[{"data":1,"prerenderedAt":554},["ShallowReactive",2],{"uc-airline-fuel-efficiency-optimization":3,"uc-regulations":329},{"useCase":4,"evidence":181,"blitsAiDeployments":247,"benchmarks":248,"indicative":255,"related":258,"indexability":327,"includeUnpublished":187},{"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":36,"indicativeValue":40,"macroEstimates":75,"feasibility":76,"implementation":88,"risk":132,"blitsAi":152,"faq":154,"related":167,"datePublished":169,"dateModified":170,"lastVerified":170,"changelog":171,"slug":180},"AI for airline fuel efficiency optimization","Airline fuel efficiency optimization","AI fuel efficiency software for airlines","AI analyzes flight data to coach pilots and cut jet fuel burn. JetBlue saved nearly 2,000 tons of fuel in six months; Icelandair's fuel savings grew 247% since 2018.","published","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.",[12,13,14,15,16],"flight fuel efficiency software","fuel saving AI for airlines","pilot fuel coaching app","jet fuel optimization AI","eco flying analytics",[18],"travel-and-hospitality",[20],"operations",[22,23,24],"prediction-and-scoring","recommendation-and-personalization","anomaly-detection",[26],"internal-tools","employee-facing","assist","early-adopters","flight operations","Jet fuel is a major operating cost and emissions source for an airline, and it is hard to manage\nday to day, because it is the sum of thousands of small decisions: the cruise cost index a\ndispatcher plans, whether a pilot taxis on one engine or two, when the auxiliary power unit is\nswitched on and off, how much alternate and contingency fuel is carried, how close the aircraft's\nzero fuel weight sits to plan. Flight data recorders capture all of this, but turning it into a\nsignal a pilot or a fuel manager can act on soon after the flight, rather than in a periodic\nreport, is the harder problem.\n\nThe step change is software that scores every flight against a fleet and route level baseline\nand coaches pilots individually and non punitively. Icelandair has used one such platform,\nSkyBreathe, since 2014, running\ninitiatives such as Cost Index Zero, single engine taxi out, alternate fuel planning, delayed APU\nstart and zero fuel weight planning, and it reports that its total fuel savings rose 247% since\n2018.",[],"1. **Ingest flight data.** Every flight's data, from the quick access recorder or ACARS, weight\n   and balance, routing and weather, is captured and normalized across the fleet.\n2. **Score against a baseline.** Models compare each flight's actual fuel burn to a fleet and\n   route level baseline and identify which specific levers, cruise cost index, taxi procedure,\n   APU timing, alternate fuel, zero fuel weight, explain the gap.\n3. **Coach the pilot.** A pilot facing app shows, in plain language, where a flight over or under\n   performed and what to try next time, framed as an opportunity rather than a mistake.\n4. **Prioritize for the fleet.** Fuel managers and flight operations engineers get the same data\n   rolled up by route, aircraft type and procedure, to decide which standard operating procedure\n   to change next.\n5. **Track and report.** Savings are tracked against the baseline over time and rolled into the\n   airline's fuel and sustainability reporting.",[35],"cost-to-serve",[37,38,39],"energy-savings","cost-savings","cost-reduction",{"referenceOrg":41,"inputs":42,"formula":70,"currency":71,"period":72,"resultLabel":73,"caveat":74},"An airline burning 500,000 metric tons of jet fuel a year",[43,49,56,63],{"key":44,"label":45,"low":46,"high":46,"unit":47,"note":48},"fuelTons","Annual jet fuel burn",500000,"metric tons per year","The reference airline.",{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"fuelPricePerTon","Jet fuel price",700,900,"USD per metric ton","Editorial assumption for a blended jet fuel price. Replace with your own.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"addressableShare","Share of fuel burn addressable by pilot and procedure coaching",0.5,0.7,"fraction of fuel burn","Editorial assumption for the share of fuel burn influenced by cruise, taxi, APU and weight decisions rather than route network design.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"savingsRate","Fuel saved on the addressable share, first year",0.01,0.03,"fraction of addressable fuel burn","Editorial assumption, replace with your own. Icelandair's 247% figure is cumulative since 2018, not a single year rate, so it does not set this range directly.","fuelTons * fuelPricePerTon * addressableShare * savingsRate","USD","per year","Jet fuel cost avoided","Gross fuel cost avoided only. It leaves out the cost of the platform, data integration and the fuel team's time, the value of the CO2 reduction under emissions trading or offset schemes, and any safety margin or operational trade offs behind a given saving.",[],{"complexity":77,"complexityNote":78,"dataPrerequisites":79,"integrations":83},"medium","The analytics themselves are available from specialist vendors; the effort is in getting a clean, complete flight data feed fleet wide, agreeing a fair baseline per route and aircraft type, and building pilot trust that the coaching is developmental, not a scorecard used against them.",[80,81,82],"Flight data recorder or ACARS data for the whole fleet, complete enough to compute a fuel baseline","Route, weight and balance and weather data per flight","A fuel policy that states what the airline can and cannot change (cost index range, contingency fuel rules) so recommendations stay inside it",[84,85,86,87],"Flight data monitoring or quick access recorder system","Flight planning and dispatch system","Pilot facing app or portal for coaching content","Fuel and sustainability reporting systems",{"steps":89,"guardrails":108,"humanInTheLoop":113,"kpisToInstrument":114,"failureModes":119},[90,93,96,99,102,105],{"title":91,"detail":92},"Start with one or two levers","Pick the levers with the clearest, safest savings, such as single engine taxi or APU timing, before tackling cost index or alternate fuel policy, which touch dispatch and safety margins.",{"title":94,"detail":95},"Agree the baseline","Set the fuel baseline per route and aircraft type with flight operations engineering, not the vendor alone, so pilots trust that a flagged flight was really an outlier.",{"title":97,"detail":98},"Make coaching non punitive","Frame every pilot facing message as an opportunity, not a report card, and keep individual results out of any performance review; a program pilots do not trust will not change behavior.",{"title":100,"detail":101},"Route bigger changes through flight operations","Aggregate findings feed proposed standard operating procedure changes, which go through the airline's normal flight operations and safety review, not an automatic push to pilots.",{"title":103,"detail":104},"Measure against the baseline, not the pilot","Track fuel saved against the fleet baseline over time, and separately track pilot app adoption, so a quiet program (low engagement) is visible before it shows up as no savings.",{"title":106,"detail":107},"Extend fleet by fleet","Roll out a proven baseline and coaching program to the next fleet type or base once adoption and savings are established on the first one.",[109,110,111,112],"Recommendations never exceed the airline's own fuel policy (cost index range, minimum contingency fuel); the assistant coaches within the policy, it does not set it","Individual pilot results are not published or tied to performance review","Every fleet level standard operating procedure change goes through flight operations and safety review, not an automatic push","Baseline and coaching content are agreed with flight operations engineering, not the vendor alone","Pilots decide what to do differently on their own flights; the tool coaches, it does not fly the aircraft or override a pilot's operational decision. Flight operations engineering owns the baseline and any standard operating procedure change, and the fuel team reviews outlier flights before they are raised with a pilot.",[115,116,117,118],"Fuel burn against baseline, by fleet, route and lever, month over month","Pilot app adoption and engagement","Savings by initiative (cost index, taxi, APU, alternate fuel, zero fuel weight), to see which lever is worth extending","CO2 or fuel cost avoided against the program's own cost",[120,123,126,129],{"title":121,"detail":122},"Coaching pilots do not trust","A program that feels punitive gets ignored or worked around. Keep it non punitive, explain the baseline, and show pilots their own trend, not a ranking.",{"title":124,"detail":125},"Savings that are really a mix shift","Fuel per flight looks better because the route or aircraft mix changed, not because behavior did. Normalize the baseline by route and aircraft type before crediting a saving to coaching.",{"title":127,"detail":128},"Recommendations that ignore the fuel policy","A model trained only on fuel burn can suggest cutting into contingency fuel or a cost index outside policy. Hard code the policy limits so recommendations never exceed them.",{"title":130,"detail":131},"A program that stalls after the easy wins","Early levers (taxi, APU) get adopted fast and then engagement drops. Keep adding levers and keep the coaching content fresh, and track engagement, not only savings.",{"euAiAct":133,"regulations":136,"guidance":139,"controls":146,"incidents":151},{"tier":134,"basis":135},"context-dependent","Annex III point 4(b) covers AI used to monitor and evaluate the performance and behavior of workers. A tool that scores each pilot's individual flights against a baseline and shows a pilot where they under performed is a form of worker performance and behavior monitoring, so per pilot scoring and coaching can fall under 4(b) even when the score is never fed into a formal performance review; whether it does depends on whether individual pilot evaluation is genuinely in scope and on the provider's own Article 6(3) assessment. Fleet or route level aggregate analytics that never attribute a score to a named pilot is the lower risk design and sits outside Annex III, because it does not evaluate a specific worker. Safety critical decisions (how much fuel to carry, whether to divert) stay with the pilot in command under existing flight operations rules regardless of tier.",[137,138],"eu-ai-act","gdpr",[140],{"title":141,"issuer":142,"region":143,"url":144,"note":145},"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 expectations for trustworthy AI in aviation, including human oversight of recommendations that touch flight operations.",[147,148,149,150],"Fuel policy limits (cost index range, minimum contingency fuel) hard coded so recommendations cannot exceed them","Individual pilot data kept out of performance review, with an accountable owner for the coaching program's use of pilot data","Baseline methodology and any standard operating procedure change documented and signed off by flight operations","Regular review of savings claims against actual fuel invoices, not only the platform's own dashboard",[],{"howToBuild":153},"Fuel analytics and per flight scoring are specialist, safety adjacent calculations that belong\nin the airline's flight data monitoring platform, not in a general purpose agent. Blits.ai's\nrole is the coaching and reporting layer around it: an **AI agent** with **custom functions**\nthat call the fuel analytics platform's API for a pilot's own recent flights and turn the\noutput into a plain language coaching message, and a **knowledge base** holding the airline's\nfuel policy and standard operating procedures, retrieved with **hybrid retrieval**, so the\nagent can explain why a recommendation stays inside policy.\n\nPilots and fuel managers can ask questions through a **web chat** or **Microsoft Teams**\nassistant, and the bot's own **analytics** dashboard, with **custom dashboard widgets**, can\ntrack usage and engagement with the coaching agent itself. Fleet level fuel savings stay in the\nairline's flight data monitoring platform and are not something the bot's analytics compute.\n**Guardrails** help keep the agent's answers inside the loaded fuel policy, and the platform is\n**model agnostic**, so the airline can change the underlying model without changing the\nintegration.",[155,158,161,164],{"question":156,"answer":157},"Does the AI decide how much fuel to load or where to divert?","No, and it should not be designed to. This category of tool coaches pilots and flight operations engineering on efficiency; it should be scoped to stay within the airline's existing fuel policy rather than set it. Safety critical fuel and diversion decisions belong with the pilot in command and the dispatcher, under the airline's normal operational rules.",{"question":159,"answer":160},"What savings have airlines reported?","OpenAirlines reports that JetBlue's Director and System Chief Pilot said the airline went from 19% to 45% engine out taxi out compliance and saved nearly 2,000 tons of fuel in six months with SkyBreathe MyFuelCoach. Icelandair's Program Manager for Fuel Safety and Efficiency reports that the airline's total fuel savings grew 247% since 2018 using the same platform, a cumulative multi year figure, not an annual rate.",{"question":162,"answer":163},"Is pilot coaching data used to rank or penalize pilots?","In the deployments on this page, OpenAirlines' case study, based on Christopher Lum's testimony, quotes JetBlue's Director and System Chief Pilot saying the airline is \"not asking the pilots to be perfect\" and wants to show them \"where the opportunity may have been\", which the case study frames as non punitive. Keeping individual results out of performance review is a guardrail we recommend for any deployment of this kind, not a fact both case studies state.",{"question":165,"answer":166},"How mature is this technology?","In production at two named airlines on one vendor's platform. Icelandair has used OpenAirlines' SkyBreathe since 2014, and OpenAirlines' case study headline for JetBlue claims a positive return on investment within three months of its own fuel program. That is evidence for two named airlines on one vendor's platform, not a claim about the vendor's full customer base or the wider market.",[168],"airline-operations-control-decision-support","2026-09-29","2026-09-30",[172,174,176,178],{"date":170,"note":173},"Published after review by an automated review workflow (independent skeptic review).",{"date":170,"note":175},"Fixed adversarial review blockers. Added two OpenAirlines product pages (SkyBreathe Analytics, SkyBreathe MyFuelCoach) as sources on both evidence records, checked word for word, to establish that the specific products JetBlue and Icelandair run are AI based; neither case study itself uses the words AI, artificial intelligence or machine learning. Reattributed the \"positive ROI in three months\" claim from JetBlue/Lum to OpenAirlines' own case study headline, in faq[3] and the JetBlue evidence summary. Reworded faq[2] to quote Lum's testimony directly instead of paraphrasing \"non punitive\" as his own word. Removed the unsupported \"conservative\" and \"well below the 247% figure\" characterization from the savingsRate note. Softened the problem paragraph's unsourced \"as soon as the data lands\" and \"rather than publishing a league table\" claims. Reworded faq[3]'s opening to \"In production at two named airlines on one vendor's platform\", consistent with adoptionStage: early-adopters.",{"date":170,"note":177},"Unpublished by an automated review workflow (independent skeptic review).",{"date":169,"note":179},"First published","airline-fuel-efficiency-optimization",[182,217],{"title":183,"useCases":184,"organization":185,"vendors":189,"summary":193,"stage":194,"year":195,"channels":196,"languages":197,"metrics":199,"outcomeDisclosed":200,"sources":201,"verification":212,"grade":214,"id":215,"organizationSlug":216},"Icelandair: total fuel savings up 247% since 2018 with SkyBreathe",[180],{"name":186,"anonymized":187,"country":188,"region":143,"industry":18},"Icelandair",false,"IS",[190],{"name":191,"role":192},"OpenAirlines","platform","Icelandair, which operates 52 aircraft to 58 destinations, has used OpenAirlines' SkyBreathe since 2014 and built its fuel program around SkyBreathe Analytics and SkyBreathe MyFuelCoach, covering initiatives such as switching from Long Range Cruise to Cost Index Zero, maximizing single engine taxi out, optimizing alternate fuel planning, delaying APU start and improving zero fuel weight planning. In OpenAirlines' case study, Helga S. Thordersen Magnusdottir, Icelandair's Program Manager for Fuel Safety and Efficiency, reports that total fuel savings have grown substantially since 2018 and that relative CO2 emissions fell between the first quarters of 2024 and 2025.","scaled",2025,[26],[198],"en",[],true,[202,206,209],{"url":203,"title":204,"publisher":191,"date":205},"https://blog.openairlines.com/case-study-icelandair-fuel-savings","Tracking fuel efficiency at Icelandair: from data to 247% fuel savings growth","2025-10-27",{"url":207,"title":208,"publisher":191},"https://www.openairlines.com/fuel-management-software/","SkyBreathe Fuel Management Software",{"url":210,"title":211,"publisher":191},"https://www.openairlines.com/pilot-engagement-app/","SkyBreathe MyFuelCoach, Pilot Engagement App",{"level":213,"checkedAt":170},"source-verified","C","icelandair-skybreathe-fuel-efficiency",null,{"title":218,"useCases":219,"organization":220,"vendors":224,"summary":226,"stage":194,"year":195,"channels":227,"languages":228,"metrics":229,"outcomeDisclosed":200,"sources":238,"verification":244,"grade":214,"id":245,"organizationSlug":246},"JetBlue: engine out taxi out compliance from 19% to 45% with SkyBreathe",[180],{"name":221,"anonymized":187,"country":222,"region":223,"industry":18},"JetBlue","US","north-america",[225],{"name":191,"role":192},"JetBlue, which operates 300 aircraft, set up a dedicated fuel optimization team and deployed SkyBreathe Analytics and the SkyBreathe MyFuelCoach pilot engagement app from OpenAirlines to turn flight data into fuel saving actions. OpenAirlines' case study headline claims a positive return on investment within three months; in the case study itself, Christopher Lum, JetBlue's Director and System Chief Pilot, reports that engine out taxi out compliance rose and fuel was saved over six months. Lum is quoted saying the airline is \"not asking the pilots to be perfect\" and wants to show them \"where the opportunity may have been\", which the case study frames as a deliberately non punitive approach.",[26],[198],[230],{"kpi":38,"value":231,"unit":232,"currency":71,"qualifier":233,"period":234,"claimant":235,"quote":236,"sourceUrl":237},300000,"currency","exact","in one month","vendor","$300,000 in savings just from engineout taxi in in one month.","https://blog.openairlines.com/fuel-efficiency-journey-jetblue",[239,242,243],{"url":237,"title":240,"publisher":191,"date":241},"JetBlue's fuel savings: positive ROI in 3 months","2025-12-15",{"url":207,"title":208,"publisher":191},{"url":210,"title":211,"publisher":191},{"level":213,"checkedAt":170},"jetblue-skybreathe-fuel-efficiency","jetblue",0,[249],{"kpi":38,"label":250,"unit":232,"currency":71,"aggregate":187,"higherIsBetter":200,"n":251,"nUpTo":247,"median":231,"min":231,"max":231,"byClaimant":252,"vendorOnly":200,"points":253},"Cost savings",1,{"organization":247,"vendor":251,"regulator":247,"independent":247},[254],{"evidenceId":245,"organization":221,"value":231,"qualifier":233,"claimant":235,"grade":214,"pooled":200},{"low":256,"high":257},1750000,9450000,[259,274,288,302],{"slug":168,"title":260,"shortTitle":261,"definition":262,"status":9,"industries":263,"functions":264,"patterns":265,"audience":27,"autonomy":266,"adoptionStage":29,"evidenceCount":267,"publicEvidenceCount":267,"organizations":268,"bestGrade":272,"headline":216,"lastVerified":273,"indexable":200},"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],[22],"copilot",3,[269,270,271],"American Airlines","British Airways","Swiss International Air Lines","B","2026-09-27",{"slug":275,"title":276,"shortTitle":277,"definition":278,"status":9,"industries":279,"functions":280,"patterns":281,"audience":27,"autonomy":28,"adoptionStage":29,"segment":283,"evidenceCount":267,"publicEvidenceCount":267,"organizations":284,"bestGrade":214,"headline":216,"lastVerified":170,"indexable":200},"ground-handling-and-turnaround-optimization","AI for aircraft turnaround and ground handling optimization","Ground handling and turnaround optimization","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.",[18],[20],[282,24,22],"computer-vision","ground operations",[285,286,287],"Alaska Airlines","Port of Seattle","Greater Toronto Airports Authority",{"slug":289,"title":290,"shortTitle":291,"definition":292,"status":9,"industries":293,"functions":294,"patterns":296,"audience":27,"autonomy":28,"adoptionStage":29,"segment":297,"evidenceCount":298,"publicEvidenceCount":298,"organizations":299,"bestGrade":272,"headline":216,"lastVerified":170,"indexable":200},"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],[20,295],"field-service",[22,24],"maintenance and engineering",2,[300,301],"Frontier Airlines","LATAM Airlines Group",{"slug":303,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":309,"patterns":310,"audience":27,"autonomy":313,"adoptionStage":29,"segment":314,"evidenceCount":267,"publicEvidenceCount":267,"organizations":315,"bestGrade":272,"headline":319,"lastVerified":326,"indexable":200},"freight-dispatch-and-load-matching-agent","AI agent for freight dispatch and load matching","Freight dispatch and load matching","An AI agent that does the coordination work behind moving a truckload: reading an emailed quote request and replying with a price, ranking which loads to show which carriers, matching pickup and delivery details to an open dock appointment slot, and chasing the exceptions, so a broker's or carrier's own staff plan lanes and handle disputes instead of typing quotes and making appointment calls.",[308],"logistics-and-transportation",[20],[311,23,312,22],"agentic-workflow","document-processing","supervised-agent","freight-brokerage",[316,317,318],"C.H. Robinson","J.B. Hunt Transport Services","Uber Freight",{"kpi":320,"label":321,"unit":322,"n":251,"nUpTo":247,"kind":323,"value":324,"qualifier":233,"claimant":325,"organization":318,"vendorReported":187},"conversion-rate-uplift","Conversion uplift","percent","reported",12,"organization","2026-09-28",{"indexable":200,"reasons":328},[],[330,336,341,349,356,363,369,376,384,391,398,404,410,416,423,430,436,443,448,454,460,467,472,477,482,489,494,499,506,511,519,526,532,538,543,548],{"id":137,"label":331,"issuer":332,"region":143,"url":333,"description":334,"useCases":335,"indexable":200},"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":138,"label":337,"issuer":332,"region":143,"url":338,"description":339,"useCases":340,"indexable":200},"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":342,"label":343,"issuer":344,"region":345,"url":346,"description":347,"useCases":348,"indexable":200},"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":350,"label":351,"issuer":352,"region":223,"url":353,"description":354,"useCases":355,"indexable":200},"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":357,"label":358,"issuer":359,"region":143,"url":360,"description":361,"useCases":362,"indexable":200},"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":364,"label":365,"issuer":332,"region":143,"url":366,"description":367,"useCases":368,"indexable":200},"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":370,"label":371,"issuer":372,"region":143,"url":373,"description":374,"useCases":375,"indexable":200},"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":377,"label":378,"issuer":379,"region":380,"url":381,"description":382,"useCases":383,"indexable":200},"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":385,"label":386,"issuer":387,"region":380,"url":388,"description":389,"useCases":390,"indexable":200},"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":392,"label":393,"issuer":394,"region":345,"url":395,"description":396,"useCases":397,"indexable":200},"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":399,"label":400,"issuer":401,"region":223,"url":402,"description":403,"useCases":397,"indexable":200},"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":405,"label":406,"issuer":332,"region":143,"url":407,"description":408,"useCases":409,"indexable":200},"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":411,"label":412,"issuer":413,"region":143,"url":414,"description":415,"useCases":409,"indexable":200},"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":417,"label":418,"issuer":419,"region":223,"url":420,"description":421,"useCases":422,"indexable":200},"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":424,"label":425,"issuer":426,"region":345,"url":427,"description":428,"useCases":429,"indexable":200},"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":431,"label":432,"issuer":332,"region":143,"url":433,"description":434,"useCases":435,"indexable":200},"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":437,"label":438,"issuer":439,"region":223,"url":440,"description":441,"useCases":442,"indexable":200},"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":444,"label":445,"issuer":332,"region":143,"url":446,"description":447,"useCases":442,"indexable":200},"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":449,"label":450,"issuer":451,"region":223,"url":452,"description":453,"useCases":442,"indexable":200},"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":455,"label":456,"issuer":457,"region":345,"url":458,"description":459,"useCases":324,"indexable":200},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":461,"label":462,"issuer":463,"region":223,"url":464,"description":465,"useCases":466,"indexable":200},"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":468,"label":469,"issuer":332,"region":143,"url":470,"description":471,"useCases":466,"indexable":200},"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":473,"label":474,"issuer":332,"region":143,"url":475,"description":476,"useCases":466,"indexable":200},"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":478,"label":479,"issuer":332,"region":143,"url":480,"description":481,"useCases":466,"indexable":200},"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":483,"label":484,"issuer":485,"region":143,"url":486,"description":487,"useCases":488,"indexable":200},"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":490,"label":491,"issuer":379,"region":380,"url":492,"description":493,"useCases":488,"indexable":200},"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":495,"label":496,"issuer":332,"region":143,"url":497,"description":498,"useCases":488,"indexable":200},"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":500,"label":501,"issuer":502,"region":223,"url":503,"description":504,"useCases":505,"indexable":200},"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":507,"label":508,"issuer":332,"region":143,"url":509,"description":510,"useCases":505,"indexable":200},"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":512,"label":513,"issuer":514,"region":515,"url":516,"description":517,"useCases":518,"indexable":200},"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":520,"label":521,"issuer":522,"region":143,"url":523,"description":524,"useCases":525,"indexable":200},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":527,"label":528,"issuer":529,"region":143,"url":530,"description":531,"useCases":525,"indexable":200},"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":533,"label":534,"issuer":535,"region":380,"url":536,"description":537,"useCases":267,"indexable":200},"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":539,"label":540,"issuer":332,"region":143,"url":541,"description":542,"useCases":267,"indexable":200},"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":544,"label":545,"issuer":332,"region":143,"url":546,"description":547,"useCases":267,"indexable":200},"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":549,"label":550,"issuer":551,"region":223,"url":552,"description":553,"useCases":267,"indexable":200},"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.",1790783077074]