[{"data":1,"prerenderedAt":593},["ShallowReactive",2],{"uc-retail-dynamic-pricing-and-markdown-optimization":3,"uc-regulations":364},{"useCase":4,"evidence":171,"blitsAiDeployments":273,"benchmarks":274,"indicative":275,"related":278,"indexability":362,"includeUnpublished":177},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":20,"channels":22,"audience":25,"autonomy":26,"adoptionStage":27,"segment":28,"problem":29,"problemStats":30,"howItWorks":31,"valueDrivers":32,"kpis":36,"indicativeValue":41,"macroEstimates":80,"feasibility":81,"implementation":94,"risk":138,"blitsAi":151,"faq":153,"related":163,"datePublished":166,"dateModified":166,"lastVerified":166,"changelog":167,"slug":170},"AI dynamic pricing and markdown optimization for retail","Dynamic pricing and markdown optimization","AI dynamic pricing and markdown for retail","Delhaize America used Revionics for pricing and markdowns; Rimi Baltic moved to elasticity based pricing on Revionics; Albert Heijn automates ESL markdown discounts.","published","AI that recommends or automatically sets the regular price, promotion or markdown for every item in every store, from demand, cost, competitor prices, inventory and, for perishables, how close the item is to its best before date, so pricing analysts manage exceptions and strategy instead of spreadsheets.",[12,13,14,15],"AI price optimization","retail markdown optimization","dynamic discounting","lifecycle price optimization",[17],"retail-and-ecommerce",[19],"product-and-pricing",[21],"prediction-and-scoring",[23,24],"internal-tools","kiosk","employee-facing","supervised-agent","early-adopters","pricing-and-merchandising","A grocery or general merchandise retailer reprices thousands of items across hundreds of stores\nevery week: regular price changes, promotions, and markdowns on seasonal and perishable stock\nthat will not sell at full price. Done by hand in spreadsheets, this is slow, inconsistent\nbetween analysts, and reactive: a marked down shelf is a decision made after the item has\nalready lost value, not before.\n\nRevionics, the pricing platform used by Delhaize America and Rimi Baltic, describes the manual\nalternative directly: before adopting price optimization software, Delhaize America's pricing\nanalysts managed prices with complex, unwieldy spreadsheets, a slow manual procedure that lacked\nscalability, and pricing processes and data collection were not standardized, which made it hard\nto ensure pricing strategies were followed enterprise wide. For perishables, the cost of getting\nthe timing wrong is thrown away stock: at Albert Heijn, Supermarket News reports that prices on\nchicken and fish are reduced automatically based on their sell by date, with a higher discount\nfor items that need to be sold soonest.",[],"1. **Bring in the signals.** Point of sale history, cost and margin, current inventory,\n   competitor prices, and, for markdowns, the sell by or best before date, feed a pricing engine\n   continuously.\n2. **Estimate demand at different prices.** The engine estimates price elasticity per item and\n   location: how much volume changes as price changes, learned from historical sales, not fixed\n   rules.\n3. **Recommend or set the price.** For everyday and promotional pricing, the engine proposes a\n   price within the retailer's guardrails for an analyst to approve; for markdowns on\n   electronic shelf labels, it can act automatically within a small set of fixed discount steps,\n   choosing the step and the timing so the item is more likely to sell before it expires.\n4. **Push the price to the shelf.** The new price reaches paper tags on the next print run, or an\n   electronic shelf label in near real time, so a fast moving markdown clock is actually visible\n   to the shopper.\n5. **Feed the outcome back.** Sell through, waste and margin by item and store retrain the\n   elasticity estimates, and pricing analysts review exceptions and set the strategy: which\n   categories run automated markdowns, and what the outer guardrails are.",[33,34,35],"revenue-growth","employee-productivity","speed",[37,38,39,40],"revenue-uplift","cost-reduction","productivity-gain","forecast-accuracy",{"referenceOrg":42,"inputs":43,"formula":75,"currency":76,"period":77,"resultLabel":78,"caveat":79},"A grocery retailer with 500 stores and USD 4 million in average annual perishable and seasonal sales per store",[44,49,56,63,70],{"key":45,"label":46,"low":47,"high":47,"unit":45,"note":48},"stores","Stores",500,"The reference retailer.",{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"perishableSalesPerStore","Perishable and seasonal sales per store per year",3000000,5000000,"USD per store per year","Editorial assumption for a full line grocery store, replace with your own perishable and seasonal category mix.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"unsoldAtFullPriceShare","Share of perishable and seasonal sales sold at a markdown or written off",0.04,0.1,"fraction of perishable and seasonal sales","Editorial assumption, replace with your own waste and markdown rate by category.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"perishableMarginRate","Gross margin on perishable and seasonal sales",0.25,0.35,"fraction of sales value","Editorial assumption: a typical gross margin range across produce, meat, dairy and general merchandise categories, replace with your own category margin mix.",{"key":71,"label":72,"low":60,"high":66,"unit":73,"note":74},"marginRecovered","Share of that lost margin recovered by better timed, better sized markdowns","fraction of the margin at risk in markdown and waste","Editorial assumption, conservative because none of the three deployments on this page discloses a percentage or currency margin result: Revionics' customer case studies for Delhaize America and Rimi Baltic state results in qualitative terms only (\"improved markdowns planning and margins\" for Delhaize America, \"positive lifts in units and revenue\" for Rimi Baltic), and Albert Heijn has not disclosed a percentage or currency figure for its dynamic discounting rollout. A separate Microsoft customer story about a related Albert Heijn initiative (cited on this library's demand forecasting page) reports a weight based saving, \"This now saves 250,000 kilos of wasted food per year\", which is not a margin or currency figure and is not used in this estimate.","stores * perishableSalesPerStore * unsoldAtFullPriceShare * perishableMarginRate * marginRecovered","USD","per year","Perishable and seasonal margin recovered","Gross margin recovered only. It leaves out the cost of the pricing platform, electronic shelf labels and integration, the risk that a wrong markdown clears stock too early at a needless discount, and any change in footfall or basket size from more visible in store discounting.",[],{"complexity":82,"complexityNote":83,"dataPrerequisites":84,"integrations":89},"medium","The pricing logic itself is a mature, well understood optimization problem. The work is integrating clean, current cost, inventory and competitor data per store and item, and, for automated markdowns, electronic shelf labels that can update fast enough to make the discount clock real.",[85,86,87,88],"At least two years of point of sale history by item and store, including promotions","Current cost, margin and inventory position by item and store","For perishables, best before or sell by dates at the item and batch level","Competitor price data where the retailer competes on price",[90,91,92,93],"Point of sale and pricing master data","Inventory and warehouse management systems","Electronic shelf labels or the store's price ticket printing system","Promotion planning and calendar systems",{"steps":95,"guardrails":114,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":125},[96,99,102,105,108,111],{"title":97,"detail":98},"Start with one lever and one category group","Pick either everyday pricing or markdowns, and a limited set of categories with clean data and clear ownership, before touching the full assortment.",{"title":100,"detail":101},"Set the guardrails before the model runs","Agree the price bounds, the fixed markdown steps if any, and which categories or events (a supplier price change, a local competitor promotion) always route to a human.",{"title":103,"detail":104},"Decide what runs automatically and what needs approval","Automated markdown execution on electronic shelf labels needs the tightest guardrails, since there is no review step before the shopper sees the price; everyday price changes can start as recommendations an analyst approves.",{"title":106,"detail":107},"Pilot in a subset of stores with a holdout","Run the new pricing in a sample of stores against a matched set that keeps the old process, so sell through, waste and margin can be compared honestly.",{"title":109,"detail":110},"Roll out category by category","Rimi Baltic moved its existing pricing rules onto the platform first, one category at a time, before switching those categories to elasticity based pricing, which kept the change manageable and let the team catch mistakes in the rules logic early.",{"title":112,"detail":113},"Give analysts the exception queue, not the whole catalogue","Once trust is established, most items should not need a human look; analysts spend their time on flagged exceptions, strategy and the categories still outside automation.",[115,116,117,118],"Hard floor and ceiling on any price or discount change, with no override outside approved limits","A fixed, published set of markdown steps for automated execution, not an unbounded discount","Competitor and cost data source and freshness checked before it can move a price","Exceptions (a data outage, an unusual price swing, a new item with no history) routed to a pricing analyst, not applied automatically","Pricing analysts own the guardrails, the category rollout plan and every exception the system flags, and review a sample of automated markdown decisions each week. Category managers sign off before a new category moves from recommendation to automated execution.",[121,122,123,124],"Sell through and waste by category and store, automated versus a holdout","Margin by category, automated versus a holdout","Analyst time spent on exceptions versus routine repricing","Price change accuracy: the share of published prices matching the system's recommendation",[126,129,132,135],{"title":127,"detail":128},"A stale signal moves the price","Bad inventory or cost data pushes a wrong price or markdown live. Check data freshness before applying a change, and cap the size of any single move.",{"title":130,"detail":131},"Discount steps train shoppers to wait","If the first markdown step always appears at a predictable point, shoppers learn to wait for it. Vary timing within the guardrails and monitor sell through at each step, not only at the end.",{"title":133,"detail":134},"No holdout, no honest result","Comparing automated stores to their own past performance overstates the effect, because seasonality and the wider market shift too. Keep a matched holdout for as long as the programme runs.",{"title":136,"detail":137},"Automation outruns the guardrails","A category is automated before its guardrails, exception rules and electronic shelf label coverage are actually ready. Gate the move to automation on a checklist, not a launch date.",{"euAiAct":139,"regulations":142,"guidance":144,"controls":145,"incidents":150},{"tier":140,"basis":141},"minimal","Setting or recommending retail prices for goods is not listed in Annex III and does not evaluate the creditworthiness, employment, or access to an essential service of a natural person, so this is minimal risk under the EU AI Act. It would need reassessment if a retailer used the same engine to set an individual price per identified customer rather than per product and store, which raises separate consumer protection and non discrimination questions the deployments on this page do not describe.",[143],"eu-ai-act",[],[146,147,148,149],"Published, auditable guardrails (price bounds, markdown steps) that automated execution cannot exceed","A permanent holdout group of stores or categories for honest measurement","Change log of every price and markdown decision, automated or analyst approved, with the data it used","Regular review of automated categories for unintended patterns, such as discount timing shoppers can predict and exploit",[],{"howToBuild":152},"Blits.ai is not the pricing engine here; the retailer keeps its own price optimization or\nmarkdown model. What Blits.ai adds is the workflow around it: an **agentic workflow**, run on a\nschedule or triggered by an event such as a data refresh, calls the retailer's pricing engine\nthrough a **custom function**, checks the proposed price or markdown against the configured\nguardrails, and either applies it through another custom function to the point of sale or\nelectronic shelf label system, or raises it for a pricing analyst to approve through **human in\nthe loop approval** when it falls outside the guardrails or touches a category not yet\nautomated.\n\nA **knowledge base** holds the current pricing policy, category rollout plan and guardrail\ndocumentation so an **AI agent** can answer an analyst's questions about why a price changed or\nwhat the current rules are for a category, in the retailer's own **internal tools**. **Test\nsuites** exercise the guardrail logic against known edge cases before every change, **monitors**\nrun scheduled health checks against that agent and alert by email or webhook if it stops\nanswering correctly, and **analytics** and the **audit logging** in the platform give a full\nrecord of every automated decision for the pricing team to review.",[154,157,160],{"question":155,"answer":156},"Does AI dynamic pricing mean charging each shopper a different price?","Not in the deployments on this page. Delhaize America and Albert Heijn price by product and, for markdowns, by store and remaining shelf life. Rimi Baltic's elasticity based pricing runs at the product and category level; its sources do not describe store level pricing. None of the three prices by identified shopper. Individual level pricing raises separate fairness and disclosure questions this page does not cover.",{"question":158,"answer":159},"How much does AI markdown optimization actually save?","None of the deployments on this page publishes a percentage or currency result for markdown or margin impact. Revionics' own case studies state results at Delhaize America and Rimi Baltic in qualitative terms only (\"improved markdowns planning and margins\" for Delhaize America, \"positive lifts in units and revenue\" for Rimi Baltic), with no percentage or currency figure attached. Albert Heijn has not disclosed a percentage or currency result for its dynamic discounting rollout either. A separate Microsoft customer story about a related Albert Heijn initiative reports that it \"now saves 250,000 kilos of wasted food per year\", a weight, not a margin or revenue figure, so treat any margin or revenue number you see elsewhere as an industry estimate, not a result from these deployments.",{"question":161,"answer":162},"What is the difference between everyday pricing and markdown optimization?","Everyday pricing sets the regular shelf price from demand, cost and competition. Markdown optimization decides how and when to discount stock that will not sell at full price, typically seasonal goods or perishables close to their best before date, which is why Albert Heijn's system works from remaining shelf life rather than from a fixed markdown calendar.",[164,165],"retail-demand-forecasting-and-replenishment","hotel-revenue-management-copilot","2026-09-29",[168],{"date":166,"note":169},"First published","retail-dynamic-pricing-and-markdown-optimization",[172,220,246],{"title":173,"useCases":174,"organization":175,"vendors":180,"summary":188,"stage":189,"year":190,"channels":191,"languages":192,"metrics":195,"outcomeDisclosed":177,"sources":196,"verification":215,"grade":217,"id":218,"organizationSlug":219},"Albert Heijn: AI powered dynamic discounting on electronic shelf labels",[170],{"name":176,"anonymized":177,"country":178,"region":179,"industry":17},"Albert Heijn",false,"NL","europe",[181,183,186],{"name":176,"role":182},"in-house",{"name":184,"role":185},"Wasteless","integrator",{"name":187,"role":185},"Pricer","Albert Heijn, the Ahold Delhaize supermarket chain in the Netherlands, built an in house algorithm that automatically discounts fresh produce, chicken and fish as their best before date approaches, so stock is cleared by the end of the day instead of thrown away. The algorithm was advised by anti waste specialist Wasteless and first tested with Pricer's electronic shelf label hardware in one store in 2019. It is now rolled out to every Albert Heijn store fitted with electronic shelf labels, working with three fixed discount levels (25%, 40% and 70%) that the software chooses per product from sales history, local and seasonal patterns, weather and current stock.","scaled",2019,[24],[193,194],"nl","en",[],[197,202,205,209],{"url":198,"title":199,"publisher":200,"date":201},"https://www.supermarketnews.com/fresh-produce/ahold-delhaize-testing-dynamic-discounts-to-reduce-waste","Ahold Delhaize Testing Dynamic Discounts to Reduce Waste","Supermarket News","2019-05-22",{"url":203,"title":204,"publisher":187},"https://www.pricer.com/news/albert-heijn-combats-food-waste-with-pricer-and-wasteless-through-ai-powered-dynamic-pricing","Albert Heijn combats food waste with Pricer and Wasteless through AI powered Dynamic Pricing",{"url":206,"title":207,"publisher":208},"https://retail-optimiser.de/en/albert-heijn-discounts-fresh-produce-dynamically-with-ai/","Albert Heijn discounts fresh produce dynamically with AI","Retail Optimiser",{"url":210,"title":211,"publisher":212,"date":213,"archivedUrl":214},"https://www.aholddelhaize.com/en/media/latest/media-releases/albert-heijn-starts-test-to-fight-food-waste-helped-by-artificial-intelligence/","Albert Heijn starts test to fight food waste helped by artificial intelligence","Ahold Delhaize","2019-05-20","https://web.archive.org/web/20200927190055/https://www.aholddelhaize.com/en/media/latest/media-releases/albert-heijn-starts-test-to-fight-food-waste-helped-by-artificial-intelligence/",{"level":216,"checkedAt":166},"source-verified","B","albert-heijn-dynamic-markdown-discounting",null,{"title":221,"useCases":222,"organization":223,"vendors":226,"summary":230,"stage":189,"year":190,"channels":231,"languages":232,"metrics":233,"outcomeDisclosed":177,"sources":234,"verification":243,"grade":244,"id":245,"organizationSlug":219},"Rimi Baltic: price elasticity based pricing with Revionics",[170],{"name":224,"anonymized":177,"country":225,"region":179,"industry":17},"Rimi Baltic","LV",[227],{"name":228,"role":229},"Revionics","platform","Rimi Baltic, the ICA Gruppen owned grocery and convenience retailer operating in Latvia, Lithuania and Estonia, replaced rule based pricing in its ERP system with the Revionics price optimization platform. It rolled out category by category in two steps, first moving its existing pricing rules onto the platform, then switching those categories to price elasticity based pricing, and completed the change across its full product assortment in all three countries in under a year.",[],[194],[],[235,238],{"url":236,"title":237,"publisher":228},"https://revionics.com/assets/revionics-case-study-rimi-baltic-a4-022221_538Dejl.pdf","Rimi Baltic modernizes pricing to serve customers better",{"url":239,"title":240,"publisher":241,"date":242},"https://www.prnewswire.com/news-releases/rimi-baltic-selects-revionics-price-optimization-to-enhance-customer-engagement-and-deliver-business-results-300794760.html","Rimi Baltic Selects Revionics Price Optimization to Enhance Customer Engagement and Deliver Business Results","PR Newswire","2019-02-13",{"level":216,"checkedAt":166},"C","rimi-baltic-price-elasticity-pricing",{"title":247,"useCases":248,"organization":249,"vendors":253,"summary":255,"stage":256,"year":257,"channels":258,"languages":259,"metrics":260,"outcomeDisclosed":177,"sources":261,"verification":271,"grade":244,"id":272,"organizationSlug":219},"Delhaize America: lifecycle price and markdown optimization with Revionics",[170],{"name":250,"anonymized":177,"country":251,"region":252,"industry":17},"Delhaize America","US","north-america",[254],{"name":228,"role":229},"Delhaize America, at the time the US subsidiary of Delhaize Group operating supermarket chains including Food Lion and Hannaford (this record predates the 2016 merger that formed Ahold Delhaize), replaced spreadsheet based pricing with the Revionics price optimization platform across all of its price zones and stores. The retailer moved from rule based pricing to scenario based, shopper centric pricing, then extended the same platform to markdown decisions, so the company could make fact based markdown and clearance decisions, including markdown cadence and depth, aligned with local shopper demand and store level inventories instead of blanket rules.","production",2014,[],[194],[],[262,265],{"url":263,"title":264,"publisher":228},"https://revionics.com/assets/delhaize_casestudy_revised_10-27_r9lqngl.pdf","Delhaize America Gains Invaluable Partner for Price Optimization Journey",{"url":266,"title":267,"publisher":268,"date":269,"archivedUrl":270},"https://www.retailtouchpoints.com/features/news-briefs/delhaize-america-selects-revionics-price-optimization","Delhaize America Selects Revionics Price Optimization","Retail TouchPoints","2014-04-16","https://web.archive.org/web/2026/https://www.retailtouchpoints.com/features/news-briefs/delhaize-america-selects-revionics-price-optimization",{"level":216,"checkedAt":166},"delhaize-america-lifecycle-price-optimization",0,[],{"low":276,"high":277},1500000,21875000,[279,298,320,336],{"slug":164,"title":280,"shortTitle":281,"definition":282,"status":9,"industries":283,"functions":284,"patterns":287,"audience":289,"autonomy":26,"adoptionStage":290,"evidenceCount":291,"publicEvidenceCount":291,"organizations":292,"bestGrade":217,"headline":219,"lastVerified":296,"indexable":297},"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],[285,286],"operations","analytics-and-reporting",[21,288],"anomaly-detection","back-office","mainstream",4,[176,293,294,295],"Morrisons","One Stop","Walmart","2026-09-27",true,{"slug":165,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":304,"patterns":305,"audience":25,"autonomy":26,"adoptionStage":27,"segment":307,"evidenceCount":308,"publicEvidenceCount":308,"organizations":309,"bestGrade":244,"headline":312,"lastVerified":319,"indexable":297},"AI copilot for hotel revenue management","Hotel revenue management copilot","An employee facing AI system that forecasts demand for a hotel or portfolio by date, room type and segment, recommends or automatically adjusts room prices and availability controls within limits a revenue manager sets, and scores group and event enquiries for true profitability, so a small revenue team can run pricing that used to need daily manual adjustment in the property management system.",[303],"travel-and-hospitality",[19,286],[21,306],"agentic-workflow","revenue-management",2,[310,311],"Hôtel Swexan","RIMC Hotels & Resorts Group",{"kpi":37,"label":313,"unit":314,"n":308,"nUpTo":273,"kind":315,"value":316,"qualifier":317,"claimant":318,"organization":310,"vendorReported":297},"Revenue uplift","percent","reported",33,"exact","vendor","2026-09-28",{"slug":321,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":327,"patterns":329,"audience":25,"autonomy":331,"adoptionStage":27,"segment":332,"evidenceCount":308,"publicEvidenceCount":308,"organizations":333,"bestGrade":217,"headline":219,"lastVerified":166,"indexable":297},"trade-promotion-optimization-for-cpg","AI trade promotion optimization for consumer goods manufacturers","Trade promotion optimization","Machine learning and optimisation that plans and prices trade promotions, discounts, displays and rebates a consumer goods manufacturer runs with retailers, forecasting the volume each promotion mechanic would generate and searching the combinations of timing, depth and mechanic that best meet a revenue, profit or volume target, for a key account manager to review and agree with the retailer.",[326,17],"manufacturing",[19,328],"sales",[21,330],"recommendation-and-personalization","copilot","revenue-growth-management",[334,335],"Henkel","PepsiCo",{"slug":337,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":343,"patterns":345,"audience":25,"autonomy":331,"adoptionStage":27,"segment":348,"evidenceCount":349,"publicEvidenceCount":349,"organizations":350,"bestGrade":217,"headline":356,"lastVerified":361,"indexable":297},"insurance-pricing-and-actuarial-copilot","AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[342],"insurance",[19,344,286],"risk-management",[21,346,306,347],"code-generation","summarization","pricing",5,[351,352,353,354,355],"Accelerant Holdings","Europ Assistance","Generali France","Kinsale Capital Group","MAIF",{"kpi":39,"label":357,"unit":358,"n":359,"nUpTo":273,"kind":315,"value":349,"qualifier":317,"claimant":360,"organization":353,"vendorReported":177},"Productivity gain","multiplier",1,"organization","2026-09-26",{"indexable":297,"reasons":363},[],[365,371,377,385,392,399,405,412,420,427,434,441,447,453,460,467,473,480,486,492,498,505,510,517,522,527,532,538,545,551,558,564,570,577,582,587],{"id":143,"label":366,"issuer":367,"region":179,"url":368,"description":369,"useCases":370,"indexable":297},"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.",230,{"id":372,"label":373,"issuer":367,"region":179,"url":374,"description":375,"useCases":376,"indexable":297},"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.",207,{"id":378,"label":379,"issuer":380,"region":381,"url":382,"description":383,"useCases":384,"indexable":297},"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":386,"label":387,"issuer":388,"region":252,"url":389,"description":390,"useCases":391,"indexable":297},"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":393,"label":394,"issuer":395,"region":179,"url":396,"description":397,"useCases":398,"indexable":297},"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":400,"label":401,"issuer":367,"region":179,"url":402,"description":403,"useCases":404,"indexable":297},"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":406,"label":407,"issuer":408,"region":179,"url":409,"description":410,"useCases":411,"indexable":297},"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":413,"label":414,"issuer":415,"region":416,"url":417,"description":418,"useCases":419,"indexable":297},"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":421,"label":422,"issuer":423,"region":416,"url":424,"description":425,"useCases":426,"indexable":297},"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":428,"label":429,"issuer":430,"region":252,"url":431,"description":432,"useCases":433,"indexable":297},"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":435,"label":436,"issuer":437,"region":381,"url":438,"description":439,"useCases":440,"indexable":297},"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":442,"label":443,"issuer":367,"region":179,"url":444,"description":445,"useCases":446,"indexable":297},"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":448,"label":449,"issuer":450,"region":179,"url":451,"description":452,"useCases":446,"indexable":297},"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":454,"label":455,"issuer":456,"region":252,"url":457,"description":458,"useCases":459,"indexable":297},"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":461,"label":462,"issuer":463,"region":381,"url":464,"description":465,"useCases":466,"indexable":297},"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":468,"label":469,"issuer":367,"region":179,"url":470,"description":471,"useCases":472,"indexable":297},"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":474,"label":475,"issuer":476,"region":252,"url":477,"description":478,"useCases":479,"indexable":297},"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":481,"label":482,"issuer":483,"region":252,"url":484,"description":485,"useCases":479,"indexable":297},"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":487,"label":488,"issuer":367,"region":179,"url":489,"description":490,"useCases":491,"indexable":297},"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":493,"label":494,"issuer":495,"region":381,"url":496,"description":497,"useCases":491,"indexable":297},"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":499,"label":500,"issuer":501,"region":252,"url":502,"description":503,"useCases":504,"indexable":297},"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":506,"label":507,"issuer":367,"region":179,"url":508,"description":509,"useCases":504,"indexable":297},"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":511,"label":512,"issuer":513,"region":179,"url":514,"description":515,"useCases":516,"indexable":297},"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":518,"label":519,"issuer":415,"region":416,"url":520,"description":521,"useCases":516,"indexable":297},"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":523,"label":524,"issuer":367,"region":179,"url":525,"description":526,"useCases":516,"indexable":297},"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":528,"label":529,"issuer":367,"region":179,"url":530,"description":531,"useCases":516,"indexable":297},"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":533,"label":534,"issuer":367,"region":179,"url":535,"description":536,"useCases":537,"indexable":297},"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":539,"label":540,"issuer":541,"region":252,"url":542,"description":543,"useCases":544,"indexable":297},"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":546,"label":547,"issuer":367,"region":179,"url":548,"description":549,"useCases":550,"indexable":297},"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":552,"label":553,"issuer":554,"region":555,"url":556,"description":557,"useCases":349,"indexable":297},"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":559,"label":560,"issuer":561,"region":179,"url":562,"description":563,"useCases":291,"indexable":297},"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":565,"label":566,"issuer":567,"region":179,"url":568,"description":569,"useCases":291,"indexable":297},"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":571,"label":572,"issuer":573,"region":416,"url":574,"description":575,"useCases":576,"indexable":297},"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.",3,{"id":578,"label":579,"issuer":367,"region":179,"url":580,"description":581,"useCases":576,"indexable":297},"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":583,"label":584,"issuer":367,"region":179,"url":585,"description":586,"useCases":576,"indexable":297},"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":588,"label":589,"issuer":590,"region":252,"url":591,"description":592,"useCases":576,"indexable":297},"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.",1790683489723]