[{"data":1,"prerenderedAt":574},["ShallowReactive",2],{"uc-retail-demand-forecasting-and-replenishment":3,"uc-regulations":359},{"useCase":4,"evidence":171,"blitsAiDeployments":273,"benchmarks":274,"indicative":275,"related":278,"indexability":357,"includeUnpublished":177},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":39,"indicativeValue":44,"macroEstimates":78,"feasibility":79,"implementation":93,"risk":139,"blitsAi":153,"faq":155,"related":165,"datePublished":166,"dateModified":166,"lastVerified":166,"changelog":167,"slug":170},"AI demand forecasting and automated replenishment for retail","Demand forecasting and replenishment","AI demand forecasting for retail replenishment","AI forecasts demand per store and item and turns it into stock orders. Albert Heijn makes almost 1 billion forecasts a day; Morrisons replaced manual replenishment.","published","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.",[12,13,14,15,16],"AI demand forecasting for retail","automated store replenishment","machine learning replenishment","AI inventory forecasting","grocery demand planning with AI",[18],"retail-and-ecommerce",[20,21],"operations","analytics-and-reporting",[23,24],"prediction-and-scoring","anomaly-detection",[26,27],"api","internal-tools","back-office","supervised-agent","mainstream","A supermarket decides every day how much of each product to send to each store. Order too little\nand the shelf is empty, the sale is lost and the customer may go elsewhere. Order too much and\nfresh food expires, ties up cash and ends up as markdowns or waste. With tens of thousands of\nproducts and hundreds of stores, that is millions of small decisions a day.\n\nTraditional replenishment ran on averages, rules of thumb and store staff counting shelves and\nkeying in orders. It copes badly with the things that move demand from one day to the next:\npromotions, price changes, weather, holidays, new products and one product cannibalizing another. The cost shows up twice,\nin empty shelves and in waste, and grocers have both commercial and public commitments to reduce\nfood waste.",[],"1. **Assemble the demand signal.** Sales history per item and location, prices, promotions,\n   planned events, weather forecasts, holidays and stock positions are loaded daily.\n2. **Forecast at the level decisions are made.** Machine learning models forecast demand per\n   item, store and day, days or weeks ahead, and learn effects such as weather on ice or\n   cannibalization between promoted soft drinks.\n3. **Turn forecasts into orders.** A replenishment engine converts the forecast into orders,\n   taking into account stock on hand, shelf life, pack sizes, delivery schedules and the service\n   level chosen for each product.\n4. **Flag exceptions.** Unusual forecasts, sudden sales changes and data gaps are flagged for a\n   planner instead of being ordered blindly.\n5. **Clear what is left.** Some retailers add dynamic markdowns, raising the discount during the\n   day for products close to their date; Albert Heijn does this with electronic shelf labels.\n6. **Learn every day.** Actual sales, waste and stockouts feed back into the next forecast.",[35,36,37,38],"cost-to-serve","revenue-growth","risk-reduction","employee-productivity",[40,41,42,43],"forecast-accuracy","productivity-gain","cost-reduction","revenue-uplift",{"referenceOrg":45,"inputs":46,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"A grocery chain with EUR 2 billion in annual sales",[47,53,60,67],{"key":48,"label":49,"low":50,"high":50,"unit":51,"note":52},"sales","Annual sales",2000000000,"EUR per year","The reference retailer.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"wasteShare","Stock written off as waste or deep markdown, as a share of sales",0.02,0.04,"fraction of sales","Editorial assumption for a grocer with a large fresh range. Replace with your own shrink and waste figures.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"costRatio","Cost of goods as a share of the sales value",0.7,0.75,"fraction","Editorial assumption, so that waste is valued at cost rather than at selling price.",{"key":68,"label":69,"low":57,"high":70,"unit":71,"note":72},"wasteReduction","Reduction in waste from better forecasts",0.1,"fraction of waste","Editorial assumption, replace with your own. No source on this page measures the waste cut by machine learning forecasting itself. For orientation only: RELEX reports a 4% reduction in fresh spoilage value (fresh products only, a vendor claim) from One Stop's 2019 forecasting and replenishment rollout, before machine learning was added; after adding machine learning, One Stop reports higher ultra fresh availability with no corresponding rise in spoilage. Albert Heijn hopes to cut its total food waste by more than 10% with its forecasting, a target rather than a result; its separate Dynamic Markdown initiative saves 250,000 kilos of food a year, a markdown effect rather than a forecasting one.","sales * wasteShare * costRatio * wasteReduction","EUR","per year","Cost of stock no longer written off","Counts only waste avoided at cost. It leaves out the sales won back from fewer empty shelves, the working capital released by lower stock, the store hours saved on manual ordering and the cost of the forecasting platform and the data work.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":87},"high","Forecasting models are mature and available from several vendors. The hard parts are clean, timely data from every store (sales, stock, deliveries, waste), promotion and price calendars the model can trust, and changing the operating model so store staff and planners stop overriding the system out of habit.",[83,84,85,86],"Several years of sales history per item and location, including promotions and prices","Accurate stock on hand, deliveries and waste recorded per store","Promotion, price and range change calendars known in advance","Supply constraints such as lead times, pack sizes and delivery schedules",[88,89,90,91,92],"Point of sale and ecommerce order data","Inventory and store stock systems","Ordering, warehouse and supplier systems","Promotion, pricing and range planning tools","Weather and event data feeds",{"steps":94,"guardrails":113,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[95,98,101,104,107,110],{"title":96,"detail":97},"Pick the categories where error costs most","Start with fresh, short shelf life and weather driven categories, where both waste and empty shelves are expensive and the gain over rules of thumb is largest.",{"title":99,"detail":100},"Fix stock and waste data first","A forecast is only as good as the stock figure it starts from. Audit stock accuracy and make recording of waste and markdowns part of the store routine before trusting automatic orders.",{"title":102,"detail":103},"Run in parallel against a holdout","Compare model forecasts and proposed orders with current orders for several weeks, on forecast error, availability and waste per category, and agree the targets before switching.",{"title":105,"detail":106},"Automate with limits and exceptions","Let the system place orders within bounds (maximum change against last week, maximum stock cover) and send exceptions to planners with the reason the forecast changed.",{"title":108,"detail":109},"Bring stores along","Explain why an order looks the way it does and track overrides. Stores that keep overriding need either better data or better explanations, not permission to ignore the system.",{"title":111,"detail":112},"Add markdowns and allocation later","Once forecasts are stable, link them to dynamic markdowns for products close to their date and to allocation across stores and fulfillment centers.",[114,115,116,117,118],"Order limits per item and store, with larger changes held for a planner","Minimum service levels set per product so essentials are never cut to meet a waste target","Human review of forecasts for new products, major promotions and unusual events","Monitoring of data freshness, so a missing sales feed stops automatic ordering instead of producing zeros","Override tracking with reasons, reviewed weekly","Planners set the service levels, order limits and promotion inputs, review exceptions and approve forecasts for new lines and big events. Store managers can override orders, but every override is logged with a reason and reviewed, because overrides are the main way a good forecast loses its value.",[121,122,123,124,125],"Forecast error per category at the level orders are placed (item, store, day)","Shelf availability and lost sales estimates per category","Waste and markdowns as a share of sales, per category and store","Days of stock cover in stores and distribution centers","Share of orders placed without manual change, and override reasons",[127,130,133,136],{"title":128,"detail":129},"Garbage stock data","Phantom stock (the system thinks it is there, the shelf is empty) stops reordering. Audit stock accuracy and let stores flag empty shelves quickly.",{"title":131,"detail":132},"Promotions the model did not know about","A promotion or price change missing from the calendar produces a stockout or a mountain of waste. Make the promotion calendar a governed input with deadlines.",{"title":134,"detail":135},"Override culture","Staff who distrust the system reorder by hand and the gain disappears. Explain orders, measure overrides and fix the data behind them.",{"title":137,"detail":138},"Optimizing waste at the expense of availability","A tight waste target empties shelves. Set service levels per product and measure both waste and availability.",{"euAiAct":140,"regulations":143,"guidance":146,"controls":147,"incidents":152},{"tier":141,"basis":142},"minimal","Forecasting product demand and ordering stock is not listed in Annex III. It would become high risk under Annex III point 4(b) only if the same system allocated tasks to employees based on their individual behavior or personal traits, or monitored and evaluated their performance, for example scheduling store staff by individual productivity. GDPR applies only when loyalty or customer level data feeds the forecasts; item and store aggregates on their own are not personal data.",[144,145],"eu-ai-act","gdpr",[],[148,149,150,151],"Documented model ownership, validation per category and monitoring of forecast error","Change control for model updates, order limits and service levels","Data quality monitoring on sales, stock and promotion feeds","Audit trail of automatic orders and manual overrides",[],{"howToBuild":154},"Blits.ai does not replace the forecasting and replenishment engine; it makes that engine easier\nto use and to govern. The retailer's own database of forecasts, orders and stock (PostgreSQL, for\nexample) can be registered as a **SQL knowledge base** that an **AI agent** queries directly, so a planner or store manager can ask in plain language why an order for an\nitem looks the way it does, and get the drivers back from the data. **Custom functions** call\nthe replenishment system's API to read exceptions and to submit approved overrides.\n\nAn **agentic workflow** can run each morning over the exception list, summarize the unusual\nforecasts with their likely causes and put proposed changes in front of a planner through\n**human in the loop confirmation**, with an audit trail per run. Store teams can reach the same\nassistant in **Microsoft Teams**, and **monitors** check every day that the assistant still\nanswers a set of known questions correctly. The platform is **model agnostic** and offers **EU\nand UAE data residency**.",[156,159,162],{"question":157,"answer":158},"How accurate is AI demand forecasting in retail?","It depends on the category and the level of detail. RELEX reports that machine learning raised One Stop's forecast accuracy by 3.17 percentage points at product and week level within four months, and 1.82 points at product, store and week level. Accuracy tends to be lower at finer levels of detail, so measure it at the level where orders are placed.",{"question":160,"answer":161},"Can store orders really be automated?","Yes, within limits. Morrisons replaced its manual model of stock replenishment with Blue Yonder's AI powered demand forecasting and replenishment in its stores, and One Stop manages forecasting and replenishment in one RELEX system that draws store level forecasts automatically into replenishment planning. Keep planners in charge of the rules and the exceptions.",{"question":163,"answer":164},"Does it reduce food waste?","It can, but published figures are few, mostly vendor claims, and none isolates machine learning. RELEX reports a 4% cut in One Stop's fresh spoilage value from its 2019 forecasting and replenishment rollout, before machine learning was added; after adding machine learning, One Stop reports 8.5% higher availability of ultra fresh products with no corresponding rise in spoilage. Albert Heijn hopes forecasting will cut its total food waste by more than 10%, a target rather than a result. Microsoft's customer story reports that Albert Heijn's Dynamic Markdown initiative, which discounts products close to their date, now saves 250,000 kilos of food a year. Measure waste and availability together, because a tight waste target can empty shelves.",[],"2026-09-27",[168],{"date":166,"note":169},"First published","retail-demand-forecasting-and-replenishment",[172,201,226,248],{"title":173,"useCases":174,"organization":175,"vendors":180,"summary":184,"stage":185,"year":186,"channels":187,"languages":188,"metrics":190,"outcomeDisclosed":177,"sources":191,"verification":196,"grade":198,"id":199,"organizationSlug":200},"Walmart: AI forecasting that positions inventory across stores and fulfillment centers",[170],{"name":176,"anonymized":177,"country":178,"region":179,"industry":18},"Walmart",false,"US","north-america",[181],{"name":182,"role":183},"Walmart Global Tech","in-house","Walmart Global Tech describes how Walmart's supply chain systems use AI and forecasting models, drawing on signals such as historical sales, seasonality, local demand and weather, to decide which products are needed and where to position them across stores and fulfillment centers before customers order. Walmart says it combines AI foresight with human expertise to refine its demand forecasting, and once an order is placed the Walmart Fulfillment Engine takes over. Walmart gives no forecasting accuracy or inventory figures.","scaled",2025,[26],[189],"en",[],[192],{"url":193,"title":194,"publisher":182,"date":195},"https://tech.walmart.com/content/walmart-global-tech/en_us/blog/post/inside-the-ai-network-orchestrating-walmarts-fastest-holiday-deliveries-yet.html","Inside the AI network orchestrating Walmart's fastest holiday deliveries yet","2025-12-08",{"level":197,"checkedAt":166},"source-verified","B","walmart-ai-supply-chain-forecasting","walmart",{"title":202,"useCases":203,"organization":204,"vendors":208,"summary":209,"stage":185,"year":210,"channels":211,"languages":212,"metrics":214,"outcomeDisclosed":215,"sources":216,"verification":222,"grade":223,"id":224,"organizationSlug":225},"Albert Heijn: daily AI demand forecasts for more than 15 million store and item combinations",[170],{"name":205,"anonymized":177,"country":206,"region":207,"industry":18},"Albert Heijn","NL","europe",[],"Albert Heijn, the leading supermarket chain in the Netherlands with more than 1,200 stores, forecasts daily sales for more than 15 million store and item combinations more than five weeks ahead, which its VP of Product Operations describes as almost one billion predictions a day, with the aim of bringing just enough stock to each store and cutting food waste. The retailer says it hopes forecasting will cut its total food waste by more than 10%, a target rather than a result. A related Dynamic Markdown initiative raises discounts on electronic shelf labels during the day for products close to their date, and the story says it now saves 250,000 kilos of wasted food per year.",2024,[26],[213],"nl",[],true,[217],{"url":218,"title":219,"publisher":220,"archivedUrl":221},"https://www.microsoft.com/en/customers/story/1739352737304784739-albertheijn-azure-open-ai-service-retailers-en-netherlands","How Albert Heijn is using Azure OpenAI to reduce food waste and help its customers eat healthy","Microsoft","https://web.archive.org/web/20241224222112/https://www.microsoft.com/en/customers/story/1739352737304784739-albertheijn-azure-open-ai-service-retailers-en-netherlands",{"level":197,"checkedAt":166},"C","albert-heijn-ai-demand-forecasting",null,{"title":227,"useCases":228,"organization":229,"vendors":232,"summary":236,"stage":185,"year":237,"channels":238,"languages":239,"metrics":240,"outcomeDisclosed":215,"sources":241,"verification":246,"grade":223,"id":247,"organizationSlug":225},"One Stop: machine learning store forecasts for fresh and weather driven products",[170],{"name":230,"anonymized":177,"country":231,"region":207,"industry":18},"One Stop","GB",[233],{"name":234,"role":235},"RELEX Solutions","platform","One Stop, the Tesco owned convenience chain with more than 900 company and franchise stores in Great Britain, moved store and distribution center forecasting and replenishment to RELEX in 2019; RELEX reports that this first rollout raised store availability by 1.9 percentage points and cut fresh spoilage value by 4%. One Stop then added machine learning forecasting to handle short shelf life lines, weather driven demand such as ice and cannibalization between promoted products. RELEX reports that within four months forecast accuracy rose by 3.17 percentage points at product and week level and 1.82 points at product, store and week level, and One Stop's Head of Supply Chain says availability of ultra fresh products with under three days of shelf life rose 8.5% with no corresponding rise in spoilage.",2022,[26],[189],[],[242],{"url":243,"title":244,"publisher":234,"date":245},"https://www.relexsolutions.com/resources/case-one-stop/","Case study: One Stop","2022-04-11",{"level":197,"checkedAt":166},"one-stop-relex-machine-learning-forecasting",{"title":249,"useCases":250,"organization":251,"vendors":253,"summary":257,"stage":185,"year":258,"channels":259,"languages":260,"metrics":261,"outcomeDisclosed":215,"sources":262,"verification":271,"grade":223,"id":272,"organizationSlug":225},"Morrisons: AI demand forecasting in place of manual store replenishment",[170],{"name":252,"anonymized":177,"country":231,"region":207,"industry":18},"Morrisons",[254,256],{"name":255,"role":235},"Blue Yonder",{"name":220,"role":235},"The UK grocer Morrisons replaced its manual model of stock replenishment with Blue Yonder's AI powered demand forecasting and replenishment solution, built on Microsoft Azure, which predicts customer demand and orders the right level of stock for its stores. Blue Yonder's customer page says it helped Morrisons increase shelf availability of more than 29,000 products in 130 categories across its 500 stores and headlines a 30% on shelf availability improvement. Technology Record, a publication produced with Microsoft's support, reported in January 2018 that the solution cut shelf gaps in Morrisons stores by 30% and stockholding in store by two to three days.",2018,[26],[189],[],[263,266],{"url":264,"title":265,"publisher":255},"https://blueyonder.com/customers/morrisons","Morrisons Simplifies Fresh Food Clearance with Blue Yonder",{"url":267,"title":268,"publisher":269,"date":270},"https://www.technologyrecord.com/article/morrisons-implements-blue-yonders-ai-stock-replenishment-technology","Morrisons implements Blue Yonder's AI stock replenishment technology","Technology Record","2018-01-15",{"level":197,"checkedAt":166},"morrisons-blue-yonder-automated-replenishment",0,[],{"low":276,"high":277},1120000,6000000,[279,296,319,341],{"slug":280,"title":281,"shortTitle":282,"definition":283,"status":9,"industries":284,"functions":286,"patterns":287,"audience":28,"autonomy":288,"adoptionStage":289,"segment":290,"evidenceCount":291,"publicEvidenceCount":291,"organizations":292,"bestGrade":223,"headline":225,"lastVerified":295,"indexable":215},"smart-meter-analytics","AI analytics for smart meter and AMI data","Smart meter analytics","AI that turns the flood of readings from smart electricity, gas and water meters into usable information: it monitors meter and network health at scale, estimates which appliances drive a household's usage from the meter signal alone, flags unusual consumption, and targets efficiency and electrification programmes at the customers who will benefit most, instead of a utility treating every meter and every customer the same way.",[285],"energy-and-utilities",[20,21],[24,23],"assist","early-adopters","metering-and-billing",2,[293,294],"Consolidated Edison (Con Edison)","Southern California Gas Company (SoCalGas)","2026-09-28",{"slug":297,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":303,"patterns":304,"audience":306,"autonomy":288,"adoptionStage":289,"segment":307,"evidenceCount":291,"publicEvidenceCount":291,"organizations":308,"bestGrade":198,"headline":311,"lastVerified":295,"indexable":215},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","An AI powered operations center that predicts patient admissions, discharges and transfers across a hospital or health system, and helps a team of coordinators sitting in one room sequence real time bed assignments, staffing levels and patient moves, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[302],"healthcare",[20,21],[23,305,24],"classification-and-routing","employee-facing","hospital operations",[309,310],"Humber River Health","Johns Hopkins Medicine",{"kpi":312,"label":313,"unit":314,"n":291,"nUpTo":273,"kind":315,"value":316,"qualifier":317,"claimant":318,"organization":310,"vendorReported":177},"processing-time-reduction","Cycle time reduction","percent","reported",38,"exact","organization",{"slug":320,"title":321,"shortTitle":322,"definition":323,"status":9,"industries":324,"functions":327,"patterns":329,"audience":28,"autonomy":332,"adoptionStage":333,"segment":334,"evidenceCount":335,"publicEvidenceCount":336,"organizations":337,"bestGrade":198,"headline":225,"lastVerified":166,"indexable":215},"portfolio-drift-monitoring-and-rebalancing","AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[325,326],"wealth-and-asset-management","banking",[20,328,21],"risk-management",[24,330,23,331],"agentic-workflow","content-generation","copilot","emerging","middle-office",4,3,[338,339,340],"Morgan Stanley","SimCorp","Vanguard",{"slug":342,"title":343,"shortTitle":344,"definition":345,"status":9,"industries":346,"functions":348,"patterns":349,"audience":306,"autonomy":332,"adoptionStage":289,"segment":350,"evidenceCount":291,"publicEvidenceCount":291,"organizations":351,"bestGrade":198,"headline":354,"lastVerified":295,"indexable":215},"ai-drug-discovery-platform","AI native platform for drug target discovery and molecule design","AI drug discovery platform","An AI native research platform that prioritizes disease targets from biological data, generates and optimizes candidate drug molecules computationally, and predicts their properties before a chemist synthesizes and tests them, so a pharmaceutical or biotech company reaches a validated preclinical candidate with far fewer molecules made and tested than a conventional medicinal chemistry program.",[347],"pharma-and-life-sciences",[20,21],[23,331],"drug discovery",[352,353],"Insilico Medicine","Recursion Pharmaceuticals",{"kpi":312,"label":313,"unit":314,"n":291,"nUpTo":273,"kind":315,"value":355,"qualifier":356,"claimant":318,"organization":352,"vendorReported":177},60,"approximately",{"indexable":215,"reasons":358},[],[360,366,371,379,386,392,399,406,414,421,428,434,441,448,454,459,466,472,478,484,490,496,502,507,512,519,526,531,537,545,551,557,563,568],{"id":144,"label":361,"issuer":362,"region":207,"url":363,"description":364,"useCases":365,"indexable":215},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":145,"label":367,"issuer":362,"region":207,"url":368,"description":369,"useCases":370,"indexable":215},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":372,"label":373,"issuer":374,"region":375,"url":376,"description":377,"useCases":378,"indexable":215},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":380,"label":381,"issuer":382,"region":179,"url":383,"description":384,"useCases":385,"indexable":215},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":387,"label":388,"issuer":362,"region":207,"url":389,"description":390,"useCases":391,"indexable":215},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":393,"label":394,"issuer":395,"region":207,"url":396,"description":397,"useCases":398,"indexable":215},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":400,"label":401,"issuer":402,"region":207,"url":403,"description":404,"useCases":405,"indexable":215},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":407,"label":408,"issuer":409,"region":410,"url":411,"description":412,"useCases":413,"indexable":215},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":415,"label":416,"issuer":417,"region":410,"url":418,"description":419,"useCases":420,"indexable":215},"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":422,"label":423,"issuer":424,"region":375,"url":425,"description":426,"useCases":427,"indexable":215},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":429,"label":430,"issuer":431,"region":179,"url":432,"description":433,"useCases":427,"indexable":215},"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":435,"label":436,"issuer":437,"region":207,"url":438,"description":439,"useCases":440,"indexable":215},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":442,"label":443,"issuer":444,"region":375,"url":445,"description":446,"useCases":447,"indexable":215},"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":449,"label":450,"issuer":362,"region":207,"url":451,"description":452,"useCases":453,"indexable":215},"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":455,"label":456,"issuer":362,"region":207,"url":457,"description":458,"useCases":453,"indexable":215},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":460,"label":461,"issuer":462,"region":179,"url":463,"description":464,"useCases":465,"indexable":215},"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":467,"label":468,"issuer":362,"region":207,"url":469,"description":470,"useCases":471,"indexable":215},"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.",12,{"id":473,"label":474,"issuer":475,"region":179,"url":476,"description":477,"useCases":471,"indexable":215},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":479,"label":480,"issuer":481,"region":375,"url":482,"description":483,"useCases":471,"indexable":215},"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":485,"label":486,"issuer":362,"region":207,"url":487,"description":488,"useCases":489,"indexable":215},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":491,"label":492,"issuer":493,"region":179,"url":494,"description":495,"useCases":489,"indexable":215},"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":497,"label":498,"issuer":409,"region":410,"url":499,"description":500,"useCases":501,"indexable":215},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":503,"label":504,"issuer":362,"region":207,"url":505,"description":506,"useCases":501,"indexable":215},"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":508,"label":509,"issuer":362,"region":207,"url":510,"description":511,"useCases":501,"indexable":215},"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":513,"label":514,"issuer":515,"region":207,"url":516,"description":517,"useCases":518,"indexable":215},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":520,"label":521,"issuer":522,"region":179,"url":523,"description":524,"useCases":525,"indexable":215},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":527,"label":528,"issuer":362,"region":207,"url":529,"description":530,"useCases":525,"indexable":215},"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":532,"label":533,"issuer":362,"region":207,"url":534,"description":535,"useCases":536,"indexable":215},"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":538,"label":539,"issuer":540,"region":541,"url":542,"description":543,"useCases":544,"indexable":215},"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":546,"label":547,"issuer":548,"region":207,"url":549,"description":550,"useCases":335,"indexable":215},"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":552,"label":553,"issuer":554,"region":207,"url":555,"description":556,"useCases":335,"indexable":215},"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":558,"label":559,"issuer":560,"region":410,"url":561,"description":562,"useCases":336,"indexable":215},"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":564,"label":565,"issuer":362,"region":207,"url":566,"description":567,"useCases":336,"indexable":215},"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":569,"label":570,"issuer":571,"region":179,"url":572,"description":573,"useCases":336,"indexable":215},"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.",1790598298532]