362,"region":225,"url":460,"description":461,"useCases":462,"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":464,"label":465,"issuer":466,"region":188,"url":467,"description":468,"useCases":469,"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":471,"label":472,"issuer":362,"region":225,"url":473,"description":474,"useCases":469,"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.",{"id":476,"label":477,"issuer":478,"region":188,"url":479,"description":480,"useCases":469,"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":482,"label":483,"issuer":484,"region":375,"url":485,"description":486,"useCases":487,"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.",12,{"id":489,"label":490,"issuer":491,"region":188,"url":492,"description":493,"useCases":494,"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.",11,{"id":496,"label":497,"issuer":362,"region":225,"url":498,"description":499,"useCases":494,"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.",{"id":501,"label":502,"issuer":362,"region":225,"url":503,"description":504,"useCases":494,"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":506,"label":507,"issuer":362,"region":225,"url":508,"description":509,"useCases":494,"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":511,"label":512,"issuer":513,"region":225,"url":514,"description":515,"useCases":207,"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.",{"id":517,"label":518,"issuer":407,"region":408,"url":519,"description":520,"useCases":207,"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.",{"id":522,"label":523,"issuer":362,"region":225,"url":524,"description":525,"useCases":207,"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":527,"label":528,"issuer":529,"region":188,"url":530,"description":531,"useCases":298,"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.",{"id":533,"label":534,"issuer":362,"region":225,"url":535,"description":536,"useCases":298,"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":538,"label":539,"issuer":540,"region":541,"url":542,"description":543,"useCases":47,"indexable":209},"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":545,"label":546,"issuer":547,"region":225,"url":548,"description":549,"useCases":280,"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.",{"id":551,"label":552,"issuer":553,"region":225,"url":554,"description":555,"useCases":280,"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":557,"label":558,"issuer":559,"region":408,"url":560,"description":561,"useCases":352,"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.",{"id":563,"label":564,"issuer":362,"region":225,"url":565,"description":566,"useCases":352,"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":568,"label":569,"issuer":362,"region":225,"url":570,"description":571,"useCases":352,"indexable":209},"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":573,"label":574,"issuer":575,"region":188,"url":576,"description":577,"useCases":352,"indexable":209},"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.",1790783077943]