[{"data":1,"prerenderedAt":565},["ShallowReactive",2],{"uc-airline-operations-control-decision-support":3,"uc-regulations":353},{"useCase":4,"evidence":176,"blitsAiDeployments":250,"benchmarks":251,"indicative":258,"related":261,"indexability":351,"includeUnpublished":182},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":23,"audience":25,"autonomy":26,"adoptionStage":27,"problem":28,"problemStats":29,"howItWorks":30,"valueDrivers":31,"kpis":36,"indicativeValue":41,"macroEstimates":76,"feasibility":77,"implementation":91,"risk":137,"blitsAi":157,"faq":159,"related":169,"datePublished":171,"dateModified":171,"lastVerified":171,"changelog":172,"slug":175},"AI decision support for airline operations control and disruption recovery","Airline operations control","AI airline disruption recovery and ops control","AI proposes aircraft swaps and retimings to controllers. SWISS says rotation optimization saved over CHF 1M; American says HEAT averted nearly 1,000 cancellations.","published","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.",[12,13,14,15,16],"airline operations control AI","irregular operations recovery optimization","IROPS decision support","airline schedule recovery","AI for the airline operations center",[18],"travel-and-hospitality",[20],"operations",[22],"prediction-and-scoring",[24],"internal-tools","employee-facing","copilot","early-adopters","An airline's plan for the day links every aircraft, crew and passenger connection. When a storm\ncloses a hub, an aircraft goes technical or a crew runs out of legal duty time, one change ripples\nthrough the network: the next flight has no aircraft, the crew is in the wrong city, passengers\nmiss connections and hotels fill up. Operations controllers have minutes to decide what to delay,\nswap or cancel.\n\nTraditionally they do it by experience, looking across separate systems for aircraft rotation,\ncrew, passengers and maintenance that were never built to optimize together. Decisions that are\ngood for one dimension, such as protecting the schedule, can be expensive in another, such as\npassenger compensation, crew overtime or noise charges. Passenger facing rebooking can soften\nthe damage, but the cost is set earlier, by the recovery plan the control center chooses.",[],"1. **Build one picture of the operation.** Aircraft rotations, crew pairings and legality,\n   passenger bookings and connections, maintenance status, airport and air traffic control\n   constraints and weather are replicated into one near real time data layer.\n2. **Predict trouble early.** Models forecast delays, missed connections and the effect of\n   forecast weather at hubs, hours ahead.\n3. **Generate recovery options.** Optimization searches for plans that retime, swap or cancel\n   flights and reassign crew, scoring each on cost, passenger impact, crew legality and\n   knock on effects.\n4. **Explain and propose.** The controller sees the recommended plan and its alternatives with\n   the trade offs in plain terms, including costs such as fuel, charges and passenger care.\n5. **Decide and execute.** Controllers accept, change or reject the plan; accepted changes flow\n   to the rotation, crew and passenger systems, which trigger rebooking and notifications.\n6. **Learn.** Outcomes of each event feed back into the models and cost functions.",[32,33,34,35],"cost-to-serve","customer-experience","speed","employee-productivity",[37,38,39,40],"cost-savings","cost-reduction","productivity-gain","customer-satisfaction",{"referenceOrg":42,"inputs":43,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"An airline operating 200,000 flights a year",[44,50,57,64],{"key":45,"label":46,"low":47,"high":47,"unit":48,"note":49},"flights","Flights per year",200000,"flights per year","The reference airline.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"disruptedShare","Share of flights that need a recovery decision",0.05,0.1,"fraction of flights","Editorial assumption for a network airline, replace with your own irregular operations data.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"costPerDisruption","Direct cost per disrupted flight",4000,10000,"USD per disrupted flight","Editorial assumption covering crew, passenger care and compensation, repositioning and charges. Replace with your own cost model.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"costReduction","Reduction in disruption cost from better recovery plans",0.02,0.03,"fraction of disruption cost","Editorial assumption, replace with your own. Neither deployment on this page reports savings from disruption recovery alone. SWISS reports more than CHF 1 million saved in the first 14 weeks of its rotation optimization feature, from lower fuel use and avoided charges such as airport noise charges, so that figure does not set this range.","flights * disruptedShare * costPerDisruption * costReduction","USD","per year","Disruption cost avoided","Counts only direct disruption cost. It leaves out savings from optimizing rotations on normal days, the revenue effect of fewer cancellations and missed connections, the cost of the data platform and optimization software, and the change in the control center's way of working.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":85},"high","The optimization is hard but available from specialist vendors and cloud providers. The larger effort is the data layer that joins rotation, crew, passenger and maintenance systems in near real time, cost functions the airline agrees on, and trust from controllers who are accountable for safe and legal operation.",[81,82,83,84],"Near real time aircraft rotation, crew pairing and passenger connection data","Crew legality rules (duty and rest limits) and qualifications in machine readable form","Agreed cost functions for delay, cancellation, passenger care, compensation and charges","Historical disruption events with the decisions taken and their outcomes",[86,87,88,89,90],"Operations control and aircraft rotation system","Crew management and tracking system","Passenger service and reservation system","Maintenance and technical fleet systems","Weather, air traffic flow and airport data feeds",{"steps":92,"guardrails":111,"humanInTheLoop":117,"kpisToInstrument":118,"failureModes":124},[93,96,99,102,105,108],{"title":94,"detail":95},"Start with one decision and one metric","Pick a frequent, contained decision such as aircraft swaps within a fleet or retiming at a hub before forecast weather, and agree how success is measured before building.",{"title":97,"detail":98},"Build the shared data layer","Replicate rotation, crew, passenger and maintenance data into one near real time view. This alone helps controllers, and every later optimization depends on it.",{"title":100,"detail":101},"Agree the cost functions","Put a price on delay minutes, missed connections, cancellations, crew overtime and charges, signed off by operations, finance and customer teams, so the optimizer and the controllers weigh options the same way.",{"title":103,"detail":104},"Recommend, do not execute","Show proposals with their trade offs and let controllers accept or reject them. Track the acceptance rate and the reasons for rejection as the main signal of fit.",{"title":106,"detail":107},"Connect to passenger recovery","Feed accepted plans straight into rebooking and customer notifications, so the passenger side starts as soon as the operational decision is taken.",{"title":109,"detail":110},"Extend to crew and network recovery","Add crew reassignment and multi hub recovery once the first use case is trusted, with crew legality checked by rules the optimizer cannot relax.",[112,113,114,115,116],"Crew duty and rest limits, qualifications and maintenance status are hard constraints the model cannot override","Controllers approve every plan before it changes the operation","Every proposal shows its cost assumptions and the alternatives considered","A manual fallback process is rehearsed for when the tool or its data feeds fail","Cost functions and model changes go through change control with operations sign off","Operations controllers and duty managers remain responsible for every decision. They approve, change or reject each proposal, and coordinators decide whether a tool such as a weather retiming plan is used at all for a given event. Crew schedulers confirm reassignments, and a review after each major disruption checks the tool's proposals against what happened.",[119,120,121,122,123],"Acceptance rate of proposals and reasons for rejection","Cancellations, delay minutes and missed connections per disruption event, against comparable events","Direct disruption cost per event (crew, passenger care, compensation, charges)","Time from disruption detection to an approved recovery plan","Controller workload and satisfaction with the tool",[125,128,131,134],{"title":126,"detail":127},"Optimizing on stale data","A plan built on an out of date crew or aircraft position is worse than none. Monitor data freshness and block proposals when feeds lag.",{"title":129,"detail":130},"Plans the controllers do not trust","If proposals ignore constraints controllers know about, acceptance collapses. Capture rejection reasons and turn them into constraints.",{"title":132,"detail":133},"Cheapest plan, worst experience","Cost functions that underprice passenger impact produce plans that save money and lose customers. Review the weights with customer teams.",{"title":135,"detail":136},"No fallback when the system fails","A recovery tool that fails during the peak of a meltdown leaves controllers without their usual process. Keep and rehearse the manual process.",{"euAiAct":138,"regulations":141,"guidance":144,"controls":151,"incidents":156},{"tier":139,"basis":140},"context-dependent","Recommending schedule, aircraft and passenger recovery plans to controllers is not listed in Annex III. Annex III point 4(b) covers AI used to make decisions affecting terms of work relationships, to allocate tasks based on individual behavior or personal traits or characteristics, or to monitor and evaluate the performance and behavior of workers. A design that reassigns individual crew members on such grounds, or that scores controllers or crew on their performance, falls in that category; one that works on flights, aircraft and crew legality and qualifications alone is less likely to, although assigning duties by qualification can still touch terms of work. The tool is not itself a safety component of an aircraft or other product regulated under Regulation (EU) 2018/1139, which Annex I Section B lists, so that route to high risk does not normally apply. Decisions that affect flight safety stay under aviation safety regulation and the airline's approved procedures; keep crew legality and maintenance limits as hard rules outside the model.",[142,143],"eu-ai-act","gdpr",[145],{"title":146,"issuer":147,"region":148,"url":149,"note":150},"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","Outlines EASA's vision for the safety and ethical considerations of AI in aviation.",[152,153,154,155],"Documented cost functions and hard constraints with an accountable owner in operations","Audit log of every proposal, the data behind it and the controller's decision","Post event reviews of major disruptions comparing proposals with outcomes","Tested manual fallback and data feed monitoring",[],{"howToBuild":158},"Blits.ai does not replace an operations research optimizer; it connects controllers and\ndownstream teams to one. An **AI agent** with **custom functions** can call the optimizer and\nthe operational systems' APIs, and read the current state from a **SQL knowledge base**, so a\ncontroller can ask in plain language which flights are at risk at a hub, what a proposed swap\ncosts or why a plan was recommended. A **knowledge base** with the airline's operations manual\nand procedures, retrieved with hybrid search, answers the rule questions.\n\n**Agentic workflows** with **human in the loop confirmation** can prepare a recovery proposal,\nwait for a controller to approve or reject it and then hand accepted changes to rebooking and\ncustomer notification, with an audit trail per run. Controllers and crew teams can use the\nassistant in **Microsoft Teams**, **monitors** check it daily against known questions, and the\nplatform is **model agnostic** with **EU and UAE data residency**.",[160,163,166],{"question":161,"answer":162},"How is this different from an AI rebooking agent?","A rebooking agent helps passengers after the airline has decided what to do. Operations control decision support shapes that decision: which flights to delay, swap or cancel and how to recover aircraft and crew. The two work best connected, so an approved plan triggers rebooking and notifications straight away.",{"question":164,"answer":165},"What results have airlines reported?","SWISS says the rotation optimization in its Operations Decision Support Suite saved more than CHF 1 million in its first three and a half months, and that controllers accept about nine in ten optimization runs. In 2023, American Airlines said its HEAT tool had prevented nearly 1,000 flight cancellations since its first use in April 2022; that is American's own figure, published without a baseline.",{"question":167,"answer":168},"Does the AI make the decisions?","No. At SWISS, operations controllers approve every change before it takes effect. At American, coordinators work with air traffic control and meteorologists to decide whether HEAT is used for a given storm, and Cranky Flier reports that HEAT starts from crew rest and availability. Beyond what these deployments disclose, a sound design keeps crew legality and maintenance limits as hard rules outside the model, because controllers remain accountable for safe and legal operation.",[170],"flight-disruption-and-rebooking-agent","2026-09-27",[173],{"date":171,"note":174},"First published","airline-operations-control-decision-support",[177,217],{"title":178,"useCases":179,"organization":180,"vendors":185,"summary":188,"stage":189,"year":190,"channels":191,"languages":192,"metrics":194,"outcomeDisclosed":195,"sources":196,"verification":212,"grade":214,"id":215,"organizationSlug":216},"American Airlines: HEAT, a machine learning tool that reshapes hub schedules ahead of storms",[175],{"name":181,"anonymized":182,"country":183,"region":184,"industry":18},"American Airlines",false,"US","north-america",[186],{"name":181,"role":187},"in-house","American Airlines built the Hub Efficiency Analytics Tool (HEAT) in house and has used it at its hubs since spring 2022. When severe weather is forecast, it weighs weather, load factors, customer connections, gate availability, air traffic control and crew constraints and shifts the departure and arrival times of flights at the hub, which operations center coordinators decide whether to apply. In a newsroom article from 2023, American says that since its initial deployment the year before, HEAT has prevented nearly 1,000 flight cancellations across its network. The same program includes intelligent gating at Dallas Fort Worth, which assigns the nearest available gate to arriving aircraft automatically.","production",2022,[24],[193],"en",[],true,[197,202,207],{"url":198,"title":199,"publisher":200,"archivedUrl":201},"https://news.aa.com/news/news-details/2023/Meet-HEAT-Americans-tool-to-manage-through-summer-storms-OPS-OTH-07/default.aspx","Meet HEAT: American's tool to manage through summer storms","American Airlines Newsroom","https://web.archive.org/web/2026/https://news.aa.com/news/news-details/2023/Meet-HEAT-Americans-tool-to-manage-through-summer-storms-OPS-OTH-07/default.aspx",{"url":203,"title":204,"publisher":205,"date":206},"https://www.cio.com/article/406357/american-airlines-takes-flight-with-analytics-transformation.html","American Airlines takes flight with analytics transformation","CIO","2022-09-07",{"url":208,"title":209,"publisher":210,"date":211},"https://crankyflier.com/2022/06/06/american-turns-on-heat-to-reduce-disruptions/","American Turns on HEAT to Reduce Disruptions","Cranky Flier","2022-06-06",{"level":213,"checkedAt":171},"source-verified","B","american-airlines-heat-hub-disruption-tool",null,{"title":218,"useCases":219,"organization":220,"vendors":223,"summary":227,"stage":189,"year":190,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":195,"sources":240,"verification":247,"grade":248,"id":249,"organizationSlug":216},"SWISS and Lufthansa Group: Operations Decision Support Suite for aircraft rotation and passenger recovery",[175],{"name":221,"anonymized":182,"country":222,"region":148,"industry":18},"Swiss International Air Lines","CH",[224],{"name":225,"role":226},"Google Cloud","platform","SWISS, part of Lufthansa Group, developed the Operations Decision Support Suite (OPSD) with Google Cloud as a layer above its operational systems. It replicates the state of the operation minute by minute with crew, passenger, rotation and technical data in one place, and uses optimization and machine learning to propose scenarios, such as swapping aircraft between flights or managing missed connections, that operations controllers approve before they take effect. The first use cases were rotation planning and passenger management. Lufthansa Group says OPSD lays the foundation for optimizing across the different operational dimensions, and eventually across airline borders, with the goal of rolling it out to other Lufthansa Group airlines.",[24],[],[231],{"kpi":37,"value":232,"unit":233,"currency":234,"qualifier":235,"period":236,"claimant":237,"quote":238,"sourceUrl":239},1000000,"currency","CHF","at-least","first three and a half months of the rotation optimization feature","organization","In three and a half months, this has generated more than a million Swiss Francs in savings.","https://cloud.google.com/customers/swiss",[241,243],{"url":239,"title":242,"publisher":225},"SWISS: Making air travel more sustainable and improving operational quality with Google Cloud",{"url":244,"title":245,"publisher":246},"https://innovation-runway.lufthansagroup.com/en/focus-areas-projects/projects/ops-suite.html","Operations Decision Support Suite (OPSD)","Lufthansa Group Innovation Runway",{"level":213,"checkedAt":171},"C","swiss-lufthansa-opsd-operations-decision-support",0,[252],{"kpi":37,"label":253,"unit":233,"currency":234,"aggregate":182,"higherIsBetter":195,"n":254,"nUpTo":250,"median":232,"min":232,"max":232,"byClaimant":255,"vendorOnly":182,"points":256},"Cost savings",1,{"organization":254,"vendor":250,"regulator":250,"independent":250},[257],{"evidenceId":249,"organization":221,"value":232,"qualifier":235,"claimant":237,"grade":248,"pooled":195},{"low":259,"high":260},800000,6000000,[262,292,311,332],{"slug":170,"title":263,"shortTitle":264,"definition":265,"status":9,"industries":266,"functions":267,"patterns":269,"audience":274,"autonomy":275,"adoptionStage":27,"evidenceCount":276,"publicEvidenceCount":276,"organizations":277,"bestGrade":214,"headline":284,"lastVerified":171,"indexable":195},"AI agent for flight disruption and rebooking","Flight disruption and rebooking","An AI agent that tells passengers proactively when their flight is delayed, cancelled or misconnected, explains why, and lets them rebook, request a refund or voucher, or claim care such as meals and hotels in one conversation on app, messaging, web or phone, within the airline's reaccommodation rules and passenger rights, handing complex itineraries and upset customers to a human with the context attached.",[18],[268,20],"customer-service",[270,271,272,273],"conversational-agent","voice-agent","agentic-workflow","content-generation","customer-facing","supervised-agent",6,[278,279,280,281,282,283],"Air India","Delta Air Lines","JetBlue","Lufthansa Group","Pegasus Airlines","United Airlines",{"kpi":285,"label":286,"unit":287,"n":254,"nUpTo":250,"kind":288,"value":289,"qualifier":290,"claimant":291,"organization":278,"vendorReported":195},"automation-rate","Automation rate","percent","reported",97,"exact","vendor",{"slug":293,"title":294,"shortTitle":295,"definition":296,"status":9,"industries":297,"functions":300,"patterns":302,"audience":25,"autonomy":275,"adoptionStage":27,"segment":305,"evidenceCount":306,"publicEvidenceCount":307,"organizations":308,"bestGrade":248,"headline":216,"lastVerified":171,"indexable":195},"fraud-alert-triage","AI agent for fraud alert triage","Fraud alert triage","An AI agent that works the fraud alert queue behind the scenes as the analyst's first pass, without contacting the customer: it enriches each alert with customer, device and payment context, closes clear false positives under documented rules, merges duplicates, and routes genuine risk to an analyst with a drafted rationale.",[298,299],"banking","payments",[301,20],"fraud-prevention",[272,303,304,22],"classification-and-routing","summarization","middle-office",3,2,[309,310],"Coast","SEB",{"slug":312,"title":313,"shortTitle":314,"definition":315,"status":9,"industries":316,"functions":318,"patterns":319,"audience":25,"autonomy":275,"adoptionStage":27,"segment":322,"evidenceCount":306,"publicEvidenceCount":306,"organizations":323,"bestGrade":214,"headline":327,"lastVerified":331,"indexable":195},"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.",[317],"logistics-and-transportation",[20],[272,320,321,22],"recommendation-and-personalization","document-processing","freight-brokerage",[324,325,326],"C.H. Robinson","J.B. Hunt Transport Services","Uber Freight",{"kpi":328,"label":329,"unit":287,"n":254,"nUpTo":250,"kind":288,"value":330,"qualifier":290,"claimant":237,"organization":326,"vendorReported":182},"conversion-rate-uplift","Conversion uplift",12,"2026-09-28",{"slug":333,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":341,"patterns":342,"audience":274,"autonomy":275,"adoptionStage":27,"evidenceCount":343,"publicEvidenceCount":344,"organizations":345,"bestGrade":214,"headline":216,"lastVerified":350,"indexable":195},"outbound-reminder-and-confirmation-agent","AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[338,339,340],"cross-industry","healthcare","government",[268,20],[271,270,272,22],5,4,[346,347,348,349],"Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","U.S. Department of Veterans Affairs","WellSpan Health","2026-09-26",{"indexable":195,"reasons":352},[],[354,360,365,373,380,386,393,400,408,415,422,428,435,442,448,453,460,465,471,477,483,489,495,500,505,512,519,524,529,536,542,548,554,559],{"id":142,"label":355,"issuer":356,"region":148,"url":357,"description":358,"useCases":359,"indexable":195},"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.",197,{"id":143,"label":361,"issuer":356,"region":148,"url":362,"description":363,"useCases":364,"indexable":195},"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.",180,{"id":366,"label":367,"issuer":368,"region":369,"url":370,"description":371,"useCases":372,"indexable":195},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":374,"label":375,"issuer":376,"region":184,"url":377,"description":378,"useCases":379,"indexable":195},"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.",83,{"id":381,"label":382,"issuer":356,"region":148,"url":383,"description":384,"useCases":385,"indexable":195},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":387,"label":388,"issuer":389,"region":148,"url":390,"description":391,"useCases":392,"indexable":195},"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.",64,{"id":394,"label":395,"issuer":396,"region":148,"url":397,"description":398,"useCases":399,"indexable":195},"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.",47,{"id":401,"label":402,"issuer":403,"region":404,"url":405,"description":406,"useCases":407,"indexable":195},"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.",36,{"id":409,"label":410,"issuer":411,"region":404,"url":412,"description":413,"useCases":414,"indexable":195},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":416,"label":417,"issuer":418,"region":369,"url":419,"description":420,"useCases":421,"indexable":195},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":423,"label":424,"issuer":425,"region":184,"url":426,"description":427,"useCases":421,"indexable":195},"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":429,"label":430,"issuer":431,"region":148,"url":432,"description":433,"useCases":434,"indexable":195},"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.",16,{"id":436,"label":437,"issuer":438,"region":369,"url":439,"description":440,"useCases":441,"indexable":195},"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":443,"label":444,"issuer":356,"region":148,"url":445,"description":446,"useCases":447,"indexable":195},"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":449,"label":450,"issuer":356,"region":148,"url":451,"description":452,"useCases":447,"indexable":195},"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.",{"id":454,"label":455,"issuer":456,"region":184,"url":457,"description":458,"useCases":459,"indexable":195},"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":461,"label":462,"issuer":356,"region":148,"url":463,"description":464,"useCases":330,"indexable":195},"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":466,"label":467,"issuer":468,"region":184,"url":469,"description":470,"useCases":330,"indexable":195},"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.",{"id":472,"label":473,"issuer":474,"region":369,"url":475,"description":476,"useCases":330,"indexable":195},"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":478,"label":479,"issuer":356,"region":148,"url":480,"description":481,"useCases":482,"indexable":195},"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.",11,{"id":484,"label":485,"issuer":486,"region":184,"url":487,"description":488,"useCases":482,"indexable":195},"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":490,"label":491,"issuer":403,"region":404,"url":492,"description":493,"useCases":494,"indexable":195},"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.",10,{"id":496,"label":497,"issuer":356,"region":148,"url":498,"description":499,"useCases":494,"indexable":195},"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":501,"label":502,"issuer":356,"region":148,"url":503,"description":504,"useCases":494,"indexable":195},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":506,"label":507,"issuer":508,"region":148,"url":509,"description":510,"useCases":511,"indexable":195},"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.",9,{"id":513,"label":514,"issuer":515,"region":184,"url":516,"description":517,"useCases":518,"indexable":195},"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.",8,{"id":520,"label":521,"issuer":356,"region":148,"url":522,"description":523,"useCases":518,"indexable":195},"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":525,"label":526,"issuer":356,"region":148,"url":527,"description":528,"useCases":276,"indexable":195},"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":530,"label":531,"issuer":532,"region":533,"url":534,"description":535,"useCases":343,"indexable":195},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":537,"label":538,"issuer":539,"region":148,"url":540,"description":541,"useCases":344,"indexable":195},"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":543,"label":544,"issuer":545,"region":148,"url":546,"description":547,"useCases":344,"indexable":195},"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":549,"label":550,"issuer":551,"region":404,"url":552,"description":553,"useCases":306,"indexable":195},"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":555,"label":556,"issuer":356,"region":148,"url":557,"description":558,"useCases":306,"indexable":195},"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":560,"label":561,"issuer":562,"region":184,"url":563,"description":564,"useCases":306,"indexable":195},"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.",1790598298471]