[{"data":1,"prerenderedAt":565},["ShallowReactive",2],{"uc-crowd-density-and-flow-monitoring":3,"uc-regulations":336},{"useCase":4,"evidence":174,"blitsAiDeployments":264,"benchmarks":265,"indicative":266,"related":269,"indexability":334,"includeUnpublished":180},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":24,"channels":28,"audience":30,"autonomy":31,"adoptionStage":32,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":40,"indicativeValue":44,"macroEstimates":87,"feasibility":88,"implementation":100,"risk":138,"blitsAi":153,"faq":155,"related":168,"datePublished":169,"dateModified":169,"lastVerified":169,"changelog":170,"slug":173},"AI crowd density and flow monitoring for venues, transport hubs and pilgrimage sites","Crowd density and flow monitoring","AI crowd density and flow monitoring","AI counts people on camera feeds and flags crowding or movement against the flow. Saudi Hajj shows it at scale and the Paris 2024 trial shows where it struggles.","published","Crowd density and flow monitoring is AI that counts people on camera feeds and flags crowding, movement against the flow and congestion points so operators can act early. It estimates how many people are in a zone and which way they move, and leaves every decision, such as opening a gate, redirecting a stream or sending staff, to the operators in the control room.",[12,13,14,15,16],"AI crowd counting","crowd management AI","crowd analytics","crowd flow analytics","AI crowd monitoring",[18,19,20],"government","logistics-and-transportation","media-and-entertainment",[22,23],"security-operations","operations",[25,26,27],"computer-vision","anomaly-detection","prediction-and-scoring",[29],"internal-tools","employee-facing","assist","emerging","Crowd risk builds where many people converge on a narrow space: an entrance, a staircase, a\ncircumambulation area, a station platform. A control room can have hundreds of camera views and\nno way to count heads on each of them, and a head count from the floor arrives after the crowd\nhas formed. Operators tend to react to a crush they can see rather than to the density that\ncame before it.\n\nCounting is the established half of the technology: a model estimates how many people are in a\nzone and in which direction they move. Flagging a group moving against the stream, a sudden\ngathering or a sudden dispersal is the newer half, and the one the Paris trial found harder to\nassess. The output of either lands on a screen or an alert in the operations room. Public\nevidence comes from two very different settings. The Saudi authorities report large scale\noperation at Hajj and publish no accuracy figures. The evaluation committee for the French trial\nat the Paris 2024 Games, as quoted in the press, reported transport operators finding density\nanalysis satisfactory with reservations and crowd movement detection hard to assess.",[],"1. **Place and calibrate the cameras.** Counting accuracy depends on camera height and angle.\n   In the Paris trial the software struggled to count tightly packed people because of how high\n   the cameras were mounted. Some deployments add thermal imaging.\n2. **Estimate density per zone.** The model converts video into a people count or density value\n   for each zone, near real time, and totals zones into a site figure that planners can compare\n   with an expected attendance.\n3. **Track direction and speed.** Flow models follow movement to show a stream, a bottleneck or\n   a group moving against the stream, as the Baseer platform at the Grand Mosque does.\n4. **Compare with a threshold set by safety planners.** Each zone has a density level at which\n   staff should act, and the system raises an alert or a dashboard color when a zone approaches\n   it. Some systems also forecast where a surge will form.\n5. **Show the operator, who decides.** The alert links to the camera view. The operator checks\n   it and chooses the response: more stewards, a closed gate, a one way system or an\n   announcement.\n6. **Review afterwards.** Alerts, operator verdicts and any incident the system missed are\n   logged, so thresholds are tuned from the site's own data.",[37,38,39],"risk-reduction","speed","customer-experience",[41,42,43],"accuracy","response-time-reduction","detection-rate-improvement",{"referenceOrg":45,"inputs":46,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A convention and exhibition center with 60 cameras on public areas and about 120 event days a year",[47,54,61,68,75],{"key":48,"label":49,"low":50,"high":51,"unit":52,"note":53},"eventDays","Event days per year",100,140,"days per year","The reference venue.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"hoursPerDay","Hours per day with crowd areas watched",8,12,"hours per day","Editorial assumption, replace with your own opening hours.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"operatorsOnCrowdScreens","Operators watching crowd areas",2,4,"operators","Editorial assumption, replace with your own control room roster.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"shareOfWatchTimeSaved","Share of watching time replaced by automatic counts and alerts",0.1,0.25,"fraction of watching time","Editorial assumption, replace with your own. No public deployment reports a time saving, so this is kept low. Operators keep checking every alert, so this is time redeployed to other work more than a smaller roster.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"costPerHour","Fully loaded cost of a control room operator",30,50,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","eventDays * hoursPerDay * operatorsOnCrowdScreens * shareOfWatchTimeSaved * costPerHour","USD","per year","Annual control room watching time cost avoided","Operator time only. It leaves out the value of an incident avoided or caught earlier, the cost of cameras, licenses and integration, and the staff time spent on false alerts.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":95},"medium","The counting and flow model is bought from a video analytics vendor or built on a computer vision platform the venue licenses or develops itself. The venue's work is camera placement and coverage, a written density threshold for every zone, the link from alerts to the operations room, and a response plan for each alert level that the operators have rehearsed.",[92,93,94],"Camera coverage of entrances, queues, corridors and gathering areas with usable sightlines at the height they are mounted","A zone map with capacity figures and the density at which each zone needs action","Historical attendance and incident records to tune thresholds",[96,97,98,99],"Video management system or camera streams","Operations room displays and alerting tools","Ticketing or access control counts, to compare with the camera figures","Public address, signage and radio systems used for the response",{"steps":101,"guardrails":117,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":128},[102,105,108,111,114],{"title":103,"detail":104},"Start with the zones where crowds form first","Pick the entrances, queues or corridors where crowding has caused a problem before, and run the model beside the operators for a full event before any alert is used to act.",{"title":106,"detail":107},"Count against a manual reference","Take manual counts at set times in each zone and compare them with the model's figure, including tightly packed groups and the busiest minutes. The Paris trial found counting harder when people stood close together and cameras were high, so test those conditions on your own cameras.",{"title":109,"detail":110},"Write the response plan for each alert level","Decide in advance who is told, what they check and what they may do at each density level, and rehearse it. An alert nobody has a plan for only adds to the noise.",{"title":112,"detail":113},"Treat movement detection as a second stage","Density counts are the easier part to validate. Flow alerts such as movement against the stream need their own test cases, because the Paris committee reported the software sometimes mistaking groups walking together for a crowd movement.",{"title":115,"detail":116},"Review alert quality on a schedule","Sample confirmed alerts, false alerts and missed incidents each month, and retune thresholds from that record rather than after an incident.",[118,119,120,121],"Every alert goes to a trained operator who checks the camera view before any response that affects visitors","The system counts and measures movement; identifying or tracking named individuals is a separate capability that needs its own legal basis and assessment","Each alert is logged with the zone, the figure, the camera view and the operator's verdict","A manual fallback, such as stewards and manual counts, stays in place for every zone the system covers","An operator in the control room reviews every alert and chooses the response. The safety manager owns the thresholds and the false alert rate, and reviews both after each event and whenever the vendor changes the model or the cameras change.",[124,125,126,127],"Count error against manual counts, by zone and by crowd level","Confirmed alerts against false alerts, by alert type","Time from alert to the first response action","Incidents the system missed and found another way",[129,132,135],{"title":130,"detail":131},"Movement confused with groups walking together","The French evaluation committee, as quoted in the press, said the processing can mistake groups of people moving the same way without particular haste for a crowd movement, and that it is hard to define a rapid regrouping or dispersal. Test movement alerts on real footage from your site before staff rely on them.",{"title":133,"detail":134},"Undercounting in dense, high angle views","The same committee reported that the software sometimes had difficulty counting people packed tightly together because of the height at which cameras were placed. Check counts at the densest points and add a camera angle or a second source of counts where the error is largest.",{"title":136,"detail":137},"Figures that look precise without a published test","The Saudi platforms report camera numbers and the number of algorithms, but no count accuracy or incident result has been published for them. Ask a vendor for accuracy on crowded footage like yours, measured by someone other than the vendor.",{"euAiAct":139,"regulations":142,"guidance":146,"controls":147,"incidents":152},{"tier":140,"basis":141},"context-dependent","Counting people and estimating flow without identifying or categorizing individuals is not listed in Annex III. The tier changes with the design: remote biometric identification of people in a crowd falls under Annex III point 1(a), biometric categorization under point 1(b), and use by or on behalf of law enforcement to assess or profile individuals under point 6. Real time remote biometric identification in publicly accessible spaces for law enforcement is prohibited under Article 5(1)(h), with narrow listed exceptions. France ran its Paris 2024 trial under a national law that authorized eight listed situations, after an opinion from the data protection authority CNIL, according to the French press.",[143,144,145],"eu-ai-act","gdpr","sdaia-ai-ethics",[],[148,149,150,151],"A data protection impact assessment before cameras are linked to analytics, with a stated purpose limited to safety and flow","Counting and flow models that output aggregate figures and alerts, not identities","Retention limits for footage and analytics logs, and signage that tells visitors about camera analytics","Independent testing of count accuracy on the venue's own footage before an alert is used to act",[],{"howToBuild":154},"The camera counting and flow model stays on the video analytics platform the venue already\nruns. Blits.ai fits at the point where a density alert turns into words for the public and\ninto data for the safety manager. When a zone nears its threshold, an **agent** drafts the\nannouncement in each visitor language, Arabic included, from wording the safety manager has\napproved and stored in a **knowledge base**. An operator checks the draft and releases it\nthrough the **web chat** widget or **WhatsApp**, or reads it out over the public address\nsystem. Regional Arabic models such as Humain and Fanar are among the supported providers.\n\nThe second job is the per zone threshold review. A scheduled **workflow** queries a **SQL\nknowledge base** that holds every alert with its zone, figure and operator verdict, and\nreturns the confirmed and false alerts for each zone ahead of the next event. The safety\nmanager decides whether a threshold changes, and the workflow never changes one itself.\n\nThe analytics platform reaches Blits.ai through an **API token**. Each message waits for an\noperator through **human in the loop confirmation**, **guardrails** keep drafts to the\napproved wording and away from any named person, and the **run history** keeps a **full\naudit trail** of drafts, releases and verdicts. Agents can run on any model provider the\nvenue allows. Dedicated **EU and UAE** production environments exist, so check that one of\nthem matches where the venue's rules say alert and message data must stay.",[156,159,162,165],{"question":157,"answer":158},"Does AI crowd monitoring identify the people in a crowd?","Counting and flow models measure density and direction and do not need to know who anyone is. Some deployments add separate functions, for example the Baseer platform at the Grand Mosque is also reported to help locate missing persons, and how it does so is not described. Identification changes the legal tier and needs its own basis.",{"question":160,"answer":161},"How accurate are AI crowd counts?","No public deployment on this page publishes a count accuracy. The Paris evaluation committee, as quoted in the press, called density analysis satisfactory according to transport operators but with reservations: the software sometimes had difficulty counting people packed tightly together because of camera height. Measure accuracy on your own cameras against manual counts.",{"question":163,"answer":164},"Can AI predict a crowd surge before it happens?","According to SDAIA, as reported by the Bahrain News Agency, the Baseer and Sawaher platforms identify congestion points and predict crowd surges at Hajj. No lead time, hit rate or false alert rate has been published, so treat prediction as a claim to test. Movement detection was the weaker part of the Paris trial: the committee found real movements hard to tell from groups walking together.",{"question":166,"answer":167},"Who decides what to do when the system raises an alert?","An operator in the control room. In the Saudi and French deployments the output goes to operations rooms or command rooms, and staff choose the response. The system supplies counts and alerts; it does not open gates or move people.",[],"2026-10-14",[171],{"date":169,"note":172},"First published","crowd-density-and-flow-monitoring",[175,210,230],{"title":176,"useCases":177,"organization":178,"vendors":183,"summary":184,"stage":185,"year":186,"channels":187,"languages":188,"metrics":189,"outcomeDisclosed":180,"sources":190,"verification":205,"grade":207,"id":208,"organizationSlug":209},"SDAIA and Ministry of Interior: Baseer crowd monitoring at the Two Holy Mosques",[173],{"name":179,"anonymized":180,"country":181,"region":182,"industry":18},"Saudi Data and Artificial Intelligence Authority (SDAIA)",false,"SA","middle-east",[],"Baseer is a computer vision platform that the Saudi Data and Artificial Intelligence Authority built with the Ministry of Interior for the Grand Mosque in Makkah and, for Hajj 1447 (2026), the entrances of both holy mosques. Reporting from the Ramadan 2025 Umrah season says it tracks crowds, detects movement against the flow of pilgrims and helps find missing persons. In May 2026 SDAIA's spokesman said it uses computer vision and large language models and gives field commanders immediate analysis, and SDAIA told Al Arabiya English that it and Sawaher combine computer vision, thermal imaging and AI analytics to monitor crowd density and movement in real time. No detection or response figure has been published.","production",2025,[29],[],[],[191,196,201],{"url":192,"title":193,"publisher":194,"date":195},"https:\u002F\u002Fgulfnews.com\u002Fworld\u002Fgulf\u002Fsaudi\u002Fsaudi-arabia-ai-harnessed-to-regulate-pilgrims-inflow-into-mecca-1.500062270","Saudi Arabia: AI harnessed to regulate pilgrims' inflow into Mecca","Gulf News","2025-03-16",{"url":197,"title":198,"publisher":199,"date":200},"https:\u002F\u002Fenglish.aawsat.com\u002Fnode\u002F5275170","Saudi Arabia Expands Use of AI to Serve Hajj Pilgrims","Asharq Al-Awsat","2026-05-20",{"url":202,"title":203,"publisher":204},"https:\u002F\u002Fwww.gdnonline.com\u002FDetails\u002F1395704","Artificial intelligence transforms Haj experience","Gulf Daily News (Bahrain News Agency)",{"level":206,"checkedAt":169},"source-verified","C","sdaia-baseer-grand-mosque-crowd-monitoring",null,{"title":211,"useCases":212,"organization":213,"vendors":214,"summary":215,"stage":216,"year":186,"channels":217,"languages":218,"metrics":219,"outcomeDisclosed":180,"sources":220,"verification":228,"grade":207,"id":229,"organizationSlug":209},"SDAIA and Ministry of Interior: Sawaher camera analytics for Hajj crowd management",[173],{"name":179,"anonymized":180,"country":181,"region":182,"industry":18},[],"Sawaher is an AI platform that the Saudi Data and Artificial Intelligence Authority built with the Ministry of Interior. It analyzes live broadcasts, recordings and images from smart cameras at the holy sites and the main land and air entry points, and uses density and crowd counting algorithms to support Hajj security and to estimate pilgrim numbers. For Hajj 1446 (2025) SDAIA reported more than 5,000 cameras integrated across more than 80 locations, with results shown to operations rooms and monitoring centers. In May 2026 its spokesman described the system again for Hajj 1447. No accuracy, incident or response time figure has been published.","scaled",[29],[],[],[221,226,227],{"url":222,"title":223,"publisher":224,"date":225},"https:\u002F\u002Fwww.gccbusinessnews.com\u002Fsdaias-sawaher-system-boosts-security\u002F","SDAIA's Sawaher system boosts security and management","GCC Business News","2025-06-06",{"url":197,"title":198,"publisher":199,"date":200},{"url":202,"title":203,"publisher":204},{"level":206,"checkedAt":169},"sdaia-sawaher-hajj-crowd-analytics",{"title":231,"useCases":232,"organization":234,"vendors":239,"summary":243,"stage":244,"year":245,"channels":246,"languages":247,"metrics":249,"outcomeDisclosed":250,"sources":251,"verification":262,"grade":207,"id":263,"organizationSlug":209},"French Ministry of the Interior: algorithmic video surveillance trial for the Paris 2024 Games",[173,233],"physical-security-video-analytics",{"name":235,"anonymized":180,"country":236,"region":237,"industry":18,"wikidata":238},"Ministry of the Interior (France)","FR","europe","Q1518496",[240],{"name":241,"role":242},"Wintics","platform","Under the 2023 law for the Paris Olympic and Paralympic Games, France trialed AI analysis of public space and transport camera feeds for eight authorized situations, among them crowd movements and crowd density, abandoned objects, intrusion and weapons. An evaluation committee delivered its report to the Interior Ministry on 14 January 2025. As quoted by the French press, it found uneven technical performance. Intrusion detection into restricted areas was the one use with unanimous support, while 62 percent of the 270 abandoned object alerts were false positives. Transport operators rated density analysis satisfactory with reservations about tightly packed groups, and crowd movement was hard to assess because the software confused groups walking together with real surges. Only one software was tested and about half of the authorized uses were never tried.","pilot",2024,[29],[248],"fr",[],true,[252,257],{"url":253,"title":254,"publisher":255,"date":256},"https:\u002F\u002Fnext.ink\u002F165699\u002Faux-jo-de-paris-la-videosurveillance-algorithmique-na-servi-a-presque-rien\u002F","Aux JO de Paris, la vidéosurveillance algorithmique n'a servi à (presque) rien","Next","2025-01-16",{"url":258,"title":259,"publisher":260,"date":261},"https:\u002F\u002Fwww.publicsenat.fr\u002Factualites\u002Fsociete\u002Fvideosurveillance-algorithmique-lexperimentation-pendant-les-jo-une-simple-etape-vers-la-generalisation","Vidéosurveillance algorithmique : l'expérimentation pendant les JO, une simple étape vers la généralisation ?","Public Sénat","2024-10-03",{"level":206,"checkedAt":169},"french-ministry-of-interior-paris-2024-algorithmic-video-trial",0,[],{"low":267,"high":268},4800,84000,[270,290,306,321],{"slug":271,"title":272,"shortTitle":273,"definition":274,"status":9,"industries":275,"functions":277,"patterns":278,"audience":280,"autonomy":281,"adoptionStage":282,"evidenceCount":283,"publicEvidenceCount":283,"organizations":284,"bestGrade":288,"headline":209,"lastVerified":289,"indexable":250},"exam-and-assessment-integrity","AI assisted proctoring and integrity monitoring for remote exams","Exam and assessment integrity","Exam and assessment integrity AI supports the integrity of a remote, high stakes exam by verifying a test taker's identity and watching for signs of cheating. It analyzes behavior such as typing patterns, facial matching and session activity for signs of impersonation or unauthorized help, and flags sessions for a trained human reviewer to decide, rather than letting a model issue an automated finding of misconduct on its own.",[276],"education",[23,22],[26,25,279],"classification-and-routing","back-office","supervised-agent","mainstream",3,[285,286,287],"Duolingo","Educational Testing Service (ETS)","Pearson VUE","B","2026-09-29",{"slug":291,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":296,"patterns":298,"audience":30,"autonomy":31,"adoptionStage":299,"segment":300,"evidenceCount":283,"publicEvidenceCount":283,"organizations":301,"bestGrade":288,"headline":209,"lastVerified":305,"indexable":250},"freight-rail-rolling-stock-predictive-maintenance","AI predictive maintenance for freight rail rolling stock","Rail rolling stock predictive maintenance","AI predictive maintenance for freight rail rolling stock is machine vision and machine learning that inspect freight railcar wheels, bearings and other running gear. As trains pass wayside sensors and camera portals at track speed, it learns what a healthy wheel or reading looks like and flags the ones that need attention before a crack, an overheating bearing or a worn wheel causes a service failure or a derailment.",[19],[23,297],"field-service",[25,26,27],"early-adopters","mechanical-and-safety",[302,303,304],"BNSF Railway","Canadian Pacific Kansas City","Norfolk Southern","2026-09-28",{"slug":307,"title":308,"shortTitle":309,"definition":310,"status":9,"industries":311,"functions":314,"patterns":316,"audience":280,"autonomy":281,"adoptionStage":282,"evidenceCount":283,"publicEvidenceCount":283,"organizations":317,"bestGrade":207,"headline":209,"lastVerified":289,"indexable":250},"trust-and-safety-content-moderation","AI content moderation for trust and safety at online platforms","Trust and safety moderation","AI content moderation for trust and safety scans user generated content, such as live video, images, chat and audio, against an online platform's policy in real time. It removes or hides the clearest violations and routes borderline cases to a human moderator with its reasoning attached, so the platform can review far more content than a human team alone while keeping irreversible decisions with a person.",[312,20,313],"technology","cross-industry",[22,315],"case-management",[25,279,26],[318,319,320],"Chatroulette","Plato","Tango",{"slug":322,"title":323,"shortTitle":324,"definition":325,"status":9,"industries":326,"functions":327,"patterns":328,"audience":280,"autonomy":31,"adoptionStage":299,"evidenceCount":65,"publicEvidenceCount":65,"organizations":329,"bestGrade":288,"headline":209,"lastVerified":169,"indexable":250},"undeclared-building-detection-from-aerial-imagery","AI detection of undeclared buildings and pools from aerial imagery for property tax and cadastre updates","Undeclared building detection","Undeclared building detection is AI that compares aerial images with cadastre and tax records to find buildings, extensions and pools missing from the register. Tax authorities and mapping agencies use it so that officers verify a list of candidates instead of surveying whole regions.",[18],[315,23],[25,26],[330,331,332,333],"Agenzia delle Entrate","Direction générale des Finances publiques (DGFiP)","Gemeente Den Haag","Bavarian State Office for Digitization, Broadband and Surveying (LDBV)",{"indexable":250,"reasons":335},[],[337,343,348,356,364,371,377,384,392,399,405,411,417,424,431,437,443,449,455,462,467,472,478,484,491,496,501,507,513,518,525,532,537,543,549,554,559],{"id":143,"label":338,"issuer":339,"region":237,"url":340,"description":341,"useCases":342,"indexable":250},"EU AI Act","European Union","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":144,"label":344,"issuer":339,"region":237,"url":345,"description":346,"useCases":347,"indexable":250},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",265,{"id":349,"label":350,"issuer":351,"region":352,"url":353,"description":354,"useCases":355,"indexable":250},"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":357,"label":358,"issuer":359,"region":360,"url":361,"description":362,"useCases":363,"indexable":250},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":365,"label":366,"issuer":367,"region":237,"url":368,"description":369,"useCases":370,"indexable":250},"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":372,"label":373,"issuer":339,"region":237,"url":374,"description":375,"useCases":376,"indexable":250},"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":378,"label":379,"issuer":380,"region":237,"url":381,"description":382,"useCases":383,"indexable":250},"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":385,"label":386,"issuer":387,"region":388,"url":389,"description":390,"useCases":391,"indexable":250},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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":393,"label":394,"issuer":395,"region":388,"url":396,"description":397,"useCases":398,"indexable":250},"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":400,"label":401,"issuer":339,"region":237,"url":402,"description":403,"useCases":404,"indexable":250},"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":406,"label":407,"issuer":408,"region":352,"url":409,"description":410,"useCases":404,"indexable":250},"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":412,"label":413,"issuer":414,"region":360,"url":415,"description":416,"useCases":404,"indexable":250},"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":418,"label":419,"issuer":420,"region":360,"url":421,"description":422,"useCases":423,"indexable":250},"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.",20,{"id":425,"label":426,"issuer":427,"region":237,"url":428,"description":429,"useCases":430,"indexable":250},"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":432,"label":433,"issuer":339,"region":237,"url":434,"description":435,"useCases":436,"indexable":250},"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.",15,{"id":438,"label":439,"issuer":440,"region":352,"url":441,"description":442,"useCases":436,"indexable":250},"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":444,"label":445,"issuer":446,"region":360,"url":447,"description":448,"useCases":436,"indexable":250},"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":450,"label":451,"issuer":339,"region":237,"url":452,"description":453,"useCases":454,"indexable":250},"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":456,"label":457,"issuer":458,"region":360,"url":459,"description":460,"useCases":461,"indexable":250},"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":463,"label":464,"issuer":339,"region":237,"url":465,"description":466,"useCases":461,"indexable":250},"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":468,"label":469,"issuer":339,"region":237,"url":470,"description":471,"useCases":461,"indexable":250},"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":473,"label":474,"issuer":475,"region":352,"url":476,"description":477,"useCases":461,"indexable":250},"telecom-consumer-rules","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":479,"label":480,"issuer":481,"region":360,"url":482,"description":483,"useCases":58,"indexable":250},"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.",{"id":485,"label":486,"issuer":487,"region":237,"url":488,"description":489,"useCases":490,"indexable":250},"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":492,"label":493,"issuer":339,"region":237,"url":494,"description":495,"useCases":490,"indexable":250},"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":497,"label":498,"issuer":339,"region":237,"url":499,"description":500,"useCases":490,"indexable":250},"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":502,"label":503,"issuer":387,"region":388,"url":504,"description":505,"useCases":506,"indexable":250},"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.",10,{"id":508,"label":509,"issuer":510,"region":360,"url":511,"description":512,"useCases":57,"indexable":250},"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.",{"id":514,"label":515,"issuer":339,"region":237,"url":516,"description":517,"useCases":57,"indexable":250},"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":519,"label":520,"issuer":521,"region":182,"url":522,"description":523,"useCases":524,"indexable":250},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","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":526,"label":527,"issuer":528,"region":237,"url":529,"description":530,"useCases":531,"indexable":250},"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.",5,{"id":533,"label":534,"issuer":339,"region":237,"url":535,"description":536,"useCases":65,"indexable":250},"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":538,"label":539,"issuer":540,"region":237,"url":541,"description":542,"useCases":65,"indexable":250},"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":544,"label":545,"issuer":546,"region":388,"url":547,"description":548,"useCases":283,"indexable":250},"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":550,"label":551,"issuer":339,"region":237,"url":552,"description":553,"useCases":283,"indexable":250},"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":555,"label":556,"issuer":387,"region":388,"url":557,"description":558,"useCases":283,"indexable":250},"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":560,"label":561,"issuer":562,"region":360,"url":563,"description":564,"useCases":283,"indexable":250},"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.",1792049860534]