[{"data":1,"prerenderedAt":559},["ShallowReactive",2],{"uc-ground-handling-and-turnaround-optimization":3,"uc-regulations":334},{"useCase":4,"evidence":174,"blitsAiDeployments":256,"benchmarks":257,"indicative":268,"related":271,"indexability":332,"includeUnpublished":180},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":40,"macroEstimates":68,"feasibility":69,"implementation":81,"risk":125,"blitsAi":145,"faq":147,"related":160,"datePublished":162,"dateModified":163,"lastVerified":163,"changelog":164,"slug":173},"AI for aircraft turnaround and ground handling optimization","Ground handling and turnaround optimization","AI aircraft turnaround optimization software","Computer vision AI tracks aircraft turnarounds and predicts departure times. Assaia reports a 44% taxi in time cut at Toronto Pearson, worth CAD 47 million a year.","published","AI, often computer vision on cameras aimed at the gate and apron, that watches each aircraft turnaround (fueling, catering, baggage, boarding, pushback) in real time, predicts the departure time as soon as the aircraft arrives, and alerts ground operations staff the moment a subprocess falls behind schedule, so they can intervene before a small delay becomes a missed slot.",[12,13,14,15,16],"aircraft turnaround AI","apron AI","ground handling computer vision","turnaround control software","airport turnaround monitoring",[18],"travel-and-hospitality",[20],"operations",[22,23,24],"computer-vision","anomaly-detection","prediction-and-scoring",[26],"internal-tools","employee-facing","assist","early-adopters","ground operations","A short haul turnaround runs on a tight schedule, and every minute is shared by several ground\nhandling teams (fueling, catering, cleaning, baggage, boarding), working from different\ncompanies with their own systems, watched by airport and airline staff who cannot see the whole\napron at once. When one subprocess runs late, whoever is watching it often finds out only once\nthe knock on effect already shows up as a late pushback, a missed slot, or a gate conflict with\nthe next aircraft.\n\nAirports and airlines have tried to fix this with radio calls and manual observation for\ndecades. The step change is computer vision trained to recognize turnaround milestones\nautomatically from cameras on the apron, so every stand gets the same real time visibility a\nsupervisor would have if they could watch every gate at once, and a prediction of the departure\ntime that updates as the turnaround progresses.",[],"1. **Watch the stand.** Cameras on the gate or apron are analyzed with computer vision to detect\n   turnaround milestones (chocks on, GPU connected, catering truck docked, cargo doors open and\n   closed, boarding started, pushback).\n2. **Predict the departure time.** As soon as the aircraft is on stand, the system builds a live\n   prediction of the off block time and updates it as each milestone happens or slips.\n3. **Alert on deviation.** When a subprocess is running behind the plan, an alert goes to the\n   ramp agent, the ground handler or the airport's turnaround coordinator, with which process is\n   late, not just that the flight is.\n4. **Act on the alert.** Staff use the extra minutes of warning to reallocate equipment, request\n   help, or flag a likely gate conflict before the next aircraft arrives, rather than reacting\n   after the delay has happened.\n5. **Feed the record back.** Every turnaround's milestones and any deviation become a record used\n   for A-CDM (Airport Collaborative Decision Making) reporting, performance review with ground\n   handlers, and to retrain the milestone detection.",[35,36,37],"cost-to-serve","speed","risk-reduction",[39],"cost-savings",{"referenceOrg":41,"inputs":42,"formula":63,"currency":64,"period":65,"resultLabel":66,"caveat":67},"An airline or airport handling 200,000 turnarounds a year at equipped stands",[43,49,56],{"key":44,"label":45,"low":46,"high":46,"unit":47,"note":48},"turnarounds","Turnarounds per year at equipped stands",200000,"turnarounds per year","The reference airline or airport.",{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"costPerMinute","Cost of ground delay per minute of taxi in time saved",60,100,"USD per minute","Editorial assumption blending fuel, crew and downstream schedule cost per minute of taxi in time saved. Replace with your own.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"minutesSaved","Average taxi in time saved per flight",0.5,4,"minutes per flight","The two results on this page come from large hub airports, as reported by the vendor, on average 49 seconds (0.82 minutes) per flight at Seattle Tacoma Airport after almost a year of use, and almost 8 minutes per flight at Toronto Pearson. The low end sits below the Seattle Tacoma average to cover a smaller airport; the high end stays well below the Toronto Pearson figure.","turnarounds * costPerMinute * minutesSaved","USD","per year","Ground delay cost avoided","Gross ground delay cost avoided only. It leaves out the camera and platform cost and the CO2 value of less time spent with engines or the auxiliary power unit running.",[],{"complexity":70,"complexityNote":71,"dataPrerequisites":72,"integrations":76},"medium","Most of the effort is operational, not technical: getting camera coverage and lines of sight confirmed per stand, getting every ground handler and airline on the apron to trust and act on the same alerts, and agreeing who owns which milestone when three or four different companies work the same aircraft.",[73,74,75],"Camera coverage of each equipped stand with a usable line of sight to the milestones being tracked","Scheduled turnaround times and Airport Collaborative Decision Making (A-CDM) data per flight","Agreement with ground handlers and airlines on which milestones are tracked and who is alerted",[77,78,79,80],"Airport operational database (AODB) and A-CDM platform","Gate and stand allocation system","Ground handler and airline messaging or alerting channels","Camera or video management infrastructure already on the apron",{"steps":82,"guardrails":101,"humanInTheLoop":106,"kpisToInstrument":107,"failureModes":112},[83,86,89,92,95,98],{"title":84,"detail":85},"Start with the stands that cost the most when they slip","Equip the highest traffic or highest connection risk stands first, where a late turnaround has the most expensive knock on effect, rather than the whole airport at once.",{"title":87,"detail":88},"Agree the milestones and who owns each one","Before switching on alerts, agree with every ground handler and airline which milestones are tracked and who is responsible for acting on a deviation in each one, or alerts get ignored as someone else's problem.",{"title":90,"detail":91},"Predict, then alert, then automate the easy part","Ship the live departure time prediction first, since it is useful with no process change, then add deviation alerts, then automate anything low risk like gate conflict flags once staff trust the predictions.",{"title":93,"detail":94},"Close the loop into A-CDM","Feed confirmed milestones into the airport's A-CDM process so the predicted off block time used for slot and gate planning reflects what is actually happening on the ramp.",{"title":96,"detail":97},"Review with ground handlers, not just internally","Use the turnaround record in regular performance reviews with ground handling partners, so the data improves the service, not just the airline's or airport's own dashboard.",{"title":99,"detail":100},"Extend stand by stand","Add camera coverage and alerts to more stands once the first group shows a clear reduction in ground delay, rather than a big bang rollout across the whole apron.",[102,103,104,105],"Alerts name the specific subprocess and party responsible, not just \"the flight is late\", so accountability is clear","Camera footage used for turnaround milestones is not repurposed to individually monitor or score ground staff without saying so","Predictions and milestone detection are reviewed against actual outcomes regularly, and a stand is paused if detection accuracy drops","The system supports A-CDM decisions; gate and slot decisions stay with airport operations and air traffic control","Ramp agents, ground handlers and the airport's turnaround coordinator decide what to do with every alert; the system does not move equipment, reassign gates or hold an aircraft itself. Airport operations owns the A-CDM process the predictions feed into, and a joint review with ground handling partners checks the milestone data against what actually happened.",[108,109,110,111],"Average taxi in and turnaround time against the pre deployment baseline, per stand","Ground delay minutes and their estimated cost, per stand and per ground handler","Alert to action time, how quickly staff respond to a deviation alert","Milestone detection accuracy against a manually reviewed sample",[113,116,119,122],{"title":114,"detail":115},"Alert fatigue","Too many low value alerts and staff start ignoring all of them, including the ones that matter. Tune alert thresholds per stand and review false alert rates with the teams receiving them.",{"title":117,"detail":118},"Camera blind spots","A stand's camera angle cannot reliably see a milestone (for example a cargo door on the far side), and the system reports a false deviation or misses a real one. Confirm line of sight per stand before turning on alerts for it, and flag stands with known coverage gaps.",{"title":120,"detail":121},"Nobody owns the alert","An alert reaches a general channel instead of the person who can act, and it is lost. Route each milestone's alert to the specific ground handler or team responsible, agreed in advance.",{"title":123,"detail":124},"Data used against ground handling partners without warning","Turnaround data introduced into supplier reviews or contract disputes without the ground handler ever seeing it live damages the trust the whole system depends on. Share the same data with partners in near real time, not only after the fact.",{"euAiAct":126,"regulations":129,"guidance":132,"controls":139,"incidents":144},{"tier":127,"basis":128},"context-dependent","Predicting a departure time and alerting on a late turnaround subprocess is not listed in Annex III: it does not decide access to a service, creditworthiness or employment, and it is not a safety component of an aircraft or a product regulated under Regulation (EU) 2018/1139 that Annex I Section B lists. Annex III point 4(b) covers AI used to monitor and evaluate the performance and behavior of workers; a design that uses the same camera feeds to score individual ground staff, rather than to track turnaround subprocesses, would sit closer to that category, and GDPR applies to any footage that identifies staff.",[130,131],"eu-ai-act","gdpr",[133],{"title":134,"issuer":135,"region":136,"url":137,"note":138},"EASA Artificial Intelligence Roadmap 2.0: A human-centric approach to AI in aviation","European Union Aviation Safety Agency","europe","https://www.easa.europa.eu/en/document-library/general-publications/easa-artificial-intelligence-roadmap-20","Sets EASA's human centric vision and action plan for AI in aviation, including conceptual guidance and rulemaking; the roadmap document itself is the source for the specifics, the landing page carries only a short summary.",[140,141,142,143],"Purpose of camera based monitoring documented and communicated to ground handling staff and partners","Alerts and predictions logged against actual outcomes, with regular accuracy review per stand","Data sharing agreement with ground handlers on how turnaround data is used in performance reviews","A stand's alerts are paused, not left running silently wrong, when detection accuracy drops below an agreed threshold",[],{"howToBuild":146},"The computer vision and turnaround prediction itself is a specialist capability that sits in a\ndedicated apron monitoring platform, not in a general purpose conversational agent. Blits.ai's\nrole is the layer ground staff and duty managers actually talk to: an **AI agent** with\n**custom functions** that call the turnaround platform's API for a stand's current status and\npredicted departure time, combined with a **knowledge base** of the airport's own turnaround\nprocedures and A-CDM rules, retrieved with **hybrid retrieval**, so staff can ask in plain\nlanguage why a stand is flagged and what the procedure says to do next.\n\nDeviation alerts can trigger an **agentic workflow** that drafts a summary for the turnaround\ncoordinator and, with **human in the loop confirmation**, notifies the responsible ground\nhandler through **Microsoft Teams** or email, rather than a plain automated message\nwith no context. **Analytics** on the agent itself tracks how staff use it, interactions,\nunanswered questions, satisfaction, and **monitors** run scheduled health checks against the\nagent so a lost connection to the turnaround platform is caught quickly rather than found by\nstaff getting no answer. The platform is **model agnostic**, with **EU and UAE data\nresidency** for airports and airlines that need it.",[148,151,154,157],{"question":149,"answer":150},"Does the AI move equipment or hold an aircraft?","No. Across the deployments described on this page, the system predicts and alerts; ramp agents, ground handlers and the airport's turnaround coordinator decide what to do. Gate and slot decisions stay with airport operations and air traffic control.",{"question":152,"answer":153},"What results have airports and airlines reported?","Assaia reports that Seattle Tacoma Airport saw an almost 10% reduction in taxi in time (on average 49 seconds per flight) after using its predicted off block times for about a year. The same case study then projects that a carrier such as Alaska Airlines, at Alaska's 2019 volume of 71,000 flights out of Seattle Tacoma, would see about USD 9.2 million a year in total operating costs eliminated, a modelled figure that combines the Seattle Tacoma taxi in result with a separate auxiliary power unit shutdown time result Assaia reports from an unnamed US airport, not a measured Alaska Airlines outcome. In a separate case study, Assaia reports a 44% reduction in average taxi in time at Toronto Pearson, with annual savings of CAD 47 million.",{"question":155,"answer":156},"Does this replace ground handling staff?","The deployments on this page describe giving staff earlier visibility of a developing delay, not replacing the teams doing fueling, catering, baggage or boarding. The value comes from staff acting sooner on an alert, not from removing the ground handling work itself.",{"question":158,"answer":159},"Is this only useful at large hub airports?","The public deployments on this page are large hub airports (Seattle Tacoma, Toronto Pearson), where high traffic and tight connections make a minute of turnaround time expensive. A smaller airport with less connection risk would see a smaller return, and cost effective camera coverage per stand is worth checking before committing to a wide rollout.",[161],"airline-operations-control-decision-support","2026-09-29","2026-09-30",[165,167,169,171],{"date":163,"note":166},"Published after review by an automated review workflow (independent skeptic review).",{"date":163,"note":168},"Editorial fix after a second, adversarial review: removed the unsupported 'both mature deployments' claim from the minutesSaved note, added a Port of Seattle / Seattle Tacoma Airport evidence record so the Seattle Tacoma figure has its own public source instead of living only inside the Alaska Airlines record's note, downgraded the Toronto Pearson evidence stage from scaled to production (the 106 gate rollout is a stated requirement, not a confirmed completed one), corrected the Alaska Airlines evidence verification note's description of the $9.2M calculation, added a later Wayback capture with a named Alaska Airlines testimonial, softened 'usually computer vision' to 'often', replaced WhatsApp with email in blitsAi.howToBuild to stay within the platform feature inventory, made the Seattle Tacoma / Seattle-Tacoma spelling consistent, and reworded the FAQ to describe deployments generally rather than claim both are identical.",{"date":163,"note":170},"Unpublished by an automated review workflow (independent skeptic review).",{"date":162,"note":172},"First published","ground-handling-and-turnaround-optimization",[175,213,236],{"title":176,"useCases":177,"organization":178,"vendors":183,"summary":187,"stage":188,"year":189,"channels":190,"languages":191,"metrics":193,"outcomeDisclosed":203,"sources":204,"verification":208,"grade":210,"id":211,"organizationSlug":212},"Toronto Pearson: 44% less taxi in time and CAD 47 million a year with Assaia",[173],{"name":179,"anonymized":180,"country":181,"region":182,"industry":18},"Greater Toronto Airports Authority",false,"CA","north-america",[184],{"name":185,"role":186},"Assaia","platform","Toronto Pearson International Airport, Canada's busiest with more than 40 airlines flying to over 160 destinations, sought full situational awareness of its turnaround process across all 106 gates to give a single source of truth for turnaround operations and feed the airport's Airport Collaborative Decision Making (A-CDM) process, and deployed Assaia's TurnaroundControl to do it. The system uses computer vision to track turnaround subprocesses such as fueling and catering against schedule, predicts departure times as soon as an aircraft arrives, and alerts operational staff to deviations so they can intervene before a delay compounds. Assaia's case study credits the deployment with a large reduction in average taxi in time and the resulting annual savings, and a shorter term reduction in ground delays; a Greater Toronto Airports Authority representative is quoted describing improved visibility and resilience across airline partners, ground handlers and service providers without repeating the numeric figures.","production",2024,[26],[192],"en",[194],{"kpi":39,"value":195,"unit":196,"currency":197,"qualifier":198,"period":65,"baseline":199,"claimant":200,"quote":201,"sourceUrl":202},47000000,"currency","CAD","exact","Average taxi in time before TurnaroundControl","vendor","44% Reduction in average taxi-In time resulting in annual savings of CAD $47 million","https://www.assaia.com/customer-stories/44-reduction-at-toronto-pearson-international-airport",true,[205],{"url":202,"title":206,"publisher":185,"archivedUrl":207},"44% Reduction in Average Taxi-In Time achieved at Toronto Pearson International Airport","https://web.archive.org/web/20240619202606/https://www.assaia.com/customer-stories/44-reduction-at-toronto-pearson-international-airport",{"level":209,"checkedAt":163},"source-verified","C","toronto-pearson-assaia-turnaround-optimization",null,{"title":214,"useCases":215,"organization":216,"vendors":219,"summary":221,"stage":188,"year":222,"channels":223,"languages":224,"metrics":225,"outcomeDisclosed":180,"sources":226,"verification":234,"grade":210,"id":235,"organizationSlug":212},"Alaska Airlines: a modelled Seattle Tacoma savings projection from Assaia",[173],{"name":217,"anonymized":180,"country":218,"region":182,"industry":18},"Alaska Airlines","US",[220],{"name":185,"role":186},"Alaska Airlines is quoted in Assaia's customer testimonial carousel as using Assaia's platform to improve its aircraft turnaround process. The same case study uses Alaska's 71,000 flights out of Seattle Tacoma International Airport (SEA) in 2019 as a worked example for a projection: it combines an almost 10% taxi in time reduction that Assaia reports for Seattle Tacoma Airport itself, with a separate 4 minute average reduction in ground power unit (GPU) and air conditioning unit (ACU) connection time that Assaia reports from \"another major airport in the US\", which lets the aircraft's own auxiliary power unit (APU) be shut down sooner. Applying the FAA modelled operating cost per block hour to both results, Assaia projects USD 9.2 million a year in total operating costs eliminated for \"a carrier such as Alaska Airlines\" at that flight volume. This is a vendor modelled projection, not a measured Alaska Airlines outcome.",2022,[26],[192],[],[227,231],{"url":228,"title":229,"publisher":185,"archivedUrl":230},"https://www.assaia.com/resources/reducing-kerosene-costs","ApronAI Case Study: Reducing Kerosene Costs","https://web.archive.org/web/20220702140239/https://assaia.com/resources/reducing-kerosene-costs",{"url":228,"title":232,"publisher":185,"archivedUrl":233},"ApronAI Case Study: Reducing Kerosene Costs (later capture with the Alaska Airlines testimonial)","https://web.archive.org/web/20231002011032/https://assaia.com/resources/reducing-kerosene-costs",{"level":209,"checkedAt":163},"alaska-airlines-assaia-turnaround-optimization",{"title":237,"useCases":238,"organization":239,"vendors":241,"summary":243,"stage":188,"year":222,"channels":244,"languages":245,"metrics":246,"outcomeDisclosed":203,"sources":252,"verification":254,"grade":210,"id":255,"organizationSlug":212},"Seattle Tacoma Airport: taxi in time cut and $25 million a year with Assaia",[173],{"name":240,"anonymized":180,"country":218,"region":182,"industry":18},"Port of Seattle",[242],{"name":185,"role":186},"Seattle Tacoma International Airport (SEA), operated by the Port of Seattle, has been using Assaia's Predicted Off-Block Time (POBT) feature for almost a year, per Assaia's own case study on reducing kerosene costs. The system builds a continuous prediction of each aircraft's off block time from live turnaround data, which the case study credits with a reduction in average taxi in time at SEA. Assaia's case study translates that reduction into a per flight and airport wide operating cost saving for SEA, separately from the modelled Alaska Airlines projection recorded in its own evidence record for this use case.",[26],[192],[247],{"kpi":39,"value":248,"unit":196,"currency":64,"qualifier":249,"period":65,"baseline":250,"claimant":200,"quote":251,"sourceUrl":228},25000000,"at-least","Taxi in time before Assaia's Predicted Off-Block Time (POBT) feature","an annual total saving of more than $25 million for all flights at SEA",[253],{"url":228,"title":229,"publisher":185,"archivedUrl":230},{"level":209,"checkedAt":163},"port-of-seattle-assaia-turnaround-optimization",0,[258,264],{"kpi":39,"label":259,"unit":196,"currency":64,"aggregate":180,"higherIsBetter":203,"n":260,"nUpTo":256,"median":248,"min":248,"max":248,"byClaimant":261,"vendorOnly":203,"points":262},"Cost savings",1,{"organization":256,"vendor":260,"regulator":256,"independent":256},[263],{"evidenceId":255,"organization":240,"value":248,"qualifier":249,"claimant":200,"grade":210,"pooled":203},{"kpi":39,"label":259,"unit":196,"currency":197,"aggregate":180,"higherIsBetter":203,"n":260,"nUpTo":256,"median":195,"min":195,"max":195,"byClaimant":265,"vendorOnly":203,"points":266},{"organization":256,"vendor":260,"regulator":256,"independent":256},[267],{"evidenceId":211,"organization":179,"value":195,"qualifier":198,"claimant":200,"grade":210,"pooled":203},{"low":269,"high":270},6000000,80000000,[272,287,301,319],{"slug":161,"title":273,"shortTitle":274,"definition":275,"status":9,"industries":276,"functions":277,"patterns":278,"audience":27,"autonomy":279,"adoptionStage":29,"evidenceCount":280,"publicEvidenceCount":280,"organizations":281,"bestGrade":285,"headline":212,"lastVerified":286,"indexable":203},"AI decision support for airline operations control and disruption recovery","Airline operations control","Decision support in an airline's operations control center that watches the day's operation, predicts where weather, delays, crew limits or technical problems will break the plan, and proposes recovery options across aircraft, crew and passengers, such as retiming flights, swapping aircraft or holding a connection, with the cost and passenger impact of each, for controllers to approve.",[18],[20],[24],"copilot",3,[282,283,284],"American Airlines","British Airways","Swiss International Air Lines","B","2026-09-27",{"slug":288,"title":289,"shortTitle":290,"definition":291,"status":9,"industries":292,"functions":293,"patterns":294,"audience":27,"autonomy":28,"adoptionStage":29,"segment":296,"evidenceCount":297,"publicEvidenceCount":297,"organizations":298,"bestGrade":210,"headline":212,"lastVerified":163,"indexable":203},"airline-fuel-efficiency-optimization","AI for airline fuel efficiency optimization","Airline fuel efficiency optimization","An AI system that analyzes flight data, fuel burn, routing, taxi procedure and auxiliary power unit use, fleet wide, to coach pilots and flight operations engineers on where a flight over or under performed against a fuel baseline, flags the highest value savings opportunities by route and procedure, and tracks the savings over time.",[18],[20],[24,295,23],"recommendation-and-personalization","flight operations",2,[299,300],"Icelandair","JetBlue",{"slug":302,"title":303,"shortTitle":304,"definition":305,"status":9,"industries":306,"functions":308,"patterns":311,"audience":313,"autonomy":28,"adoptionStage":314,"segment":315,"evidenceCount":297,"publicEvidenceCount":297,"organizations":316,"bestGrade":210,"headline":212,"lastVerified":163,"indexable":203},"catastrophe-exposure-assessment","AI for catastrophe and exposure assessment in insurance","Catastrophe exposure assessment","AI that turns satellite and geospatial data into a fast, portfolio wide view of which policies and properties are exposed to a catastrophe, before and immediately after the event, so exposure managers and claims teams can quantify the loss, prioritize response and reach affected customers first, without waiting for ground surveys.",[307],"insurance",[309,310,20],"claims","risk-management",[22,23,24,312],"agentic-workflow","back-office","emerging","catastrophe-and-exposure",[317,318],"Sompo Japan Insurance","Suncorp Group",{"slug":320,"title":321,"shortTitle":322,"definition":323,"status":9,"industries":324,"functions":325,"patterns":327,"audience":27,"autonomy":28,"adoptionStage":29,"segment":328,"evidenceCount":297,"publicEvidenceCount":297,"organizations":329,"bestGrade":285,"headline":212,"lastVerified":163,"indexable":203},"aircraft-predictive-maintenance","AI predictive maintenance for aircraft fleets","Aircraft predictive maintenance","AI that continuously analyzes aircraft sensor and flight data to flag a developing technical fault before it grounds an aircraft, so an airline's engineering team can fix it on a scheduled visit instead of an unplanned aircraft on ground (AOG) event. It moves maintenance planning from reacting to a fault message or a crew reported defect to predicting a developing issue from patterns in the data before it occurs.",[18],[20,326],"field-service",[24,23],"maintenance and engineering",[330,331],"Frontier Airlines","LATAM Airlines Group",{"indexable":203,"reasons":333},[],[335,341,346,354,361,368,374,381,389,396,403,409,415,421,428,435,441,448,453,459,466,473,478,483,488,495,500,505,512,517,525,531,537,543,548,553],{"id":130,"label":336,"issuer":337,"region":136,"url":338,"description":339,"useCases":340,"indexable":203},"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":131,"label":342,"issuer":337,"region":136,"url":343,"description":344,"useCases":345,"indexable":203},"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":347,"label":348,"issuer":349,"region":350,"url":351,"description":352,"useCases":353,"indexable":203},"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":355,"label":356,"issuer":357,"region":182,"url":358,"description":359,"useCases":360,"indexable":203},"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.",95,{"id":362,"label":363,"issuer":364,"region":136,"url":365,"description":366,"useCases":367,"indexable":203},"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":369,"label":370,"issuer":337,"region":136,"url":371,"description":372,"useCases":373,"indexable":203},"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":375,"label":376,"issuer":377,"region":136,"url":378,"description":379,"useCases":380,"indexable":203},"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":382,"label":383,"issuer":384,"region":385,"url":386,"description":387,"useCases":388,"indexable":203},"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":390,"label":391,"issuer":392,"region":385,"url":393,"description":394,"useCases":395,"indexable":203},"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":397,"label":398,"issuer":399,"region":350,"url":400,"description":401,"useCases":402,"indexable":203},"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":404,"label":405,"issuer":406,"region":182,"url":407,"description":408,"useCases":402,"indexable":203},"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":410,"label":411,"issuer":337,"region":136,"url":412,"description":413,"useCases":414,"indexable":203},"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":416,"label":417,"issuer":418,"region":136,"url":419,"description":420,"useCases":414,"indexable":203},"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":422,"label":423,"issuer":424,"region":182,"url":425,"description":426,"useCases":427,"indexable":203},"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":429,"label":430,"issuer":431,"region":350,"url":432,"description":433,"useCases":434,"indexable":203},"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":436,"label":437,"issuer":337,"region":136,"url":438,"description":439,"useCases":440,"indexable":203},"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":442,"label":443,"issuer":444,"region":182,"url":445,"description":446,"useCases":447,"indexable":203},"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":449,"label":450,"issuer":337,"region":136,"url":451,"description":452,"useCases":447,"indexable":203},"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":454,"label":455,"issuer":456,"region":182,"url":457,"description":458,"useCases":447,"indexable":203},"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":460,"label":461,"issuer":462,"region":350,"url":463,"description":464,"useCases":465,"indexable":203},"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":467,"label":468,"issuer":469,"region":182,"url":470,"description":471,"useCases":472,"indexable":203},"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":474,"label":475,"issuer":337,"region":136,"url":476,"description":477,"useCases":472,"indexable":203},"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":479,"label":480,"issuer":337,"region":136,"url":481,"description":482,"useCases":472,"indexable":203},"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":484,"label":485,"issuer":337,"region":136,"url":486,"description":487,"useCases":472,"indexable":203},"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":489,"label":490,"issuer":491,"region":136,"url":492,"description":493,"useCases":494,"indexable":203},"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":496,"label":497,"issuer":384,"region":385,"url":498,"description":499,"useCases":494,"indexable":203},"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":501,"label":502,"issuer":337,"region":136,"url":503,"description":504,"useCases":494,"indexable":203},"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":506,"label":507,"issuer":508,"region":182,"url":509,"description":510,"useCases":511,"indexable":203},"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":513,"label":514,"issuer":337,"region":136,"url":515,"description":516,"useCases":511,"indexable":203},"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":518,"label":519,"issuer":520,"region":521,"url":522,"description":523,"useCases":524,"indexable":203},"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.",5,{"id":526,"label":527,"issuer":528,"region":136,"url":529,"description":530,"useCases":60,"indexable":203},"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":532,"label":533,"issuer":534,"region":136,"url":535,"description":536,"useCases":60,"indexable":203},"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":538,"label":539,"issuer":540,"region":385,"url":541,"description":542,"useCases":280,"indexable":203},"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":544,"label":545,"issuer":337,"region":136,"url":546,"description":547,"useCases":280,"indexable":203},"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":549,"label":550,"issuer":337,"region":136,"url":551,"description":552,"useCases":280,"indexable":203},"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":554,"label":555,"issuer":556,"region":182,"url":557,"description":558,"useCases":280,"indexable":203},"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.",1790783077031]