[{"data":1,"prerenderedAt":582},["ShallowReactive",2],{"uc-workforce-scheduling-in-stores":3,"uc-regulations":362},{"useCase":4,"evidence":168,"blitsAiDeployments":260,"benchmarks":261,"indicative":276,"related":279,"indexability":360,"includeUnpublished":174},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":41,"macroEstimates":76,"feasibility":77,"implementation":89,"risk":130,"blitsAi":150,"faq":152,"related":162,"datePublished":163,"dateModified":163,"lastVerified":163,"changelog":164,"slug":167},"AI workforce scheduling for retail stores","Workforce scheduling in stores","AI staff scheduling software for retail stores","AI builds retail staff schedules from sales and traffic data. Legion reports scheduling time cut by 66% at Helzberg and by 50% at SMCP North America.","published","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.",[12,13,14,15],"AI staff scheduling","retail labour scheduling software","demand driven store scheduling","AI shift scheduling for retail",[17],"retail-and-ecommerce",[19,20],"human-resources","operations",[22,23],"prediction-and-scoring","recommendation-and-personalization",[25,26],"mobile-app","internal-tools","employee-facing","supervised-agent","early-adopters","store-operations","Retail scheduling has to balance forecast demand, wage budgets, labour law, and what each\nhourly employee actually wants, and it has to be rebuilt often as conditions change. Legion\nreports that SMCP North America's managers spent over seven hours a month building schedules by\nhand before adopting Legion's AI scheduling platform, without good visibility into peak times,\nemployee preferences or compliance, and that updates travelled by paper or email. Legion's case\nstudy on Helzberg describes a similarly manual, time consuming process before it moved to an AI\nplatform: schedules were slow to build and hard to keep compliant with labour standards and\nbudget limits at the same time.\n\nThe cost is not only the manager's time. A schedule built on averages, not on the day's actual\ndemand drivers, either overstaffs quiet hours or leaves a store short handed at its busiest\nmoments, and disputes over shift swaps and time off requests add friction that a spreadsheet or\nemail chain cannot resolve fairly.",[],"1. **Forecast the demand.** The system forecasts sales, transactions or foot traffic per store,\n   per day and often per shorter interval, from historical sales, trends and known events.\n2. **Generate the schedule.** An optimisation model turns the forecast, labour standards, budget\n   limits and each employee's set availability, preferences and skill or performance data into a\n   draft schedule, matching the right people to the times the store needs them most.\n3. **Let employees shape it.** Employees set availability, bid on shifts, request time off and\n   swap shifts through a mobile app; every request is time stamped so competing requests are\n   resolved fairly and consistently.\n4. **Manager reviews, not rebuilds.** The manager reviews the draft, adjusts the exceptions that\n   need judgement, and publishes it, instead of building the schedule from a blank sheet.\n5. **Learn from what happens.** Actual sales, attendance and swap patterns feed back into the next\n   forecast and schedule, and flag policy violations before they become compliance incidents.",[35,36,37],"cost-to-serve","employee-productivity","compliance",[39,40],"productivity-gain","employee-adoption",{"referenceOrg":42,"inputs":43,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A specialty retail chain with 200 stores, one manager per store spending time on scheduling",[44,50,57,64],{"key":45,"label":46,"low":47,"high":48,"unit":45,"note":49},"stores","Stores in the chain",150,230,"Illustrative specialty retail chain, sized between SMCP North America's 100+ stores and Helzberg's 230 stores, both referenced on this page.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"managerHoursPerMonth","Manager hours spent scheduling per store per month, before AI scheduling",5,7,"hours per store per month","Legion reports that SMCP North America's managers spent over seven hours a month on manual scheduling before adopting Legion. That figure is not stated per store or per manager; it is treated as roughly per store here since SMCP North America runs one manager per store, and the low end of the range is kept for stores with lighter scheduling loads.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"hoursSavedShare","Share of that time saved after AI scheduling",0.4,0.57,"fraction of scheduling hours","The high end matches Legion's reported reduction for SMCP North America, from 7 to 3 hours a month, about 57%, and stays below Legion's reported 66% reduction in scheduling time for Helzberg.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"managerHourlyCost","Fully loaded cost of a store manager's time",20,35,"USD per hour","Editorial assumption, replace with your own fully loaded manager cost.","stores * managerHoursPerMonth * hoursSavedShare * managerHourlyCost * 12","USD","per year","Manager hours cost avoided from scheduling automation","Counts only the manager time saved on building the schedule. It leaves out the cost of the platform itself, any change in total labour cost from better matched shifts, and the extra sales Legion attributes to SMCP North America from managers spending more of that freed time selling and coaching, which would need its own conservative estimate.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"medium","The forecasting and optimisation logic is bought from a workforce management vendor, not built. The work is loading accurate labour rules and budget constraints per store or region, integrating with the time and attendance and payroll systems already in place, and getting manager and employee buy in for a system that changes a routine they own.",[81,82,83],"Historical sales, transactions or foot traffic per store, at the interval schedules are built","Labour standards, union rules and budget limits per store or region","Employee availability, skills and shift preferences, kept current",[85,86,87,88],"Point of sale or traffic system for the demand signal","Time and attendance and payroll systems","A mobile app or portal employees already use, for shift swaps and time off","HR system, for employee data and skill or role information",{"steps":90,"guardrails":106,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":117},[91,94,97,100,103],{"title":92,"detail":93},"Load real labour rules before the first schedule","Encode every applicable labour standard, meal break rule, minor work restriction and overtime threshold per store or region before go live, not after the first violation.",{"title":95,"detail":96},"Start with one region and a mixed set of stores","Pilot in stores with different traffic patterns and team sizes, not just the easiest ones, so the forecast and schedule quality is tested against real variety before a full rollout.",{"title":98,"detail":99},"Give employees a reason to use the app","Make shift swaps, availability and time off requests genuinely easier through the app than the old process, so adoption is pulled by employees, not pushed by managers.",{"title":101,"detail":102},"Keep the manager's review step meaningful","Show the manager why the draft schedule looks the way it does, so a review is a real check, not a rubber stamp of a schedule nobody understands.",{"title":104,"detail":105},"Watch labour cost and service level together","Track wage cost and customer facing coverage side by side after go live; a schedule that cuts cost by leaving the floor short handed at peak times is not a win.",[107,108,109,110],"Every published schedule checked against labour law and union rules before it goes live","A manager can override any AI generated shift, with the reason recorded","Shift preference and time off requests are timestamped and resolved in a documented, fair order","Employee performance data used in scheduling is limited to what is disclosed to employees and relevant to staffing","The manager reviews and publishes every schedule and owns every exception: late call outs, disputed swaps, a new hire without a performance history yet. Employees can flag a schedule they believe is wrong or unfair before it is published, and HR reviews recurring compliance flags rather than individual shifts.",[113,114,115,116],"Manager hours spent building and adjusting schedules, before and after","Schedule adherence and unplanned absence rate","Compliance violations caught before publish versus after","Employee app adoption and satisfaction with the scheduling process",[118,121,124,127],{"title":119,"detail":120},"Optimising labour cost against a bad forecast","A precise schedule built on an inaccurate demand forecast still under or overstaffs the floor. Track forecast accuracy separately from schedule compliance.",{"title":122,"detail":123},"Performance based scheduling without disclosure","Using individual sales or performance data to decide who gets the best shifts, without telling employees, damages trust and may raise fairness and works council questions. Disclose what data drives scheduling and let employees see their own record.",{"title":125,"detail":126},"Manager override becomes the norm","If managers routinely override the AI schedule, the model is not fitting the store's real constraints. Treat a high override rate as a signal to fix the inputs, not just a manager preference.",{"title":128,"detail":129},"Treating early pilot results as the steady state","The first stores to adopt are often the most engaged. Measure time saved and satisfaction again after the novelty wears off and across less enthusiastic stores.",{"euAiAct":131,"regulations":134,"guidance":137,"controls":144,"incidents":149},{"tier":132,"basis":133},"context-dependent","Annex III point 4 of the EU AI Act covers employment and worker management. Point 4(b) lists AI systems intended to be used to make decisions affecting terms of work related relationships, the promotion or termination of work related contractual relationships, to allocate tasks based on individual behaviour or personal traits or characteristics, or to monitor and evaluate a worker's performance and behaviour, as high risk. A scheduling system that allocates shifts based on an employee's own sales or productivity data falls within that category and needs the risk management, data governance, logging, human oversight and other obligations the Act sets for high risk systems. A system that schedules only from demand forecasts and each employee's stated availability is less likely to fall under Annex III, but a system whose schedules decide hours or other terms of work can still be caught by point 4(b), so the design, and any Article 6(3) exemption, needs assessing case by case.",[135,136],"eu-ai-act","gdpr",[138],{"title":139,"issuer":140,"region":141,"url":142,"note":143},"Annex III, point 4: employment, workers' management and access to self-employment","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Point 4(b) lists systems intended to be used to make decisions affecting terms of work related relationships, to allocate tasks based on individual behaviour or personal traits or characteristics, or to monitor and evaluate a worker's performance and behaviour, as high risk; a scheduler that only matches demand and stated availability is less likely to fall under Annex III, but a system whose schedules decide hours or other terms of work can still be caught by point 4(b).",[145,146,147,148],"Documented labour rule and budget logic, reviewed when local law changes","Human oversight of every published schedule, with override rights and a recorded reason","A published policy on what employee data feeds scheduling, and access for employees to their own record","Logging of schedule generation, overrides and compliance flags for audit",[],{"howToBuild":151},"Blits.ai does not forecast demand or optimise shifts itself; that stays with the retailer's\nworkforce management platform. What Blits.ai adds is the conversational layer around it. An\n**AI agent** with a **custom function** into the scheduling system lets employees ask about\ntheir own shifts, request a swap or report unavailability in plain language over **WhatsApp**,\ninstead of learning a separate portal. An **agentic workflow** with **human in the loop\nconfirmation** requires an approve or reject decision above a configurable threshold before a\nshift swap or time off request is finalised, and escalates disputes through **human handover**.\n\nA **knowledge base** holds the store's own scheduling policy and labour rules so employees get a\nconsistent answer to \"why was I not scheduled Saturday\", and **analytics** track how many\nrequests the agent resolves without needing that confirmation step. Because the platform is\n**model agnostic**, a retailer can run the same agent on the model it already trusts for other\nstaff facing tools.",[153,156,159],{"question":154,"answer":155},"Does AI scheduling remove the manager from the process?","No, and this page recommends against treating the AI generated schedule as final: a manager should keep reviewing and publishing it and owning every exception. Legion reports that SMCP North America's managers went from seven to three hours a month on scheduling, which freed time for coaching and selling rather than removing the manager's role.",{"question":157,"answer":158},"Is AI staff scheduling high risk under the EU AI Act?","It depends on the design. Annex III point 4(b) covers systems intended to be used to make decisions affecting terms of work related relationships, that allocate tasks based on individual behaviour or personal traits or characteristics, or that monitor and evaluate a worker's performance, which describes a scheduling system that factors in individual sales or productivity. A scheduler built only on demand forecasts and stated availability is less likely to fall under Annex III, but a system whose schedules decide hours or other terms of work can still be caught by point 4(b), so this needs a case by case assessment. When it does apply, the usual high risk obligations follow: risk management, human oversight, logging and a documented basis for the data used.",{"question":160,"answer":161},"What results have retailers reported?","Legion reports a 66% reduction in scheduling time at Helzberg and, at SMCP North America, a 50% reduction in scheduling time alongside a 22% figure it currently calls a boost in manager driven sales (an earlier Legion post about the same SMCP North America deployment labelled the same 22% figure manager productivity instead). Both are vendor reported case studies naming the retailer, not independently audited figures.",[],"2026-09-29",[165],{"date":163,"note":166},"First published","workforce-scheduling-in-stores",[169,204,228],{"title":170,"useCases":171,"organization":172,"vendors":177,"summary":181,"stage":182,"year":183,"channels":184,"languages":185,"metrics":186,"outcomeDisclosed":195,"sources":196,"verification":199,"grade":201,"id":202,"organizationSlug":203},"ALDO Group: 66% adoption in 3 months for Legion's AI scheduling and communication app",[167],{"name":173,"anonymized":174,"country":175,"region":176,"industry":17},"ALDO Group",false,"CA","north-america",[178],{"name":179,"role":180},"Legion Technologies","platform","ALDO Group, the Montreal based fashion footwear and accessories retailer that operates the ALDO, Call It Spring and GLOBO banners, replaced paper based schedule tracking and fragmented store communication with Legion's workforce management app for more than 5,000 associates across North America. Legion's case study reports 66% adoption of the app within 3 months and a 72% clock in improvement, and quotes ALDO's Senior Manager for Sales and Operations on giving associates mobile access to their schedules and freeing store managers to spend more time on the sales floor.","production",2025,[25,26],[],[187],{"kpi":40,"value":188,"unit":189,"qualifier":190,"period":191,"claimant":192,"quote":193,"sourceUrl":194},66,"percent","exact","in the first 3 months","vendor","66% adoption in 3 months, 72% clock-in improvement","https://legion.co/resources/case-studies/aldo-case-study/",true,[197],{"url":194,"title":198,"publisher":179},"How ALDO Group Transformed Operations and Boosted Engagement with Legion",{"level":200,"checkedAt":163},"source-verified","C","aldo-group-legion-scheduling",null,{"title":205,"useCases":206,"organization":207,"vendors":210,"summary":212,"stage":182,"year":183,"channels":213,"languages":214,"metrics":215,"outcomeDisclosed":195,"sources":216,"verification":226,"grade":201,"id":227,"organizationSlug":203},"Helzberg: 66% less time spent scheduling with Legion AI workforce management",[167],{"name":208,"anonymized":174,"country":209,"region":176,"industry":17},"Helzberg Diamonds","US",[211],{"name":179,"role":180},"Helzberg Diamonds, a Berkshire Hathaway owned jewelry retailer with 230 stores in 36 states and more than 1,600 employees, replaced manual scheduling with Legion's AI workforce management platform. Legion's case study reports a 66% reduction in scheduling time, shown as a headline statistic rather than a full sentence, and that schedules now factor in traffic, transactions, net sales history and employee performance and preferences. A divisional vice president of retail innovation and operations describes the generated schedule as ready within seconds.",[25,26],[],[],[217,220,223],{"url":218,"title":219,"publisher":179},"https://legion.co/legion-case-study-helzberg/","Driving 20-Carat Performance: How Legion WFM Helps Helzberg Associates Sparkle",{"url":221,"title":222,"publisher":179},"https://legion.co/page-sitemap.xml","Legion.co page sitemap (Helzberg branded image upload paths, dated May and July 2025)",{"url":224,"title":225,"publisher":179},"https://legion.co/resource-sitemap.xml","Legion.co resource sitemap (Helzberg case study page and image upload path, dated September 2025)",{"level":200,"checkedAt":163},"helzberg-ai-scheduling",{"title":229,"useCases":230,"organization":231,"vendors":233,"summary":235,"stage":182,"year":236,"channels":237,"languages":238,"metrics":239,"outcomeDisclosed":195,"sources":247,"verification":258,"grade":201,"id":259,"organizationSlug":203},"SMCP North America: Legion reports 50% faster scheduling and a 22% boost in manager driven sales",[167],{"name":232,"anonymized":174,"country":209,"region":176,"industry":17},"SMCP North America",[234],{"name":179,"role":180},"SMCP North America, the operator of Sandro, Maje, Claudie Pierlot and Fursac stores with more than 100 US and Canadian locations and over 500 hourly associates, replaced manual, paper and email based scheduling with Legion's AI workforce management platform during the COVID-19 pandemic. Legion's case study reports zero compliance violations after go live and full adoption of the optional employee mobile app.",2020,[25,26],[],[240,244],{"kpi":39,"value":241,"unit":189,"qualifier":190,"claimant":192,"quote":242,"sourceUrl":243},50,"SMCP North America replaced time-consuming manual scheduling with Legion's AI-powered Workforce Management (WFM) Platform, reducing scheduling time by 50% and increasing manager-driven sales by 22%.","https://legion.co/resources/case-studies/smcp-case-study/",{"kpi":40,"value":245,"unit":189,"qualifier":190,"claimant":192,"quote":246,"sourceUrl":243},100,"100% adoption of the optional mobile app for real-time schedule visibility",[248,250,254],{"url":243,"title":249,"publisher":179},"How SMCP Unlocked a New Superpower with AI-Based Scheduling",{"url":251,"title":252,"publisher":179,"date":253},"https://legion.co/blog/smcp-bringing-people-business-together-ai-based-scheduling/","SMCP: Bringing People and Business Together with AI-Based Scheduling","2020-11-19",{"url":255,"title":256,"publisher":179,"date":257},"https://legion.co/blog/smcp-luxury-retail-case-study/","SMCP's New Superpower: AI-Based Scheduling with Legion WFM","2024-08-22",{"level":200,"checkedAt":163},"smcp-ai-scheduling",0,[262,270],{"kpi":40,"label":263,"unit":189,"aggregate":195,"higherIsBetter":195,"n":264,"nUpTo":260,"median":265,"min":188,"max":245,"byClaimant":266,"vendorOnly":195,"points":267},"Employee adoption",2,83,{"organization":260,"vendor":264,"regulator":260,"independent":260},[268,269],{"evidenceId":259,"organization":232,"value":245,"qualifier":190,"claimant":192,"grade":201,"pooled":195},{"evidenceId":202,"organization":173,"value":188,"qualifier":190,"claimant":192,"grade":201,"pooled":195},{"kpi":39,"label":271,"unit":189,"aggregate":195,"higherIsBetter":195,"n":272,"nUpTo":260,"median":241,"min":241,"max":241,"byClaimant":273,"vendorOnly":195,"points":274},"Productivity gain",1,{"organization":260,"vendor":272,"regulator":260,"independent":260},[275],{"evidenceId":259,"organization":232,"value":241,"qualifier":190,"claimant":192,"grade":201,"pooled":195},{"low":277,"high":278},72000,385433.99999999994,[280,302,325,344],{"slug":281,"title":282,"shortTitle":283,"definition":284,"status":9,"industries":285,"functions":289,"patterns":290,"audience":27,"autonomy":291,"adoptionStage":29,"evidenceCount":292,"publicEvidenceCount":292,"organizations":293,"bestGrade":201,"headline":297,"lastVerified":163,"indexable":195},"ai-candidate-sourcing-and-talent-rediscovery","AI agent for candidate sourcing and talent rediscovery","AI candidate sourcing and rediscovery","AI that builds and works the candidate pipeline before an application arrives: it matches open roles against a company's own past applicants sitting unused in the applicant tracking system, ranks and surfaces the best fits for a recruiter to approach, and optimizes career site content and outreach to attract more of the right applicants, instead of a recruiter starting each search from an empty external search or a job board.",[286,287,288],"cross-industry","manufacturing","technology",[19],[23,22],"assist",3,[294,295,296],"AtkinsRéalis","Box","Forvia",{"kpi":298,"label":299,"unit":189,"n":264,"nUpTo":260,"kind":300,"value":301,"qualifier":190,"claimant":192,"organization":294,"vendorReported":195},"processing-time-reduction","Cycle time reduction","reported",30,{"slug":303,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":309,"patterns":310,"audience":27,"autonomy":28,"adoptionStage":29,"segment":313,"evidenceCount":292,"publicEvidenceCount":292,"organizations":314,"bestGrade":318,"headline":319,"lastVerified":324,"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.",[308],"logistics-and-transportation",[20],[311,23,312,22],"agentic-workflow","document-processing","freight-brokerage",[315,316,317],"C.H. Robinson","J.B. Hunt Transport Services","Uber Freight","B",{"kpi":320,"label":321,"unit":189,"n":272,"nUpTo":260,"kind":300,"value":322,"qualifier":190,"claimant":323,"organization":317,"vendorReported":174},"conversion-rate-uplift","Conversion uplift",12,"organization","2026-09-28",{"slug":326,"title":327,"shortTitle":328,"definition":329,"status":9,"industries":330,"functions":331,"patterns":333,"audience":335,"autonomy":28,"adoptionStage":336,"evidenceCount":337,"publicEvidenceCount":337,"organizations":338,"bestGrade":318,"headline":203,"lastVerified":343,"indexable":195},"retail-demand-forecasting-and-replenishment","AI demand forecasting and automated replenishment for retail","Demand forecasting and replenishment","Machine learning that forecasts demand for every item in every store or fulfillment center, day by day, from sales history, promotions, prices, weather and local events, and turns the forecast into automatic store and warehouse orders within limits set by planners, who handle the exceptions.",[17],[20,332],"analytics-and-reporting",[22,334],"anomaly-detection","back-office","mainstream",4,[339,340,341,342],"Albert Heijn","Morrisons","One Stop","Walmart","2026-09-27",{"slug":345,"title":346,"shortTitle":347,"definition":348,"status":9,"industries":349,"functions":351,"patterns":352,"audience":27,"autonomy":291,"adoptionStage":353,"segment":354,"evidenceCount":337,"publicEvidenceCount":337,"organizations":355,"bestGrade":318,"headline":203,"lastVerified":163,"indexable":195},"clinical-trial-site-selection-and-feasibility","AI for clinical trial site selection and feasibility","Clinical trial site selection","AI that scores and ranks candidate investigator sites and countries for a planned clinical trial by predicted enrollment speed, access to the eligible patient population and historical performance, so clinical operations teams choose and activate a shortlist of sites with a higher chance of meeting enrollment targets on time, instead of relying on which sites a study team happens to know.",[350],"pharma-and-life-sciences",[20,332],[22,23],"emerging","clinical development",[356,357,358,359],"Amgen","American Society of Clinical Oncology (ASCO)","Novartis","PSI CRO",{"indexable":195,"reasons":361},[],[363,366,371,379,386,393,398,404,412,419,426,433,439,445,452,459,465,472,478,483,489,496,501,508,513,518,523,529,535,541,548,554,560,566,571,576],{"id":135,"label":364,"issuer":140,"region":141,"url":142,"description":365,"useCases":48,"indexable":195},"EU AI Act","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.",{"id":136,"label":367,"issuer":140,"region":141,"url":368,"description":369,"useCases":370,"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.",207,{"id":372,"label":373,"issuer":374,"region":375,"url":376,"description":377,"useCases":378,"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.",122,{"id":380,"label":381,"issuer":382,"region":176,"url":383,"description":384,"useCases":385,"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.",92,{"id":387,"label":388,"issuer":389,"region":141,"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.",71,{"id":394,"label":395,"issuer":140,"region":141,"url":396,"description":397,"useCases":188,"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.",{"id":399,"label":400,"issuer":401,"region":141,"url":402,"description":403,"useCases":241,"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.",{"id":405,"label":406,"issuer":407,"region":408,"url":409,"description":410,"useCases":411,"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.",37,{"id":413,"label":414,"issuer":415,"region":408,"url":416,"description":417,"useCases":418,"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":420,"label":421,"issuer":422,"region":176,"url":423,"description":424,"useCases":425,"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.",22,{"id":427,"label":428,"issuer":429,"region":375,"url":430,"description":431,"useCases":432,"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.",21,{"id":434,"label":435,"issuer":140,"region":141,"url":436,"description":437,"useCases":438,"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.",17,{"id":440,"label":441,"issuer":442,"region":141,"url":443,"description":444,"useCases":438,"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.",{"id":446,"label":447,"issuer":448,"region":176,"url":449,"description":450,"useCases":451,"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.",16,{"id":453,"label":454,"issuer":455,"region":375,"url":456,"description":457,"useCases":458,"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":460,"label":461,"issuer":140,"region":141,"url":462,"description":463,"useCases":464,"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":466,"label":467,"issuer":468,"region":176,"url":469,"description":470,"useCases":471,"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":473,"label":474,"issuer":475,"region":176,"url":476,"description":477,"useCases":471,"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":479,"label":480,"issuer":140,"region":141,"url":481,"description":482,"useCases":322,"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":484,"label":485,"issuer":486,"region":375,"url":487,"description":488,"useCases":322,"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":490,"label":491,"issuer":492,"region":176,"url":493,"description":494,"useCases":495,"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.",11,{"id":497,"label":498,"issuer":140,"region":141,"url":499,"description":500,"useCases":495,"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.",{"id":502,"label":503,"issuer":504,"region":141,"url":505,"description":506,"useCases":507,"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.",10,{"id":509,"label":510,"issuer":407,"region":408,"url":511,"description":512,"useCases":507,"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.",{"id":514,"label":515,"issuer":140,"region":141,"url":516,"description":517,"useCases":507,"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":519,"label":520,"issuer":140,"region":141,"url":521,"description":522,"useCases":507,"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":524,"label":525,"issuer":140,"region":141,"url":526,"description":527,"useCases":528,"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.",9,{"id":530,"label":531,"issuer":532,"region":176,"url":533,"description":534,"useCases":54,"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.",{"id":536,"label":537,"issuer":140,"region":141,"url":538,"description":539,"useCases":540,"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.",6,{"id":542,"label":543,"issuer":544,"region":545,"url":546,"description":547,"useCases":53,"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":549,"label":550,"issuer":551,"region":141,"url":552,"description":553,"useCases":337,"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":555,"label":556,"issuer":557,"region":141,"url":558,"description":559,"useCases":337,"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":561,"label":562,"issuer":563,"region":408,"url":564,"description":565,"useCases":292,"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":567,"label":568,"issuer":140,"region":141,"url":569,"description":570,"useCases":292,"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":572,"label":573,"issuer":140,"region":141,"url":574,"description":575,"useCases":292,"indexable":195},"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":577,"label":578,"issuer":579,"region":176,"url":580,"description":581,"useCases":292,"indexable":195},"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.",1790699629320]