[{"data":1,"prerenderedAt":528},["ShallowReactive",2],{"uc-restaurant-demand-and-labor-forecasting":3,"uc-regulations":301},{"useCase":4,"evidence":151,"blitsAiDeployments":220,"benchmarks":221,"indicative":230,"related":233,"indexability":299,"includeUnpublished":157},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":23,"audience":24,"autonomy":25,"adoptionStage":26,"segment":27,"problem":28,"problemStats":29,"howItWorks":30,"valueDrivers":31,"kpis":35,"indicativeValue":39,"macroEstimates":65,"feasibility":66,"implementation":77,"risk":118,"blitsAi":130,"faq":132,"related":145,"datePublished":146,"dateModified":146,"lastVerified":146,"changelog":147,"slug":150},"AI demand forecasting for restaurant labor and prep scheduling","Restaurant demand and labor forecasting","AI labor forecasting for restaurants","Fourth reports AI forecasting lifted Chili's scheduling accuracy 20% and saved 600 labor hours weekly across 1,200 restaurants. See how it works.","published","A machine learning model that predicts sales and guest traffic per restaurant, per daypart and often per menu item, from point of sale history, local events and weather, so managers can build staff schedules and prep lists against a forecast instead of a manual guess or last week's numbers.",[12,13,14,15],"restaurant labor forecasting","demand forecasting for restaurants","prep forecasting","daypart demand forecasting",[17],"travel-and-hospitality",[19,20],"operations","human-resources",[22],"prediction-and-scoring",[],"employee-facing","copilot","early-adopters","back-of-house","A restaurant manager building next week's schedule and prep list is really making a forecast by\nhand: how busy will Tuesday lunch be, how many burgers will sell before the game starts, how many\npeople does that need on the line. Fourth's own case study on Chili's describes the result of doing\nthis manually at scale: \"Chili's managers' manual forecasting kept them chained to their laptops\ninstead of out on the floor with their guests and staff,\" made worse by a spike in turnover after\nthe Covid-19 pandemic that left newer managers struggling with inconsistent, inaccurate\nforecasting.\n\nGet the forecast wrong and a restaurant is either overstaffed, which eats margin directly, or\nunderstaffed, which shows up in slow service, wasted or run out prep, and a harder shift for the\nteam on the floor, which itself feeds the turnover that made the forecasting problem worse in the\nfirst place.",[],"1. **Learn the pattern.** The model trains on each restaurant's own point of sale history, the\n   same way a long tenured manager learns a location's rhythm, but across every location and\n   every daypart at once.\n2. **Add what changes the pattern.** Local events, school calendars, weather and known promotions\n   or menu changes are layered on top of the base sales pattern.\n3. **Forecast sales and traffic per daypart.** The output is a projected sales and guest count for\n   each shift, not just a single daily number, so staffing can flex within the day.\n4. **Turn the forecast into a schedule.** Labor scheduling software uses the forecast, plus labor\n   law constraints and staff availability, to propose a shift by shift schedule for a manager to\n   review and publish.\n5. **Turn the same forecast into a prep list.** The same demand number drives how much of each\n   menu item to prep, so prep and staffing move off the same, single forecast instead of two\n   separate guesses.\n6. **Compare forecast to actual, every period.** Forecast accuracy is tracked against actual sales\n   so the model, and the manager's trust in it, improves over time.",[32,33,34],"cost-to-serve","employee-productivity","customer-experience",[36,37,38],"forecast-accuracy","hours-saved","productivity-gain",{"referenceOrg":40,"inputs":41,"formula":60,"currency":61,"period":62,"resultLabel":63,"caveat":64},"A casual dining chain with 1,200 restaurants",[42,47,53],{"key":43,"label":44,"low":45,"high":45,"unit":43,"note":46},"restaurants","Restaurants",1200,"Fourth's Chili's case study: 1,200 restaurants. The source does not say company operated.",{"key":48,"label":49,"low":50,"high":50,"unit":51,"note":52},"gmHoursSavedPerWeek","General manager hours saved on forecasting and scheduling, per restaurant per week",0.5,"hours per restaurant per week","Chili's, via Fourth: GMs saved 30 minutes a week on forecasting, or 600 labor hours a week across 1,200 restaurants.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"fullyLoadedManagerHourlyCost","Fully loaded cost of a general manager hour",30,45,"USD per hour","Editorial assumption for a US restaurant general manager, fully loaded (base pay, payroll taxes and benefits). Replace with your own figure.","restaurants * gmHoursSavedPerWeek * 52 * fullyLoadedManagerHourlyCost","USD","per year","Annual value of general manager time returned from manual forecasting","This values only the manager's own time saved on the forecasting task itself, as reported by Chili's. It leaves out any labor cost saved from more accurate hourly scheduling, any reduction in food waste from better prep forecasts, and the cost of the forecasting software and integration.",[],{"complexity":67,"complexityNote":68,"dataPrerequisites":69,"integrations":73},"medium","The forecasting model itself is a well understood problem if the restaurant already has clean point of sale history; the real work is integrating that forecast into the scheduling and prep workflow managers actually use, and building trust so managers do not simply override it.",[70,71,72],"At least one to two years of clean, per daypart point of sale sales history per restaurant","A calendar of known local events, promotions and menu changes per restaurant","Actual staffing and prep outcomes to check the forecast against and keep improving it",[74,75,76],"Point of sale system, for historical and near real time sales data","Labor scheduling and time and attendance system","Inventory or prep management system, if the same forecast drives prep quantities",{"steps":78,"guardrails":94,"humanInTheLoop":99,"kpisToInstrument":100,"failureModes":105},[79,82,85,88,91],{"title":80,"detail":81},"Start with sales and traffic, not the full schedule","Get the forecast itself accurate and trusted at the daypart level before automating the schedule or prep list end to end.",{"title":83,"detail":84},"Keep the manager as the final approver","Publish a proposed schedule and prep list from the forecast, but require a manager to review and approve it, at least until forecast accuracy is proven at that specific restaurant.",{"title":86,"detail":87},"Track forecast accuracy against actuals, per restaurant","A chain wide average hides restaurants where the model is systematically wrong. Track and review accuracy restaurant by restaurant, not only as a company average.",{"title":89,"detail":90},"Feed local knowledge back in","Give managers an easy way to flag a known local event or closure the model would not know about, and feed that back into future forecasts for that location.",{"title":92,"detail":93},"Measure manager time, not just accuracy","Fourth's Chili's results report minutes saved per manager per week alongside an accuracy improvement; instrument both, because a more accurate forecast that still takes as long to use has not solved the real problem.",[95,96,97,98],"Labor law constraints (minimum rest, predictive scheduling rules, minor work rules) enforced in the schedule the forecast feeds, not left to the model","A manager must review and approve every published schedule, at least during rollout","Forecast accuracy monitored per restaurant, with an alert when a location's error grows","No prep quantity automatically increased without a person able to override it for a known local event","The general manager reviews the proposed schedule and prep list before it is published or acted on, can flag a local event or anomaly the model would not otherwise know about, and remains accountable for the shift, not the forecast.",[101,102,103,104],"Forecast accuracy against actual sales, per restaurant and per daypart","Manager hours spent on forecasting and scheduling, before and after","Labor cost as a percentage of sales, before and after, on comparable trading weeks","Food waste or stock outs tied to prep quantities driven by the forecast",[106,109,112,115],{"title":107,"detail":108},"A chain average that hides bad forecasts at specific restaurants","Company wide accuracy improves while a handful of locations get worse, unnoticed. Review accuracy restaurant by restaurant, not only as an aggregate.",{"title":110,"detail":111},"Local knowledge the model cannot see","A road closure, a new competitor, a one off local event, none of these show up in sales history until it is too late. Give managers a fast way to override or flag it.",{"title":113,"detail":114},"Managers who stop reviewing and just approve","Once trust builds, review can become a rubber stamp, so a data error or a bad input event goes live unnoticed. Sample a share of published schedules for a genuine second look.",{"title":116,"detail":117},"Optimizing labor cost at the expense of service","A model tuned only to minimize labor cost can leave a restaurant short staffed against genuine demand spikes. Track service speed and satisfaction alongside labor cost, not labor cost alone.",{"euAiAct":119,"regulations":122,"guidance":124,"controls":125,"incidents":129},{"tier":120,"basis":121},"context-dependent","Annex III point 4(b) covers AI systems used to make decisions affecting the terms of a work relationship, its promotion or termination, to allocate tasks based on individual behaviour or personal traits, or to monitor and evaluate the performance and behaviour of workers. Working hours and shift allocation are terms of that relationship, so shift scheduling is the limb this reaches most directly. Classification turns on the system's intended purpose, not on whether a human signs off: Article 14 requires human oversight for systems that are already high risk, it does not exempt a system from the tier. A system whose only purpose is to forecast aggregate, restaurant level demand, with no output about any named worker, sits outside point 4(b). The same forecast, used by a system that allocates shifts to named workers or sets their hours, falls inside it regardless of any review step; staying out of that tier depends on the system's purpose, or on an Article 6(3) derogation, not on manager sign off.",[123],"eu-ai-act",[],[126,127,128],"A named manager reviews and approves every published schedule, not an automatic publish","Forecast accuracy tracked and reviewed per restaurant, not only company wide","Labor law rules (rest periods, predictive scheduling, minor work rules) enforced separately from the forecast, not inferred by the model",[],{"howToBuild":131},"Blits.ai does not produce the demand forecast itself; that stays with the restaurant's own\nforecasting or workforce management system. On Blits.ai this is an **agentic workflow** with a\n**custom function** that calls that external system's API, or a **SQL knowledge base**\nconnection that reads its forecast and historical data directly, so the workflow acts on that\nspecific chain's own numbers. A **flow** turns the forecast into a proposed schedule and prep\nlist against fixed rules (labor law constraints, prep par levels) and routes it to the general\nmanager for **human in the loop confirmation** before anything is published.\n\nThe platform is model agnostic for the large language models behind the agent and the flow, so a\nchain can change the reasoning model without rebuilding the workflow around it. The forecasting\nmodel itself is not a Blits.ai capability: it is read from, not run by, the workflow above.",[133,136,139,142],{"question":134,"answer":135},"How much can AI forecasting improve restaurant scheduling accuracy?","Reported results vary by chain and by what is measured. Fourth reports that Chili's improved forecasting accuracy by 20% and saved general managers 30 minutes a week, or 600 labor hours a week across 1,200 restaurants; Fourth separately reports a 7% increase in scheduling accuracy and a 22% reduction in over scheduled hours at Thai Leisure Group, the UK operator of Chaophraya and Thaikhun. Both are vendor reported figures for named customers, not independently audited.",{"question":137,"answer":138},"Does the AI decide the schedule, or does a manager?","Neither Fourth case study on this page says who publishes the final schedule at that specific chain; that detail is not disclosed. As a general pattern, and what this page's implementation playbook recommends, the software proposes a forecast and a draft schedule for a manager to review before it goes live. Whether a human signs off does not by itself change the EU AI Act tier: a system that only forecasts aggregate demand sits outside Annex III 4(b), while one that goes on to allocate shifts to named workers or set their hours can fall inside it either way.",{"question":140,"answer":141},"Does the same forecast drive food prep as well as labor?","It can, since both come from the same underlying demand forecast per daypart. None of the named deployments on this page discloses a food waste or stock out result specifically, only labor time and scheduling accuracy.",{"question":143,"answer":144},"What data does a restaurant need before starting?","Clean, per daypart point of sale history, ideally a year or more, plus a calendar of known local events and promotions. Fourth's Chili's case study cites over 20 years of Chili's own HotSchedules data as part of what it used to build the forecast.",[],"2026-09-29",[148],{"date":146,"note":149},"First published","restaurant-demand-and-labor-forecasting",[152,189],{"title":153,"useCases":154,"organization":155,"vendors":160,"summary":164,"stage":165,"year":166,"channels":167,"languages":168,"metrics":170,"outcomeDisclosed":179,"sources":180,"verification":184,"grade":186,"id":187,"organizationSlug":188},"Chili's: Fourth AI demand and labor forecasting",[150],{"name":156,"anonymized":157,"country":158,"region":159,"industry":17},"Chili's Grill & Bar (Brinker International)",false,"US","north-america",[161],{"name":162,"role":163},"Fourth","platform","Brinker International's Chili's Grill & Bar turned to workforce management vendor Fourth for AI demand forecasting after Chili's own general managers found manual forecasting time consuming and, following a spike in turnover after the Covid-19 pandemic, newer managers struggled with inconsistent and inaccurate forecasting across its 1,200 restaurants. Fourth's forecasting draws on more than 20 years of Chili's own HotSchedules data. Brinker International's VP of Asset Management, Jason Noorian, is quoted in Fourth's case study calling the accurate forecast \"foundational\" to getting the right team and guest experience.","scaled",2024,[],[169],"en",[171],{"kpi":37,"value":172,"unit":173,"qualifier":174,"period":175,"claimant":176,"quote":177,"sourceUrl":178},600,"hours","exact","per week, nationally, across 1,200 restaurants","vendor","Saved GM's 30 mins / week or 600 labor hours per week nationally","https://www.fourth.com/case-study/chilis-grill-and-bar",true,[181],{"url":178,"title":182,"publisher":162,"archivedUrl":183},"Chili's Boosts Scheduling Accuracy by 20% and Saves 600 Labor Hours a Week with Fourth's AI Forecasting","https://web.archive.org/web/20240616122446/https://www.fourth.com/case-study/chilis-grill-and-bar",{"level":185,"checkedAt":146},"source-verified","C","chilis-fourth-ai-labor-forecasting",null,{"title":190,"useCases":191,"organization":192,"vendors":196,"summary":198,"stage":165,"year":166,"channels":199,"languages":200,"metrics":201,"outcomeDisclosed":179,"sources":214,"verification":218,"grade":186,"id":219,"organizationSlug":188},"Thai Leisure Group: Fourth AI scheduling and forecasting",[150],{"name":193,"anonymized":157,"country":194,"region":195,"industry":17},"Thai Leisure Group","GB","europe",[197],{"name":162,"role":163},"Thai Leisure Group, the UK restaurant group behind the Chaophraya and Thaikhun Thai restaurant brands, moved from Excel based manual scheduling across its 16 restaurants to Fourth's workforce management platform. Fourth's case study describes the deployment as AI driven Revenue Based Scheduling that removes manual assumptions from forecasting and building schedules. Richard Simpson, the group's Director of Operations for Chaophraya, is quoted in Fourth's case study saying the system \"always learns\" as it gets more information about the business.",[],[169],[202,208],{"kpi":37,"value":203,"unit":173,"qualifier":204,"period":205,"claimant":176,"quote":206,"sourceUrl":207},56000,"at-least","in the 12 months reported, across the group's 16 restaurants","22% reduction in over scheduling equating to 56000+ hours in 12 months.","https://www.fourth.com/case-study/thai-leisure",{"kpi":209,"value":210,"unit":211,"qualifier":174,"period":212,"claimant":176,"quote":213,"sourceUrl":207},"revenue-uplift",15.7,"percent","period not stated","Proven technology from Fourth allowed managers to stay front of house and focus on delivering excellent customer service and great food, resulting in a 15.7% sales increase.",[215],{"url":207,"title":216,"publisher":162,"archivedUrl":217},"Boosting Sales by 15%: Thai Leisure's Journey to Operational Excellence with Fourth","https://web.archive.org/web/20240911065346/https://www.fourth.com/case-study/thai-leisure",{"level":185,"checkedAt":146},"thai-leisure-group-fourth-ai-scheduling",0,[222],{"kpi":37,"label":223,"unit":173,"aggregate":157,"higherIsBetter":179,"n":224,"nUpTo":220,"median":225,"min":172,"max":203,"byClaimant":226,"vendorOnly":179,"points":227},"Hours saved",2,28300,{"organization":220,"vendor":224,"regulator":220,"independent":220},[228,229],{"evidenceId":219,"organization":193,"value":203,"qualifier":204,"claimant":176,"grade":186,"pooled":179},{"evidenceId":187,"organization":156,"value":172,"qualifier":174,"claimant":176,"grade":186,"pooled":179},{"low":231,"high":232},936000,1404000,[234,256,270,287],{"slug":235,"title":236,"shortTitle":237,"definition":238,"status":9,"industries":239,"functions":241,"patterns":242,"audience":24,"autonomy":244,"adoptionStage":26,"segment":245,"evidenceCount":246,"publicEvidenceCount":246,"organizations":247,"bestGrade":186,"headline":251,"lastVerified":146,"indexable":179},"workforce-scheduling-in-stores","AI workforce scheduling for retail stores","Workforce scheduling in stores","AI that builds store staff schedules from forecast sales, foot traffic, labour rules and each employee's own preferences and skills, so a manager gets a compliant, demand matched schedule quickly, and only has to handle exceptions such as a late call out or a disputed shift swap.",[240],"retail-and-ecommerce",[20,19],[22,243],"recommendation-and-personalization","supervised-agent","store-operations",3,[248,249,250],"ALDO Group","Helzberg Diamonds","SMCP North America",{"kpi":252,"label":253,"unit":211,"n":224,"nUpTo":220,"kind":254,"value":255,"qualifier":174,"claimant":176,"organization":250,"vendorReported":179},"employee-adoption","Employee adoption","reported",100,{"slug":257,"title":258,"shortTitle":259,"definition":260,"status":9,"industries":261,"functions":262,"patterns":263,"audience":24,"autonomy":25,"adoptionStage":26,"evidenceCount":246,"publicEvidenceCount":246,"organizations":264,"bestGrade":268,"headline":188,"lastVerified":269,"indexable":179},"airline-operations-control-decision-support","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.",[17],[19],[22],[265,266,267],"American Airlines","British Airways","Swiss International Air Lines","B","2026-09-27",{"slug":271,"title":272,"shortTitle":273,"definition":274,"status":9,"industries":275,"functions":276,"patterns":277,"audience":24,"autonomy":280,"adoptionStage":26,"segment":281,"evidenceCount":246,"publicEvidenceCount":246,"organizations":282,"bestGrade":186,"headline":188,"lastVerified":286,"indexable":179},"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.",[17],[19],[278,279,22],"computer-vision","anomaly-detection","assist","ground operations",[283,284,285],"Alaska Airlines","Port of Seattle","Greater Toronto Airports Authority","2026-09-30",{"slug":288,"title":289,"shortTitle":290,"definition":291,"status":9,"industries":292,"functions":293,"patterns":294,"audience":24,"autonomy":280,"adoptionStage":26,"segment":295,"evidenceCount":224,"publicEvidenceCount":224,"organizations":296,"bestGrade":186,"headline":188,"lastVerified":286,"indexable":179},"airline-fuel-efficiency-optimization","AI for airline fuel efficiency optimization","Airline fuel efficiency optimization","An AI system that analyzes flight data, fuel burn, routing, taxi procedure and auxiliary power unit use, fleet wide, to coach pilots and flight operations engineers on where a flight over or under performed against a fuel baseline, flags the highest value savings opportunities by route and procedure, and tracks the savings over time.",[17],[19],[22,243,279],"flight operations",[297,298],"Icelandair","JetBlue",{"indexable":179,"reasons":300},[],[302,308,314,322,329,336,342,349,357,364,371,377,383,389,396,403,409,416,421,427,434,441,446,451,456,463,468,473,480,485,493,500,506,512,517,522],{"id":123,"label":303,"issuer":304,"region":195,"url":305,"description":306,"useCases":307,"indexable":179},"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":309,"label":310,"issuer":304,"region":195,"url":311,"description":312,"useCases":313,"indexable":179},"gdpr","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":315,"label":316,"issuer":317,"region":318,"url":319,"description":320,"useCases":321,"indexable":179},"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":323,"label":324,"issuer":325,"region":159,"url":326,"description":327,"useCases":328,"indexable":179},"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":330,"label":331,"issuer":332,"region":195,"url":333,"description":334,"useCases":335,"indexable":179},"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":337,"label":338,"issuer":304,"region":195,"url":339,"description":340,"useCases":341,"indexable":179},"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":343,"label":344,"issuer":345,"region":195,"url":346,"description":347,"useCases":348,"indexable":179},"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":350,"label":351,"issuer":352,"region":353,"url":354,"description":355,"useCases":356,"indexable":179},"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":358,"label":359,"issuer":360,"region":353,"url":361,"description":362,"useCases":363,"indexable":179},"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":365,"label":366,"issuer":367,"region":318,"url":368,"description":369,"useCases":370,"indexable":179},"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":372,"label":373,"issuer":374,"region":159,"url":375,"description":376,"useCases":370,"indexable":179},"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":378,"label":379,"issuer":304,"region":195,"url":380,"description":381,"useCases":382,"indexable":179},"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":384,"label":385,"issuer":386,"region":195,"url":387,"description":388,"useCases":382,"indexable":179},"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":390,"label":391,"issuer":392,"region":159,"url":393,"description":394,"useCases":395,"indexable":179},"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":397,"label":398,"issuer":399,"region":318,"url":400,"description":401,"useCases":402,"indexable":179},"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":404,"label":405,"issuer":304,"region":195,"url":406,"description":407,"useCases":408,"indexable":179},"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":410,"label":411,"issuer":412,"region":159,"url":413,"description":414,"useCases":415,"indexable":179},"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":417,"label":418,"issuer":304,"region":195,"url":419,"description":420,"useCases":415,"indexable":179},"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":422,"label":423,"issuer":424,"region":159,"url":425,"description":426,"useCases":415,"indexable":179},"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":428,"label":429,"issuer":430,"region":318,"url":431,"description":432,"useCases":433,"indexable":179},"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":435,"label":436,"issuer":437,"region":159,"url":438,"description":439,"useCases":440,"indexable":179},"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":442,"label":443,"issuer":304,"region":195,"url":444,"description":445,"useCases":440,"indexable":179},"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":447,"label":448,"issuer":304,"region":195,"url":449,"description":450,"useCases":440,"indexable":179},"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":452,"label":453,"issuer":304,"region":195,"url":454,"description":455,"useCases":440,"indexable":179},"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":457,"label":458,"issuer":459,"region":195,"url":460,"description":461,"useCases":462,"indexable":179},"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":464,"label":465,"issuer":352,"region":353,"url":466,"description":467,"useCases":462,"indexable":179},"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":469,"label":470,"issuer":304,"region":195,"url":471,"description":472,"useCases":462,"indexable":179},"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":474,"label":475,"issuer":476,"region":159,"url":477,"description":478,"useCases":479,"indexable":179},"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":481,"label":482,"issuer":304,"region":195,"url":483,"description":484,"useCases":479,"indexable":179},"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":486,"label":487,"issuer":488,"region":489,"url":490,"description":491,"useCases":492,"indexable":179},"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":494,"label":495,"issuer":496,"region":195,"url":497,"description":498,"useCases":499,"indexable":179},"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":501,"label":502,"issuer":503,"region":195,"url":504,"description":505,"useCases":499,"indexable":179},"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":507,"label":508,"issuer":509,"region":353,"url":510,"description":511,"useCases":246,"indexable":179},"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":513,"label":514,"issuer":304,"region":195,"url":515,"description":516,"useCases":246,"indexable":179},"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":518,"label":519,"issuer":304,"region":195,"url":520,"description":521,"useCases":246,"indexable":179},"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":523,"label":524,"issuer":525,"region":159,"url":526,"description":527,"useCases":246,"indexable":179},"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.",1790783076162]