[{"data":1,"prerenderedAt":583},["ShallowReactive",2],{"uc-store-and-shelf-monitoring":3,"uc-regulations":358},{"useCase":4,"evidence":164,"blitsAiDeployments":251,"benchmarks":252,"indicative":267,"related":270,"indexability":356,"includeUnpublished":170},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":23,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":43,"macroEstimates":78,"feasibility":79,"implementation":91,"risk":132,"blitsAi":145,"faq":147,"related":157,"datePublished":159,"dateModified":159,"lastVerified":159,"changelog":160,"slug":163},"AI computer vision for store shelf and stock monitoring","Store and shelf monitoring","AI shelf monitoring for retail and CPG","AI shelf cameras and robots flag out of stocks faster than manual audits. Schnucks reports 14 times more detection, and Trax reports a 2.1% sales uplift for Henkel.","published","Computer vision, on cameras, shelf edge sensors or autonomous robots, that scans store shelves for gaps, misplaced items and wrong prices, and turns what it sees into a prioritised task list for store staff or field sales representatives, so out of stocks and shelf compliance problems are caught within hours instead of at the next manual walk.",[12,13,14,15,16],"shelf monitoring AI","on shelf availability AI","shelf scanning robot","retail computer vision for inventory","shelf image recognition",[18,19],"retail-and-ecommerce","manufacturing",[21,22],"operations","sales",[24,25],"computer-vision","anomaly-detection",[27],"internal-tools","back-office","assist","early-adopters","store-operations","A shelf that looks fine from the end of the aisle can still be missing the one size or flavour a\nshopper wants. Store associates and field sales representatives have traditionally found this\nthe slow way: an occasional manual walk of the aisles with a clipboard or a phone, covering only\na fraction of the floor. By the time a gap is logged, the sale is already lost, and a walk that\nonly reaches part of the shelf misses far more out of stocks than it finds.\n\nThe problem sits on both sides of the shelf. Retailers lose sales and see slower, less accurate\nreplenishment signals when the shelf state is not known. Consumer goods manufacturers lose\ndistribution and shelf share they have paid for in trade terms, and field reps can end up\nspending a large share of a store visit on manual counts instead of selling: Trax's case study\non Henkel reports that its reps spent only ten minutes of every hour in a store on active selling\nbefore Henkel started using its shelf image recognition.",[],"1. **Capture the shelf.** An autonomous robot that traverses the aisles on a schedule photographs\n   every shelf section several times a day; a fixed camera, a shelf edge sensor or a field rep's\n   own phone camera can capture the same view without a robot.\n2. **Read the image.** Computer vision matches what is on the shelf, and what is missing, against\n   the planogram and the product catalogue: out of stocks, low stock, misplaced items, wrong\n   facings and price tag mismatches.\n3. **Prioritise, do not just report.** A well designed system ranks findings by expected sales\n   impact, rather than listing them in shelf order, so the highest value gap gets fixed first.\n4. **Push a task, not a dashboard.** A prioritised, store or route specific task list reaches the\n   associate's handheld or the field rep's phone, with the aisle, the SKU and the action needed.\n5. **Close the loop.** Completed tasks, and the shelf state after restocking, feed back into\n   inventory and replenishment systems and into the KPI dashboards category and account managers\n   use to negotiate with retail partners.",[36,37,38],"cost-to-serve","revenue-growth","employee-productivity",[40,41,42],"detection-rate-improvement","revenue-uplift","productivity-gain",{"referenceOrg":44,"inputs":45,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"A regional grocery chain with 100 stores and USD 25 million in annual sales per store",[46,52,59,66],{"key":47,"label":48,"low":49,"high":50,"unit":47,"note":51},"stores","Stores in the chain",50,150,"Editorial assumption for an illustrative regional grocery chain, a range around the size of Schnucks' 112 store chain referenced on this page. Replace with your own store count.",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"salesPerStore","Annual sales per store",20000000,35000000,"USD per store per year","Editorial assumption for a regional grocery chain. Replace with your own store level revenue.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"oosSalesAtRisk","Share of sales at risk from out of stocks before the change",0.02,0.04,"fraction of sales","Editorial assumption, replace with your own on shelf availability audit.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"oosImprovement","Share of that risk removed by shelf monitoring",0.1,0.2,"fraction of the at risk sales","Conservative against Schnucks' reported at least 20% reduction in out of stocks in stores using the robot, reported at the September 2020 expansion announcement, kept lower here because that figure covers one retailer's deployment, not a cross industry average.","stores * salesPerStore * oosSalesAtRisk * oosImprovement","USD","per year","Sales recovered from fewer out of stocks","Gross sales recovered only. It leaves out the cost of the cameras, robots or platform, the labour used to act on the task list, and any change in markdowns or waste from tighter replenishment signals.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":86},"medium","The computer vision itself is usually bought, not built: the work is fitting it to the retailer's own planogram and product catalogue, routing the resulting tasks to the right device, and closing the loop back into replenishment and field sales systems.",[83,84,85],"A current planogram or shelf plan per store or store cluster","A product catalogue with images, matched to barcodes and SKUs","A task or work order system that store associates or field reps already use",[87,88,89,90],"The shelf camera, sensor or robot platform's own detection API","Store inventory and replenishment system","Field force or task management tool used by associates and sales reps","Point of sale or category management reporting, for the resulting KPIs",{"steps":92,"guardrails":108,"humanInTheLoop":113,"kpisToInstrument":114,"failureModes":119},[93,96,99,102,105],{"title":94,"detail":95},"Start with the highest value gaps, not full coverage","Begin with the categories and store clusters where out of stocks cost the most, rather than trying to cover every shelf on day one.",{"title":97,"detail":98},"Match detections to the current planogram","Load the planogram and product catalogue the vision system checks against, and keep both current, or every detection is measured against a shelf plan that no longer matches reality.",{"title":100,"detail":101},"Turn detections into one task, not a report","Route each confirmed gap as a single, prioritised task to the associate or rep responsible, with the aisle and SKU, instead of a dashboard someone has to remember to check.",{"title":103,"detail":104},"Feed the loop back into replenishment","Send confirmed out of stocks and shelf state changes into the store's inventory and ordering system, so the detection improves replenishment accuracy, not just visibility.",{"title":106,"detail":107},"Measure detection against a manual baseline first","Before trusting the system, compare its findings against a manual audit on a sample of shelves, so false positives and blind spots are known before staff are asked to act on them.",[109,110,111,112],"A confidence threshold below which a detection is queued for human review, not auto pushed as a task","Store staff and field reps can mark a task as wrong, and that feedback retrains the matching","No biometric identification of shoppers who appear incidentally in shelf images","Clear ownership of the planogram data feeding the comparison, with a review date","People still do the physical work: restocking, fixing a misplaced item, checking a low confidence detection before acting on it. Category and account managers review the resulting KPI trends, not individual detections, and decide what changes in the shelf plan or the order.",[115,116,117,118],"Out of stock detections versus a manual audit baseline, on the same shelves","Time from detection to a completed task","Task completion rate and false positive rate reported back by staff","Sales or share of shelf in the categories covered, before and after",[120,123,126,129],{"title":121,"detail":122},"A stale planogram makes every comparison wrong","The system flags gaps against a shelf plan nobody updated. Give the planogram an owner and a review date, the same as any other approved content.",{"title":124,"detail":125},"Detection without action","Gaps are found but no one restocks them, so the dashboard looks busy and the shelf does not change. Route a single task to a named owner, not a report to a queue.",{"title":127,"detail":128},"Cameras that see people, not just shelves","A shelf camera's field of view can capture shoppers or staff. Angle and mask the feed so only the shelf is analysed, and document that in the privacy notice.",{"title":130,"detail":131},"Treating a pilot's numbers as the chainwide number","Early pilot stores are often the easiest cases. Track results separately by rollout wave before quoting a single improvement figure company wide.",{"euAiAct":133,"regulations":136,"guidance":139,"controls":140,"incidents":144},{"tier":134,"basis":135},"minimal","The system analyses shelves and products, not natural persons, and makes no decision about a person's access to a service, a job, credit or benefits, so it falls outside Annex III. If camera placement or software is changed to identify or track individual shoppers or staff, the analysis changes and biometric identification rules would apply.",[137,138],"eu-ai-act","gdpr",[],[141,142,143],"Camera and robot placement and processing scoped to shelves and products, not people","A named owner for the planogram and product catalogue the system checks against","A manual audit sample kept running after go live, to catch drift in detection accuracy",[],{"howToBuild":146},"Blits.ai does not do the shelf image recognition itself; that stays with a specialised\ncomputer vision or robotics vendor. What Blits.ai builds well is the layer that turns a\ndetection feed into action. A **custom function** calls the vision vendor's API for new,\nconfirmed out of stocks and shelf compliance issues, and an **agentic workflow** turns each one\ninto a task addressed to the right store associate or field sales representative, with\n**human in the loop approval** before a low confidence detection, or a task above a set\nthreshold, is pushed. A **knowledge base** holds the store's own planogram notes and\nmerchandising standards so the task includes the right instruction, not just a SKU number.\n\nStore associates and field reps reach their task list and can ask follow up questions through\n**channels** such as WhatsApp, or a custom application built on the REST or WebSocket API\nchannel, and can escalate a task they cannot complete through **human handover** to a\nsupervisor. **Analytics** dashboards track detections, task completion and time to close by\nstore or route, with custom dashboard widgets and calculated statistics for the metrics a\ncategory or account manager needs, and the same tenant can keep its detection and task data in\nits own region under Blits.ai's **EU and UAE data residency** options.",[148,151,154],{"question":149,"answer":150},"Does AI shelf monitoring replace store associates?","No. It replaces the manual walk that finds a gap, not the physical work of restocking it. Trax's case study on Henkel reports that reps' active selling time rose after Henkel adopted shelf image recognition, and Schnucks' VP of IT infrastructure said the wider Tally rollout would free store teams from tedious inventory tasks to focus more on service, a stated aim rather than a separately measured result.",{"question":152,"answer":153},"How much of an improvement in out of stocks is realistic?","It depends on the baseline and how much of the store is covered. Schnucks reported at least a 20% reduction in out of stocks and 14 times more out of stock detection than manual auditing, in stores using the robot, at its September 2020 expansion announcement, and Trax reports a 4.3% reduction in out of stocks for Henkel from shelf image recognition across its distribution. Treat any single figure as tied to that organization's starting point, category mix and who is making the claim.",{"question":155,"answer":156},"Does this need robots, or do cameras work too?","The evidence on this page covers two modes without a fixed camera. Autonomous robots such as Simbe Robotics' Tally scan aisles on a schedule, as at Schnucks, and vendor image recognition such as Trax, fed by images the field force captures, lets a manufacturer's sales reps check shelf compliance without a robot: GoGo SqueeZ's case study describes reps uploading a shelf photo, and Henkel's describes reps capturing SKU level information without naming the capture method. Fixed shelf edge cameras are also sold for this job, but no evidence record on this page covers one.",[158],"retail-demand-forecasting-and-replenishment","2026-09-29",[161],{"date":159,"note":162},"First published","store-and-shelf-monitoring",[165,201,225],{"title":166,"useCases":167,"organization":168,"vendors":173,"summary":177,"stage":178,"year":179,"channels":180,"languages":181,"metrics":182,"outcomeDisclosed":191,"sources":192,"verification":196,"grade":198,"id":199,"organizationSlug":200},"GoGo SqueeZ: distribution and sales growth with Trax Image Recognition",[163],{"name":169,"anonymized":170,"country":171,"region":172,"industry":19},"GoGo SqueeZ",false,"US","north-america",[174],{"name":175,"role":176},"Trax Retail","platform","GoGo SqueeZ, described by Trax as one of the fastest growing international healthy snack food companies, with 68% of the market share in the pouch category, used Trax Image Recognition to get SKU level visibility into shelf placement, price and display across its retail accounts. Its own analytics team turned the shelf data into scorecards and store level recommendations for its field force, and Trax's case study reports outlets that adopted the recommendations grew three times faster than outlets that did not.","production",2021,[27],[],[183],{"kpi":41,"value":184,"unit":185,"qualifier":186,"period":187,"claimant":188,"quote":189,"sourceUrl":190},7,"percent","exact","versus the period before using Trax","vendor","GoGo SqueeZ used Trax IR and saw 11% increase in total points of distribution, 26% sales growth YoY, and 7% sales growth versus the period prior to using Trax.","https://traxretail.com/case-studies/gogo-squeeze/",true,[193],{"url":190,"title":194,"publisher":175,"date":195},"GoGo SqueeZ increased sales by 7% with Trax Image Recognition","2021-10-19",{"level":197,"checkedAt":159},"source-verified","C","gogo-squeez-trax-shelf-intelligence",null,{"title":202,"useCases":203,"organization":204,"vendors":208,"summary":210,"stage":178,"year":211,"channels":212,"languages":213,"metrics":214,"outcomeDisclosed":191,"sources":219,"verification":223,"grade":198,"id":224,"organizationSlug":200},"Henkel: sales uplift and out of stock reduction with Trax shelf image recognition",[163],{"name":205,"anonymized":170,"country":206,"region":207,"industry":19},"Henkel","DE","europe",[209],{"name":175,"role":176},"Henkel, the German consumer goods manufacturer, used Trax Image Recognition to monitor more than 900 SKUs across 2,500 stores in Germany. Trax's case study reports that before the rollout, Henkel's sales reps used manual methods to measure in store distribution and shelf share and spent only about ten minutes of every store hour on active selling, and that with Trax, Henkel monitored close to 500,000 products in three and a half months and identified more than 20,000 that were regularly missing from shelves.",2020,[27],[],[215],{"kpi":41,"value":216,"unit":185,"qualifier":186,"claimant":188,"quote":217,"sourceUrl":218},2.1,"Trax enabled Henkel to reduce OOS by 4.3% and see sales uplift of 2.1%.","https://traxretail.com/case-studies/henkel/",[220],{"url":218,"title":221,"publisher":175,"date":222},"Henkel saw sales uplift and OOS reduction with Trax Image Recognition","2021-01-14",{"level":197,"checkedAt":159},"henkel-trax-shelf-intelligence",{"title":226,"useCases":227,"organization":228,"vendors":230,"summary":233,"stage":178,"year":211,"channels":234,"languages":235,"metrics":236,"outcomeDisclosed":191,"sources":244,"verification":249,"grade":198,"id":250,"organizationSlug":200},"Schnucks: Tally shelf scanning robot expansion to 62 of 112 stores announced",[163],{"name":229,"anonymized":170,"country":171,"region":172,"industry":18},"Schnuck Markets",[231],{"name":232,"role":176},"Simbe Robotics","Schnuck Markets, a St Louis based regional grocer, piloted Simbe Robotics' Tally shelf scanning robot at several stores from July 2017 and deployed it to 15 stores in fall 2018. In September 2020, Schnucks and Simbe announced an expansion to 46 more supermarkets, bringing the rollout to 62 of the company's 112 stores. The robots travel the store floor two to three times a day and scan roughly 35,000 products each pass; with the expanded deployment, the companies said Tally would scan an average of more than 4.2 million products daily, feeding out of stock and shelf condition data into Schnucks' inventory and replenishment systems.",[27],[],[237],{"kpi":40,"value":238,"unit":239,"qualifier":186,"period":240,"claimant":241,"quote":242,"sourceUrl":243},14,"multiplier","in stores using the robot, reported at the September 2020 expansion announcement","organization","Dave Steck, vice president of IT infrastructure and development at Schnucks, reported that Tally has provided 14 times more out-of-stock detection than manual auditing and at least a 20% reduction in out-of-stocks in stores using the robot.","https://www.supermarketnews.com/grocery-technology/schnuck-markets-rolls-out-shelf-scanning-robots-to-over-half-of-store-base",[245],{"url":243,"title":246,"publisher":247,"date":248},"Schnuck Markets rolls out shelf-scanning robots to over half of store base","Supermarket News","2020-09-30",{"level":197,"checkedAt":159},"schnucks-shelf-scanning-robots",0,[253,261],{"kpi":41,"label":254,"unit":185,"aggregate":191,"higherIsBetter":191,"n":255,"nUpTo":251,"median":256,"min":216,"max":184,"byClaimant":257,"vendorOnly":191,"points":258},"Revenue uplift",2,4.55,{"organization":251,"vendor":255,"regulator":251,"independent":251},[259,260],{"evidenceId":199,"organization":169,"value":184,"qualifier":186,"claimant":188,"grade":198,"pooled":191},{"evidenceId":224,"organization":205,"value":216,"qualifier":186,"claimant":188,"grade":198,"pooled":191},{"kpi":40,"label":262,"unit":239,"aggregate":191,"higherIsBetter":191,"n":263,"nUpTo":251,"median":238,"min":238,"max":238,"byClaimant":264,"vendorOnly":170,"points":265},"Detection improvement",1,{"organization":263,"vendor":251,"regulator":251,"independent":251},[266],{"evidenceId":250,"organization":229,"value":238,"qualifier":186,"claimant":241,"grade":198,"pooled":191},{"low":268,"high":269},2000000,42000000,[271,290,315,335],{"slug":158,"title":272,"shortTitle":273,"definition":274,"status":9,"industries":275,"functions":276,"patterns":278,"audience":28,"autonomy":280,"adoptionStage":281,"evidenceCount":282,"publicEvidenceCount":282,"organizations":283,"bestGrade":288,"headline":200,"lastVerified":289,"indexable":191},"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.",[18],[21,277],"analytics-and-reporting",[279,25],"prediction-and-scoring","supervised-agent","mainstream",4,[284,285,286,287],"Albert Heijn","Morrisons","One Stop","Walmart","B","2026-09-27",{"slug":291,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":298,"patterns":300,"audience":303,"autonomy":280,"adoptionStage":304,"segment":305,"evidenceCount":306,"publicEvidenceCount":184,"organizations":307,"bestGrade":288,"headline":200,"lastVerified":289,"indexable":191},"agentic-payment-initiation","AI agent for payment initiation within a customer mandate","Agentic payment initiation","An AI agent that initiates and completes payments or purchases on a customer's behalf, within a mandate the customer set in advance (spending caps, allowed merchants or categories, a tokenized credential and rules for when to ask for confirmation), and then confirms and reconciles every transaction it made.",[296,297,18],"payments","banking",[299,22,21],"customer-service",[301,302],"agentic-workflow","conversational-agent","customer-facing","emerging","front-office",8,[308,309,310,311,312,313,314],"DBS Bank","ING","Majid Al Futtaim","PayPal","Banco Santander","Ulta Beauty","Visa",{"slug":316,"title":317,"shortTitle":318,"definition":319,"status":9,"industries":320,"functions":322,"patterns":323,"audience":325,"autonomy":280,"adoptionStage":30,"segment":178,"evidenceCount":326,"publicEvidenceCount":326,"organizations":327,"bestGrade":288,"headline":331,"lastVerified":289,"indexable":191},"production-line-quality-inspection","AI quality inspection on the production line","Production quality inspection","AI that inspects every unit on a production line, from camera images, sound or machine process data, to find defects, missing parts and wrong variants in real time, and routes the few anomalies it flags to a quality inspector instead of relying on manual sampling at the end of the line.",[19,321],"automotive",[21],[24,25,324,301],"synthetic-data-generation","employee-facing",3,[328,329,330],"Audi","BMW Group","Pegatron",{"kpi":332,"label":333,"unit":185,"n":263,"nUpTo":251,"kind":334,"value":184,"qualifier":186,"claimant":188,"organization":330,"vendorReported":191},"cost-reduction","Cost reduction","reported",{"slug":336,"title":337,"shortTitle":338,"definition":339,"status":9,"industries":340,"functions":343,"patterns":345,"audience":325,"autonomy":347,"adoptionStage":30,"evidenceCount":326,"publicEvidenceCount":326,"organizations":348,"bestGrade":198,"headline":352,"lastVerified":289,"indexable":191},"sales-quote-and-estimate-generation","AI quote and estimate generation from customer requirements","Quote and estimate generation","AI that turns what a customer sends, such as a product list, a drawing, a roof photo or a request for quotation, into a draft quote: it reads the input, matches items to the catalog, calculates quantities and applies the organization's price rules, and hands the draft to a seller or estimator who checks and sends it.",[341,19,342,18],"cross-industry","energy-and-utilities",[22,344],"product-and-pricing",[346,301,24],"document-processing","copilot",[349,350,351],"DeAcero","Enpal","The ODP Corporation",{"kpi":353,"label":354,"unit":185,"n":263,"nUpTo":251,"kind":334,"value":355,"qualifier":186,"claimant":188,"organization":350,"vendorReported":191},"handling-time-reduction","Handling time reduction",87.5,{"indexable":191,"reasons":357},[],[359,365,370,378,385,392,398,404,412,419,426,433,439,445,452,459,464,471,477,483,489,496,501,508,513,518,523,529,535,541,549,555,561,567,572,577],{"id":137,"label":360,"issuer":361,"region":207,"url":362,"description":363,"useCases":364,"indexable":191},"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":138,"label":366,"issuer":361,"region":207,"url":367,"description":368,"useCases":369,"indexable":191},"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":371,"label":372,"issuer":373,"region":374,"url":375,"description":376,"useCases":377,"indexable":191},"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":379,"label":380,"issuer":381,"region":172,"url":382,"description":383,"useCases":384,"indexable":191},"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":386,"label":387,"issuer":388,"region":207,"url":389,"description":390,"useCases":391,"indexable":191},"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":393,"label":394,"issuer":361,"region":207,"url":395,"description":396,"useCases":397,"indexable":191},"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":399,"label":400,"issuer":401,"region":207,"url":402,"description":403,"useCases":49,"indexable":191},"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":191},"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":191},"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":172,"url":423,"description":424,"useCases":425,"indexable":191},"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":374,"url":430,"description":431,"useCases":432,"indexable":191},"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":361,"region":207,"url":436,"description":437,"useCases":438,"indexable":191},"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":207,"url":443,"description":444,"useCases":438,"indexable":191},"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":172,"url":449,"description":450,"useCases":451,"indexable":191},"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":374,"url":456,"description":457,"useCases":458,"indexable":191},"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":361,"region":207,"url":462,"description":463,"useCases":238,"indexable":191},"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.",{"id":465,"label":466,"issuer":467,"region":172,"url":468,"description":469,"useCases":470,"indexable":191},"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":472,"label":473,"issuer":474,"region":172,"url":475,"description":476,"useCases":470,"indexable":191},"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":478,"label":479,"issuer":361,"region":207,"url":480,"description":481,"useCases":482,"indexable":191},"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":484,"label":485,"issuer":486,"region":374,"url":487,"description":488,"useCases":482,"indexable":191},"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":172,"url":493,"description":494,"useCases":495,"indexable":191},"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":361,"region":207,"url":499,"description":500,"useCases":495,"indexable":191},"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":207,"url":505,"description":506,"useCases":507,"indexable":191},"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":191},"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":361,"region":207,"url":516,"description":517,"useCases":507,"indexable":191},"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":361,"region":207,"url":521,"description":522,"useCases":507,"indexable":191},"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":361,"region":207,"url":526,"description":527,"useCases":528,"indexable":191},"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":172,"url":533,"description":534,"useCases":184,"indexable":191},"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":361,"region":207,"url":538,"description":539,"useCases":540,"indexable":191},"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":548,"indexable":191},"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":550,"label":551,"issuer":552,"region":207,"url":553,"description":554,"useCases":282,"indexable":191},"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":556,"label":557,"issuer":558,"region":207,"url":559,"description":560,"useCases":282,"indexable":191},"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":562,"label":563,"issuer":564,"region":408,"url":565,"description":566,"useCases":326,"indexable":191},"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":568,"label":569,"issuer":361,"region":207,"url":570,"description":571,"useCases":326,"indexable":191},"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":573,"label":574,"issuer":361,"region":207,"url":575,"description":576,"useCases":326,"indexable":191},"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":578,"label":579,"issuer":580,"region":172,"url":581,"description":582,"useCases":326,"indexable":191},"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.",1790683489200]