[{"data":1,"prerenderedAt":542},["ShallowReactive",2],{"uc-aircraft-predictive-maintenance":3,"uc-regulations":317},{"useCase":4,"evidence":182,"blitsAiDeployments":236,"benchmarks":237,"indicative":238,"related":241,"indexability":315,"includeUnpublished":188},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":40,"macroEstimates":74,"feasibility":75,"implementation":87,"risk":131,"blitsAi":151,"faq":153,"related":166,"datePublished":170,"dateModified":171,"lastVerified":171,"changelog":172,"slug":181},"AI predictive maintenance for aircraft fleets","Aircraft predictive maintenance","Aircraft predictive maintenance software","AI flags a developing fault before it grounds an aircraft. Lufthansa Technik reports LATAM Airlines Group's early results: 20% fewer delays and cancellations.","published","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.",[12,13,14,15,16],"predictive maintenance for aircraft","MRO predictive analytics","aircraft health monitoring AI","fleet health analytics","aircraft reliability prediction",[18],"travel-and-hospitality",[20,21],"operations","field-service",[23,24],"prediction-and-scoring","anomaly-detection",[26],"internal-tools","employee-facing","assist","early-adopters","maintenance and engineering","An unplanned technical fault is costly for an airline's schedule: the aircraft is grounded\n(AOG), the next flights on its rotation need another aircraft or a cancellation, and\npassengers, crew and the network all absorb the disruption. Aircraft generate more sensor and\nsystem data across a flight than any reliability team can review by eye fleet wide, and by the\ntime a fault shows up as a defect message or a crew reported write up, the window to fix it on a\nscheduled visit has often already closed.\n\nThe step change predictive analytics offers is moving from reacting to a fault message or a\ncrew reported defect to predicting a developing issue from patterns in full flight and system\ndata before it occurs. Lufthansa Technik says its AVIATAR Predictive Health Analytics module\nenables LATAM Airlines Group to optimize fleet maintenance \"by predicting potential technical\nissues before they occur, enhancing operational reliability and reducing unplanned maintenance\nevents.\" Frontier Airlines' own newsroom describes the same module as one that \"transforms\nunscheduled maintenance events into scheduled ones.\"",[],"1. **Watch the fleet.** Aircraft system and sensor data, from health monitoring feeds such as\n   ACARS or a quick access recorder, is analyzed continuously for the patterns that have\n   preceded past failures, by fleet type and, where the data allows, by individual aircraft.\n2. **Flag it early.** When a developing fault is likely, the tool alerts the airline's\n   reliability or engineering team with the affected system and the evidence behind the flag.\n3. **Plan the fix.** Engineering schedules the repair into the next planned maintenance visit,\n   or, if the risk is high enough, brings the aircraft in sooner on its own terms rather than an\n   unplanned AOG.\n4. **Confirm and close.** A certified engineer inspects and repairs the flagged system and\n   records the outcome, including whether the flag was correct.\n5. **Learn.** Confirmed and false alerts both feed back into the model, so the fleet's own\n   failure history keeps improving what counts as an early warning worth acting on.",[35,36,37],"cost-to-serve","risk-reduction","employee-productivity",[39],"cost-reduction",{"referenceOrg":41,"inputs":42,"formula":69,"currency":70,"period":71,"resultLabel":72,"caveat":73},"An airline with a fleet of 150 aircraft",[43,48,55,62],{"key":44,"label":45,"low":46,"high":46,"unit":44,"note":47},"aircraft","Fleet size",150,"The reference airline.",{"key":49,"label":50,"low":51,"high":52,"unit":53,"note":54},"aogEventsPerAircraft","Unplanned AOG events per aircraft per year without predictive analytics",3,6,"AOG events per aircraft per year","Editorial assumption for a modern narrow body fleet, counting any unplanned technical grounding (not only a multi day grounding); replace with your own reliability data.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"avoidableShare","Share of AOG events an early warning could have turned into scheduled maintenance",0.1,0.25,"fraction of AOG events","Editorial assumption, replace with your own reliability data. Not derived from Lufthansa Technik's reported 20% fewer delays and cancellations for LATAM Airlines Group, since that figure measures a different quantity (delays and cancellations, from LATAM's first results, with no stated baseline period or fleet scope), not the share of AOG events converted to scheduled maintenance.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"costPerAvoidedAog","Cost avoided per AOG event turned into scheduled maintenance",15000,40000,"USD per event","Editorial assumption covering delay, cancellation, repositioning and passenger care costs avoided when a fault is fixed on a scheduled visit instead of an AOG. Replace with your own cost model.","aircraft * aogEventsPerAircraft * avoidableShare * costPerAvoidedAog","USD","per year","AOG and disruption cost avoided","Counts only the cost of AOG events avoided. It leaves out the platform and data integration cost, the reliability team's time reviewing alerts, any change in scheduled maintenance capacity needed to absorb the extra planned work, and the false alert rate, which the evidence on this page does not disclose.",[],{"complexity":76,"complexityNote":77,"dataPrerequisites":78,"integrations":82},"high","The hard part is not the analytics but the data: a reliable streaming feed per aircraft type, enough historical failures to tell a real early warning from noise, and an engineering team that trusts the alerts enough to change a maintenance plan because of one. Smaller or newer fleets have less failure history to learn from.",[79,80,81],"Aircraft health and sensor data feeds per fleet type (for example ACARS or a quick access recorder)","Historical fault and repair history per fleet type, with enough failures to distinguish a real pattern from noise","An engineering process that can act on an alert, moving a scheduled visit forward or adding a task to one already planned",[83,84,85,86],"Fleet health monitoring or predictive analytics platform","Maintenance and engineering (M&E) system for work orders and scheduled visit planning","Electronic technical logbook, so flagged and confirmed faults are recorded against the aircraft","Parts and inventory system, so a scheduled fix can be planned against part availability",{"steps":88,"guardrails":107,"humanInTheLoop":112,"kpisToInstrument":113,"failureModes":118},[89,92,95,98,101,104],{"title":90,"detail":91},"Start with one fleet and one data feed","Pick the fleet type with the best quality health monitoring data and the highest AOG cost, connect that feed, and prove the alert is worth acting on before promising fleet wide coverage.",{"title":93,"detail":94},"Set a threshold engineering will actually act on","Agree with engineering how confident an alert needs to be before it changes a maintenance plan, and start conservative; a threshold nobody acts on is worse than no alert at all.",{"title":96,"detail":97},"Keep the decision with engineering","The tool flags a likely fault and the evidence behind it; a certified engineer decides whether and when to act. No maintenance plan changes automatically.",{"title":99,"detail":100},"Close the loop on every alert","Record whether each alert was confirmed or a false alarm, so the false alert rate is visible and the model, and engineering's trust in it, both improve over time.",{"title":102,"detail":103},"Plan scheduled capacity for the extra work","Early warnings only help if there is a scheduled visit slot to put the fix into; agree with maintenance planning how flagged work competes with the existing schedule.",{"title":105,"detail":106},"Extend fleet by fleet","Add the next fleet type once the first one shows a real reduction in AOG events, since each fleet type needs its own data feed and failure history.",[108,109,110,111],"Every alert states the evidence behind it and the confidence level, not just a system name","A certified engineer decides whether and when to act; no maintenance plan changes automatically","False alerts are tracked and reported alongside confirmed ones, not hidden in an aggregate save figure","Fleet health alerts feed the airline's existing maintenance program and minimum equipment list (MEL) process, they do not replace it","Reliability and engineering teams review every alert before it changes a maintenance plan, and a certified engineer signs off the repair once the aircraft is worked on. Maintenance planning decides how flagged work is scheduled against existing capacity.",[114,115,116,117],"Unplanned AOG events per fleet type, against a comparable prior period","Share of alerts confirmed as a real fault versus false alerts","Time from alert to a scheduled fix, and time from alert to an AOG it prevented","Engineering adoption, how often an alert is actually acted on",[119,122,125,128],{"title":120,"detail":121},"Alerts nobody trusts","A noisy model gets ignored, including the alerts that matter. Track and publish the false alert rate, and tune or retire a model that stays noisy.",{"title":123,"detail":124},"An early warning with nowhere to go","Engineering agrees the alert is real but has no scheduled visit slot to put the fix into before the risk becomes an AOG anyway. Plan scheduled maintenance capacity for flagged work, not just unplanned work.",{"title":126,"detail":127},"Confidence mistaken for certainty","A high confidence score is read as a confirmed diagnosis rather than a reason to inspect. Keep the language and the workflow clear that the tool flags risk, an engineer confirms the fault.",{"title":129,"detail":130},"Thin data on a small or new fleet","A fleet type with few aircraft or little failure history gives the model too little to learn from, and its alerts are unreliable. Start predictive analytics on the fleet type with the most history, and treat a new fleet type's early alerts with more skepticism.",{"euAiAct":132,"regulations":135,"guidance":138,"controls":145,"incidents":150},{"tier":133,"basis":134},"context-dependent","A tool that flags a likely fault for a certified engineer to confirm is not listed in Annex III: predictive maintenance does not decide access to a service, creditworthiness or employment. Annex I Section B point 20 lists Regulation (EU) 2018/1139 only in so far as it concerns the design, production and placing on the market of unmanned aircraft and their engines, propellers, parts and remote control equipment; it does not cover crewed airline fleets such as LATAM's or Frontier's. A ground based maintenance analytics tool for a crewed fleet is therefore not an Annex I Section B product or safety component in the first place, so it does not fall under Article 6(1) through that entry, and it stays advisory, with every finding going through the airline's approved maintenance program and a certified engineer's sign off. Article 108 separately amends Regulation (EU) 2018/1139 so that when EASA adopts implementing or delegated acts on AI systems that are safety components, it must take the AI Act's Chapter III Section 2 requirements into account; that amendment governs aircraft systems within EASA's own certification regime, not a ground based analytics tool like this one. A design where the tool's output determined continued airworthiness without human review would need a different assessment.",[136,137],"eu-ai-act","gdpr",[139],{"title":140,"issuer":141,"region":142,"url":143,"note":144},"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 and sets the action plan and rulemaking pace for its AI Programme.",[146,147,148,149],"Every alert is traceable to the data and confidence level behind it","Certified engineer sign off on every repair, recorded in the electronic technical logbook","False alert rate tracked and reviewed alongside confirmed alerts","Change control when the underlying model or its thresholds are updated",[],{"howToBuild":152},"The fault prediction model itself is a specialist capability that belongs in a dedicated fleet\nhealth platform with access to the raw sensor and flight data, not in a general purpose\nconversational agent. Blits.ai's role is the layer engineers actually talk to: an **AI agent**\nwith **custom functions** that call the fleet health platform's API for an aircraft's current\nalerts and their supporting evidence, combined with a **SQL knowledge base** over the airline's\nown fault and repair history, so an engineer can ask in plain language what an alert means and\nwhether a similar pattern has been seen on this fleet before.\n\nAlerts above an agreed confidence threshold can trigger an **agentic workflow** that drafts a\nsummary for the reliability team and, with **human in the loop confirmation**, notifies\nmaintenance planning through **Microsoft Teams** so the fix can be considered for the next\nscheduled visit, rather than a raw automated message with no context. The agent can log each\nalert's outcome (confirmed or false) as engineering closes it, which **custom dashboard\nwidgets** in **analytics** can surface as a confirmed versus false alert rate and time from\nalert to fix. **Monitors** check the agent's connection to the fleet health platform daily, and the\nplatform is **model agnostic**, with **EU and UAE data residency** for airlines that need it.",[154,157,160,163],{"question":155,"answer":156},"Does the AI decide what repair to make?","No. In the recommended design, the tool flags a developing fault and the evidence behind it, and a certified engineer reviews the flag and decides whether and when to act. Neither source on this page states who reviews an alert at LATAM or Frontier; this is general practice for the design, not a claim about either organization. The tool's job is to flag a developing problem early enough that engineering has a real choice, fix it on a scheduled visit rather than react to an aircraft on ground event.",{"question":158,"answer":159},"What results have airlines reported?","Lufthansa Technik reports that LATAM Airlines Group's first results with AVIATAR Predictive Health Analytics show 20% fewer delays and cancellations, without a stated baseline period or fleet scope. Frontier Airlines' own newsroom describes selecting the same module for its Airbus fleet at the end of 2025; it does not say the module is live on any aircraft or report a measured result.",{"question":161,"answer":162},"How much history does an airline need before this works?","Enough failures per fleet type for a model to tell a real early warning from noise. The evidence on this page does not disclose a minimum, but a newer or smaller fleet type should expect less reliable alerts at first and a longer period before the false alert rate settles down.",{"question":164,"answer":165},"What is the biggest implementation risk?","An early warning with nowhere to go: engineering agrees a fault is developing but has no scheduled visit slot to fix it in before the risk becomes an AOG anyway. Predictive alerts only pay off if maintenance planning has the capacity to act on them.",[167,168,169],"airline-operations-control-decision-support","industrial-asset-predictive-maintenance","field-technician-copilot-and-dispatch","2026-09-29","2026-09-30",[173,175,177,179],{"date":171,"note":174},"Published after review by an automated review workflow (independent skeptic review).",{"date":171,"note":176},"Fixed the EU AI Act basis (Annex I Section B point 20 only covers unmanned aircraft, not crewed fleets; corrected the Article 108 mechanism and dropped the unverified Article 2(2) article list), reframed the definition and problem around the sourced claim that the tool predicts an issue before it occurs, from Lufthansa Technik and Frontier, instead of an unsourced periodic versus continuous claim, moved adoptionStage to early-adopters, dropped mttr-reduction from kpis, corrected the EASA AI Roadmap 2.0 guidance note and the Frontier AMOS quote wording, expanded MEL, and softened the analytics dashboard claim in blitsAi.howToBuild.",{"date":171,"note":178},"Unpublished by an automated review workflow (independent skeptic review).",{"date":170,"note":180},"First published","aircraft-predictive-maintenance",[183,213],{"title":184,"useCases":185,"organization":186,"vendors":191,"summary":195,"stage":196,"year":197,"channels":198,"languages":199,"metrics":201,"outcomeDisclosed":188,"sources":202,"verification":208,"grade":210,"id":211,"organizationSlug":212},"Frontier Airlines: adds AVIATAR Predictive Health Analytics to its Airbus fleet",[181],{"name":187,"anonymized":188,"country":189,"region":190,"industry":18},"Frontier Airlines",false,"US","north-america",[192],{"name":193,"role":194},"Lufthansa Technik","platform","Frontier Airlines, an ultra low fare carrier that maintains its large Airbus A320 family fleet from its main hangar in Denver, selected AVIATAR Predictive Health Analytics, Condition Monitoring and the AI based Technical Repetitives Examination module to add to its existing Lufthansa Technik digital tech ops suite, alongside AMOS maintenance and engineering software it selected at the end of 2024 and flydocs digital records management it has used for almost a decade. Frontier's own newsroom describes Predictive Health Analytics as using full flight data to anticipate technical issues, turn unscheduled maintenance into scheduled maintenance and give proactive troubleshooting recommendations, and quotes Frontier's Director of Engineering and Fleet linking the wider ecosystem to better forecasting of reliability issues, though without a quantified result for Frontier specifically, and without saying the module is yet live on any aircraft.","announced",2025,[26],[200],"en",[],[203],{"url":204,"title":205,"publisher":206,"date":207},"https://news.flyfrontier.com/frontier-opts-for-more-of-lufthansa-techniks-digital-tech-ops-and-engineering-products/","Frontier Opts for More of Lufthansa Technik's Digital Tech Ops and Engineering Products","Frontier Airlines Newsroom","2025-12-15",{"level":209,"checkedAt":170},"source-verified","B","frontier-airlines-aviatar-predictive-maintenance",null,{"title":214,"useCases":215,"organization":216,"vendors":220,"summary":222,"stage":223,"year":197,"channels":224,"languages":225,"metrics":226,"outcomeDisclosed":227,"sources":228,"verification":233,"grade":234,"id":235,"organizationSlug":212},"LATAM Airlines Group: fewer delays and cancellations with AVIATAR Predictive Health Analytics",[181],{"name":217,"anonymized":188,"country":218,"region":219,"industry":18},"LATAM Airlines Group","CL","latin-america",[221],{"name":193,"role":194},"LATAM Airlines Group, the largest airline group in Latin America, signed a multi year contract with Lufthansa Technik to roll out the AVIATAR digital operations suite, including Predictive Health Analytics and the Electronic Technical Logbook, across its Airbus A320, Boeing 777 and Boeing 787 fleets, covering more than 300 aircraft. Predictive Health Analytics watches aircraft system data to flag developing technical issues before they cause a delay or cancellation, and the Electronic Technical Logbook digitizes cockpit to maintenance communication. Lufthansa Technik's own announcement reports LATAM's first results as fewer delays and cancellations, without giving a baseline period or fleet scope for the figure.","production",[26],[],[],true,[229],{"url":230,"title":231,"publisher":193,"date":232},"https://www.lufthansa-technik.com/en/latam-opts-for-lufthansa-technik-s-digital-platform-aviatar-cecbda242cc7c47c","LATAM opts for Lufthansa Technik's digital platform AVIATAR","2025-04-08",{"level":209,"checkedAt":170},"C","latam-airlines-aviatar-predictive-maintenance",0,[],{"low":239,"high":240},675000,9000000,[242,255,274,292],{"slug":167,"title":243,"shortTitle":244,"definition":245,"status":9,"industries":246,"functions":247,"patterns":248,"audience":27,"autonomy":249,"adoptionStage":29,"evidenceCount":51,"publicEvidenceCount":51,"organizations":250,"bestGrade":210,"headline":212,"lastVerified":254,"indexable":227},"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],[23],"copilot",[251,252,253],"American Airlines","British Airways","Swiss International Air Lines","2026-09-27",{"slug":168,"title":256,"shortTitle":257,"definition":258,"status":9,"industries":259,"functions":262,"patterns":263,"audience":27,"autonomy":28,"adoptionStage":29,"segment":264,"evidenceCount":265,"publicEvidenceCount":265,"organizations":266,"bestGrade":210,"headline":212,"lastVerified":254,"indexable":227},"AI predictive maintenance for industrial and energy assets","Industrial predictive maintenance","Machine learning that learns the normal behaviour of industrial and energy equipment from sensor and process data, flags early signs of degradation weeks or months before a failure, and turns them into prioritised maintenance work, so plants and utilities plan repairs instead of reacting to breakdowns.",[260,261],"energy-and-utilities","manufacturing",[20,21],[24,23],"asset-management",7,[267,268,269,270,271,272,273],"ADNOC","Colgate-Palmolive","Duke Energy","DuPont","Georgia-Pacific","Holcim","Shell",{"slug":169,"title":275,"shortTitle":276,"definition":277,"status":9,"industries":278,"functions":280,"patterns":282,"audience":27,"autonomy":249,"adoptionStage":286,"segment":287,"evidenceCount":51,"publicEvidenceCount":51,"organizations":288,"bestGrade":210,"headline":212,"lastVerified":254,"indexable":227},"AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","AI that decides whether a fault needs a site visit at all, predicts what work and parts a job will need, helps plan and update appointments, and gives technicians on site guided diagnosis and instant answers from manuals and past jobs, so more jobs are fixed on the first visit.",[279],"telecommunications",[21,20,281],"customer-service",[23,283,284,285],"rag-knowledge-assistant","conversational-agent","classification-and-routing","emerging","network",[289,290,291],"Bouygues Telecom","nbn","Openreach",{"slug":293,"title":294,"shortTitle":295,"definition":296,"status":9,"industries":297,"functions":298,"patterns":300,"audience":302,"autonomy":303,"adoptionStage":29,"segment":287,"evidenceCount":304,"publicEvidenceCount":304,"organizations":305,"bestGrade":210,"headline":212,"lastVerified":254,"indexable":227},"predictive-network-maintenance","AI for predictive network maintenance in telecom","Predictive network maintenance","Machine learning that spots the early signs of network failure, such as degrading cells, faulty customer equipment, ageing hardware or planned digging near fibre, and triggers a preventive fix, a remote reset or a targeted intervention before customers lose service.",[279],[299,21,20],"network-operations",[24,23,301],"agentic-workflow","back-office","supervised-agent",9,[306,307,308,309,310,311,312,313,314],"Grameenphone","KDDI","Orange","Telefónica España","Telkomsel","Telstra","TIM Brasil","Verizon","Vodafone",{"indexable":227,"reasons":316},[],[318,324,329,337,344,351,357,364,372,379,386,392,398,404,411,418,424,431,436,442,449,456,461,466,471,478,483,488,494,499,507,514,520,526,531,536],{"id":136,"label":319,"issuer":320,"region":142,"url":321,"description":322,"useCases":323,"indexable":227},"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":137,"label":325,"issuer":320,"region":142,"url":326,"description":327,"useCases":328,"indexable":227},"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":330,"label":331,"issuer":332,"region":333,"url":334,"description":335,"useCases":336,"indexable":227},"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":338,"label":339,"issuer":340,"region":190,"url":341,"description":342,"useCases":343,"indexable":227},"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":345,"label":346,"issuer":347,"region":142,"url":348,"description":349,"useCases":350,"indexable":227},"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":352,"label":353,"issuer":320,"region":142,"url":354,"description":355,"useCases":356,"indexable":227},"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":358,"label":359,"issuer":360,"region":142,"url":361,"description":362,"useCases":363,"indexable":227},"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":365,"label":366,"issuer":367,"region":368,"url":369,"description":370,"useCases":371,"indexable":227},"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":373,"label":374,"issuer":375,"region":368,"url":376,"description":377,"useCases":378,"indexable":227},"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":380,"label":381,"issuer":382,"region":333,"url":383,"description":384,"useCases":385,"indexable":227},"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":387,"label":388,"issuer":389,"region":190,"url":390,"description":391,"useCases":385,"indexable":227},"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":393,"label":394,"issuer":320,"region":142,"url":395,"description":396,"useCases":397,"indexable":227},"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":399,"label":400,"issuer":401,"region":142,"url":402,"description":403,"useCases":397,"indexable":227},"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":405,"label":406,"issuer":407,"region":190,"url":408,"description":409,"useCases":410,"indexable":227},"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":412,"label":413,"issuer":414,"region":333,"url":415,"description":416,"useCases":417,"indexable":227},"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":419,"label":420,"issuer":320,"region":142,"url":421,"description":422,"useCases":423,"indexable":227},"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":425,"label":426,"issuer":427,"region":190,"url":428,"description":429,"useCases":430,"indexable":227},"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":432,"label":433,"issuer":320,"region":142,"url":434,"description":435,"useCases":430,"indexable":227},"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":437,"label":438,"issuer":439,"region":190,"url":440,"description":441,"useCases":430,"indexable":227},"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":443,"label":444,"issuer":445,"region":333,"url":446,"description":447,"useCases":448,"indexable":227},"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":450,"label":451,"issuer":452,"region":190,"url":453,"description":454,"useCases":455,"indexable":227},"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":457,"label":458,"issuer":320,"region":142,"url":459,"description":460,"useCases":455,"indexable":227},"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":462,"label":463,"issuer":320,"region":142,"url":464,"description":465,"useCases":455,"indexable":227},"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":467,"label":468,"issuer":320,"region":142,"url":469,"description":470,"useCases":455,"indexable":227},"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":472,"label":473,"issuer":474,"region":142,"url":475,"description":476,"useCases":477,"indexable":227},"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":479,"label":480,"issuer":367,"region":368,"url":481,"description":482,"useCases":477,"indexable":227},"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":484,"label":485,"issuer":320,"region":142,"url":486,"description":487,"useCases":477,"indexable":227},"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":489,"label":490,"issuer":491,"region":190,"url":492,"description":493,"useCases":265,"indexable":227},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":495,"label":496,"issuer":320,"region":142,"url":497,"description":498,"useCases":265,"indexable":227},"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":500,"label":501,"issuer":502,"region":503,"url":504,"description":505,"useCases":506,"indexable":227},"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":508,"label":509,"issuer":510,"region":142,"url":511,"description":512,"useCases":513,"indexable":227},"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":515,"label":516,"issuer":517,"region":142,"url":518,"description":519,"useCases":513,"indexable":227},"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":521,"label":522,"issuer":523,"region":368,"url":524,"description":525,"useCases":51,"indexable":227},"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":527,"label":528,"issuer":320,"region":142,"url":529,"description":530,"useCases":51,"indexable":227},"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":532,"label":533,"issuer":320,"region":142,"url":534,"description":535,"useCases":51,"indexable":227},"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":537,"label":538,"issuer":539,"region":190,"url":540,"description":541,"useCases":51,"indexable":227},"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.",1790783084877]