[{"data":1,"prerenderedAt":534},["ShallowReactive",2],{"uc-trade-promotion-optimization-for-cpg":3,"uc-regulations":306},{"useCase":4,"evidence":157,"blitsAiDeployments":213,"benchmarks":214,"indicative":215,"related":218,"indexability":304,"includeUnpublished":163},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":42,"macroEstimates":71,"feasibility":72,"implementation":84,"risk":125,"blitsAi":137,"faq":139,"related":149,"datePublished":152,"dateModified":152,"lastVerified":152,"changelog":153,"slug":156},"AI trade promotion optimization for consumer goods manufacturers","Trade promotion optimization","AI trade promotion optimization for CPG","PepsiCo's PromoAI runs across all global markets, with about 85% of its recommendations executed. Henkel uses generative AI to speed up promotion planning.","published","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.",[12,13,14,15],"trade promotion management AI","TPO software","AI revenue growth management","promotion planning optimization",[17,18],"manufacturing","retail-and-ecommerce",[20,21],"product-and-pricing","sales",[23,24],"prediction-and-scoring","recommendation-and-personalization",[26],"internal-tools","employee-facing","copilot","early-adopters","revenue-growth-management","Henkel's chief digital and information officer, Michael Nilles, describes trade promotion\nmanagement as \"still a complicated animal.\" Working with SAP, Henkel built a generative AI\nnatural language layer over trade promotion data, a tool that gives key account managers plain\nlanguage hints rather than one that forecasts lift and searches combinations itself, and Nilles\nsays it made the process \"much more intuitive for the key account managers.\" Independent trade\npress reporting on that deployment describes account managers going from a plan that took months\nto one ready to share by the next day.\n\nPepsiCo's own account of building PromoAI describes a move away from spreadsheet based planning\ntoward optimisation supported workflows, at a scale that puts the underlying problem in\nperspective: PepsiCo describes itself as a global leader in the consumer goods sector with annual\nrevenues exceeding USD 90 billion, and a single promotional calendar for that portfolio can be\nchosen from millions of product, promotion and timing combinations, while still respecting\nretailer specific rules on margin, volume and promotional frequency. Manual planning cannot\nsearch that space; it can only check a handful of scenarios a planner already suspects will work.",[],"1. **Forecast the response.** A machine learning model forecasts the incremental volume, the\n   lift above baseline sales, that each promotion mechanic would generate for a product, account\n   and time period, learning from past promotions, price and competitor activity.\n2. **Search the plan, not one scenario.** An optimisation model, not the forecast alone, searches\n   the combinations of timing, depth and mechanic across the whole account and calendar for the\n   plan that best meets a chosen goal: manufacturer revenue, retailer revenue, margin or volume,\n   within business rules such as minimum margin or a promotional frequency cap.\n3. **Let the planner set the goal.** Key account managers or revenue management teams choose\n   what to prioritise for a given account or period, and can adjust the weighting between\n   objectives such as revenue and margin rather than accepting a single fixed answer.\n4. **Review before it goes to the retailer.** Planners review the recommended calendar, can\n   override individual promotions, and only then take it into the negotiation with the retailer.\n5. **Learn from what happened.** Actual sell through after each promotion feeds back into the\n   forecasting model, so the next planning cycle starts from a better estimate of lift.",[35,36,37],"revenue-growth","cost-to-serve","employee-productivity",[39,40,41],"revenue-uplift","forecast-accuracy","productivity-gain",{"referenceOrg":43,"inputs":44,"formula":66,"currency":67,"period":68,"resultLabel":69,"caveat":70},"A consumer goods manufacturer with USD 1 billion in annual trade promotion spend",[45,52,59],{"key":46,"label":47,"low":48,"high":49,"unit":50,"note":51},"tradeSpend","Annual trade promotion spend",500000000,1500000000,"USD per year","Editorial assumption for a large consumer goods manufacturer's annual trade promotion spend, replace with your own trade spend figure.",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"ineffectiveShare","Share of that spend estimated to generate no incremental volume",0.1,0.2,"fraction of trade spend","Editorial assumption, replace with your own promotion effectiveness analysis; this is not sourced from either evidence record on this page.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"recoveredShare","Share of that ineffective spend an optimised plan recovers",0.15,0.3,"fraction of the ineffective spend","Editorial assumption, kept conservative because neither evidence record on this page discloses a comparable recovery figure.","tradeSpend * ineffectiveShare * recoveredShare","USD","per year","Trade spend efficiency recovered per year","Entirely editorial: PepsiCo discloses that about 85% of PromoAI's optimised recommendations are accepted and executed, and that planning cycles for specific pricing and promotional tasks have compressed from weeks to minutes, but it explicitly withholds the underlying financial figures as commercially sensitive, and Henkel's own reporting gives no quantified figure at all. Every input here should be replaced with the manufacturer's own promotion effectiveness data before this number is used for anything but illustration. It also leaves out the cost of the platform and data integration and any change in retailer relationships from a more disciplined promotion plan.",[],{"complexity":73,"complexityNote":74,"dataPrerequisites":75,"integrations":79},"high","The forecasting model is the easier half. The harder part is the optimisation layer that respects each retailer's own margin, volume and frequency rules at the same time as the manufacturer's own revenue and profit targets, and integrating with the trade spend, sales and finance systems that already hold the numbers a key account manager is measured on.",[76,77,78],"Promotion history per product, account and mechanic, with actual lift where it is known","Trade term and margin rules per retailer","Base price, cost and competitor activity data",[80,81,82,83],"Trade promotion management or revenue growth management system of record","Sales and account planning tools key account managers already use","Finance systems for trade spend accrual and settlement","Point of sale or shipment data for measuring actual promotion lift",{"steps":85,"guardrails":101,"humanInTheLoop":106,"kpisToInstrument":107,"failureModes":112},[86,89,92,95,98],{"title":87,"detail":88},"Start with the categories where promotion spend is largest","Optimise the accounts and categories that carry the most trade spend first, where a percentage improvement in efficiency is worth the most in absolute terms.",{"title":90,"detail":91},"Build the forecast before the optimisation","Get promotional lift forecasting accurate and trusted on its own before adding the optimisation layer on top of it; an optimiser built on a poor forecast just searches confidently for the wrong answer.",{"title":93,"detail":94},"Encode real retailer rules, not generic ones","Load each retailer's actual margin floors, volume commitments and promotional frequency limits, agreed with the account team, so the recommended plan is one a planner can actually take into the room.",{"title":96,"detail":97},"Let planners adjust the objective, not just the output","Give key account managers a way to reweight revenue, margin and volume for a specific account or period, so the system supports a negotiation instead of replacing the planner's judgement about that account.",{"title":99,"detail":100},"Close the loop with actual results","Measure the lift each promotion actually delivered against the forecast, and feed that back into the model, or the forecast quietly drifts from reality.",[102,103,104,105],"Retailer specific margin, volume and compliance rules enforced as hard constraints, not suggestions","A planner reviews and can override any recommended promotion before it reaches a retailer","Version history on every promotion plan, so a planner can see what changed and why","Segregation between the system that recommends a plan and the system that pays out trade spend","Key account managers and revenue management teams choose the objective, review every recommended plan, and take the final call on what goes to a retailer, especially in accounts where the relationship, not the model, decides the right approach. Finance reviews trade spend accruals against the plan actually executed, not the plan the model proposed.",[108,109,110,111],"Forecast accuracy of promotional lift, before and after each planning cycle","Share of recommended plans planners accept without changes, and what they change when they do not","Trade spend efficiency, incremental volume per unit of trade spend","Time from the start of a planning cycle to an agreed calendar",[113,116,119,122],{"title":114,"detail":115},"Optimising for revenue while margin erodes","A plan can hit a revenue target by promoting deeply and often, at the cost of margin. Set margin and frequency as hard constraints, not just one line on a scorecard.",{"title":117,"detail":118},"A forecast that has not seen this year's conditions","A model trained on stable years overstates lift when input costs, competitor activity or the account relationship has shifted. Monitor forecast error by account, not only in aggregate.",{"title":120,"detail":121},"Treating the recommended plan as final","A plan that goes to a retailer unreviewed misses account context the model cannot see. Keep a real review step, not a rubber stamp, especially in strategic accounts.",{"title":123,"detail":124},"No segregation between planning and payout","If the same system both recommends promotions and approves the resulting trade spend payout, errors and manipulation are harder to catch. Keep planning and financial settlement in separate systems with their own controls.",{"euAiAct":126,"regulations":129,"guidance":131,"controls":132,"incidents":136},{"tier":127,"basis":128},"minimal","The system optimises promotion terms between a manufacturer and a retailer; it does not decide a natural person's access to credit, employment, essential services or a regulated product, so it falls outside Annex III. It would need reassessment if a manufacturer used the same technique to set individually targeted prices or offers for identified consumers.",[130],"eu-ai-act",[],[133,134,135],"Hard constraints for retailer margin, volume and compliance rules, checked before a plan is finalised","Human review of every plan before it is presented to a retailer","Separation of duties between the planning system and trade spend payout approval",[],{"howToBuild":138},"Blits.ai does not build the demand forecasting or optimisation engine itself; that stays with\na revenue growth management platform or the manufacturer's own data science team. What\nBlits.ai adds is the conversational layer key account managers use around it. An **AI\nagent** with a **custom function** into the optimisation engine and a **SQL knowledge base**\nover trade spend and promotion history lets a planner ask questions in plain language, such as\nwhat a proposed promotion is forecast to cost or return for a given account, before it reaches\nthe negotiation.\n\nAn **agentic workflow** with **human in the loop approval** routes a finalised plan for sign\noff before it is shared with a retailer, and a **knowledge base** holds each retailer's own\ntrade terms and compliance rules so the agent's answers stay grounded in the account's actual\nagreement. The workflow's run history gives a full audit trail of every approval, and because\nthe platform is **model agnostic**, the same agent can run on whichever model the manufacturer\nalready trusts for other planning tools.",[140,143,146],{"question":141,"answer":142},"Does AI decide which promotions run?","In the deployments on this page, no. Henkel's chief digital and information officer describes the tool as making trade promotion \"more intuitive\" for key account managers, and PepsiCo's own paper describes PromoAI generating multiple optimised calendars under different objective settings and presenting them to the retailer \"as a menu of analytically grounded options,\" with planners able to apply manual modifications before a calendar is finalised. For PepsiCo, the system narrows millions of combinations to a short list of calendars; Henkel's tool works differently, surfacing hints from the data rather than searching combinations itself. Either way, a person still decides.",{"question":144,"answer":145},"What results have manufacturers reported?","PepsiCo's own paper reports that in most markets about 85% of PromoAI's optimised promotional recommendations were accepted and executed by the business, and that planning and execution cycles for specific pricing and promotional tasks compressed from weeks to minutes; it explicitly withholds the underlying financial figures as commercially sensitive. Henkel itself has not published a quantified result. Independent trade press reporting, not a Henkel quote, describes account managers producing a finished plan by the next day instead of the months it used to take.",{"question":147,"answer":148},"Is this the same as dynamic pricing to individual shoppers?","No. Trade promotion optimisation sets the terms of a promotion a manufacturer runs with a retailer, at the product and account level, not a price shown to an individual shopper. A system that personalised prices to identified consumers would raise different questions and likely a different EU AI Act analysis.",[150,151],"retail-demand-forecasting-and-replenishment","retail-dynamic-pricing-and-markdown-optimization","2026-09-29",[154],{"date":152,"note":155},"First published","trade-promotion-optimization-for-cpg",[158,188],{"title":159,"useCases":160,"organization":161,"vendors":166,"summary":170,"stage":171,"year":172,"channels":173,"languages":174,"metrics":175,"outcomeDisclosed":176,"sources":177,"verification":183,"grade":185,"id":186,"organizationSlug":187},"PepsiCo: PromoAI and PricingAI for revenue growth management",[156],{"name":162,"anonymized":163,"country":164,"region":165,"industry":17},"PepsiCo",false,"US","north-america",[167],{"name":168,"role":169},"In house","in-house","PepsiCo describes, in its own published account, two large scale optimisation systems built and deployed to support its revenue growth management: PromoAI, which couples machine learning promotional forecasts with a mixed integer linear programming model to search millions of product, promotion and timing combinations for the calendar that maximises PepsiCo and retailer revenue within business constraints, and PricingAI, which optimises base prices across the portfolio using Bayesian hierarchical models of price elasticity. The paper reports that planners and revenue management teams reviewed model outputs during validation before the systems were put into production use.","scaled",2026,[26],[],[],true,[178],{"url":179,"title":180,"publisher":181,"date":182},"https://arxiv.org/abs/2606.17941","PepsiCo Deploys AI-Driven Pricing and Promotion Optimization at Scale","arXiv (PepsiCo author preprint; published in INFORMS Journal on Applied Analytics)","2026-06-16",{"level":184,"checkedAt":152},"source-verified","B","pepsico-trade-promotion-optimization",null,{"title":189,"useCases":190,"organization":191,"vendors":195,"summary":199,"stage":200,"year":172,"channels":201,"languages":202,"metrics":203,"outcomeDisclosed":176,"sources":204,"verification":210,"grade":211,"id":212,"organizationSlug":187},"Henkel: generative AI for trade promotion management with SAP",[156],{"name":192,"anonymized":163,"country":193,"region":194,"industry":17},"Henkel","DE","europe",[196],{"name":197,"role":198},"SAP","platform","Henkel, the German consumer goods manufacturer, worked with SAP's co innovation team to build a generative AI natural language layer for trade promotion management on top of the Just Ask feature of SAP Analytics Cloud. It gives key account managers plain language hints over trade promotion data rather than forecasting lift and searching combinations itself, so it is not an optimisation engine of the kind PromoAI is. Henkel's chief digital and information officer, Michael Nilles, says the tool made trade promotion planning more intuitive for the company's key account managers, and independent trade press reporting describes account managers producing a finished plan by the next day instead of the months it used to take.","production",[26],[],[],[205],{"url":206,"title":207,"publisher":208,"date":209},"https://www.cio.com/article/4143020/4-gen-ai-success-stories.html","4 gen AI success stories","CIO","2026-03-17",{"level":184,"checkedAt":152},"C","henkel-trade-promotion-optimization",0,[],{"low":216,"high":217},7500000,90000000,[219,239,251,277],{"slug":150,"title":220,"shortTitle":221,"definition":222,"status":9,"industries":223,"functions":224,"patterns":227,"audience":229,"autonomy":230,"adoptionStage":231,"evidenceCount":232,"publicEvidenceCount":232,"organizations":233,"bestGrade":185,"headline":187,"lastVerified":238,"indexable":176},"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],[225,226],"operations","analytics-and-reporting",[23,228],"anomaly-detection","back-office","supervised-agent","mainstream",4,[234,235,236,237],"Albert Heijn","Morrisons","One Stop","Walmart","2026-09-27",{"slug":151,"title":240,"shortTitle":241,"definition":242,"status":9,"industries":243,"functions":244,"patterns":245,"audience":27,"autonomy":230,"adoptionStage":29,"segment":246,"evidenceCount":247,"publicEvidenceCount":247,"organizations":248,"bestGrade":185,"headline":187,"lastVerified":152,"indexable":176},"AI dynamic pricing and markdown optimization for retail","Dynamic pricing and markdown optimization","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.",[18],[20],[23],"pricing-and-merchandising",3,[234,249,250],"Delhaize America","Rimi Baltic",{"slug":252,"title":253,"shortTitle":254,"definition":255,"status":9,"industries":256,"functions":259,"patterns":260,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":247,"publicEvidenceCount":247,"organizations":264,"bestGrade":211,"headline":268,"lastVerified":238,"indexable":176},"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.",[257,17,258,18],"cross-industry","energy-and-utilities",[21,20],[261,262,263],"document-processing","agentic-workflow","computer-vision",[265,266,267],"DeAcero","Enpal","The ODP Corporation",{"kpi":269,"label":270,"unit":271,"n":272,"nUpTo":213,"kind":273,"value":274,"qualifier":275,"claimant":276,"organization":266,"vendorReported":176},"handling-time-reduction","Handling time reduction","percent",1,"reported",87.5,"exact","vendor",{"slug":278,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":284,"patterns":286,"audience":290,"autonomy":230,"adoptionStage":29,"segment":291,"evidenceCount":292,"publicEvidenceCount":292,"organizations":293,"bestGrade":185,"headline":299,"lastVerified":238,"indexable":176},"business-connectivity-quoting-and-service-assistant","AI assistant for B2B telecom quoting, sales and service","B2B quoting and service","An AI assistant that serves business customers of a telecom operator and the sellers who look after them: it answers product, pricing and contract questions, prepares configurations and quotes for connectivity, mobile fleets and devices, drafts responses to tenders, and handles routine service requests and fault tickets, with a sales or service specialist approving anything binding.",[283],"telecommunications",[21,285,20],"customer-service",[287,288,262,24,289],"conversational-agent","rag-knowledge-assistant","content-generation","customer-facing","front-office",5,[294,295,296,297,298],"Lumen Technologies","SoftBank Corp.","Telefónica España","Verizon","Vodafone Business",{"kpi":300,"label":301,"unit":271,"n":272,"nUpTo":213,"kind":273,"value":302,"qualifier":275,"claimant":303,"organization":295,"vendorReported":163},"containment-rate","Containment rate",70,"organization",{"indexable":176,"reasons":305},[],[307,313,319,327,334,341,347,354,362,369,376,383,389,395,402,409,415,422,428,434,440,447,452,459,464,469,474,480,487,493,500,506,512,518,523,528],{"id":130,"label":308,"issuer":309,"region":194,"url":310,"description":311,"useCases":312,"indexable":176},"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":314,"label":315,"issuer":309,"region":194,"url":316,"description":317,"useCases":318,"indexable":176},"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":320,"label":321,"issuer":322,"region":323,"url":324,"description":325,"useCases":326,"indexable":176},"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":328,"label":329,"issuer":330,"region":165,"url":331,"description":332,"useCases":333,"indexable":176},"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":335,"label":336,"issuer":337,"region":194,"url":338,"description":339,"useCases":340,"indexable":176},"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":342,"label":343,"issuer":309,"region":194,"url":344,"description":345,"useCases":346,"indexable":176},"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":348,"label":349,"issuer":350,"region":194,"url":351,"description":352,"useCases":353,"indexable":176},"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":355,"label":356,"issuer":357,"region":358,"url":359,"description":360,"useCases":361,"indexable":176},"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":363,"label":364,"issuer":365,"region":358,"url":366,"description":367,"useCases":368,"indexable":176},"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":370,"label":371,"issuer":372,"region":165,"url":373,"description":374,"useCases":375,"indexable":176},"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":377,"label":378,"issuer":379,"region":323,"url":380,"description":381,"useCases":382,"indexable":176},"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":384,"label":385,"issuer":309,"region":194,"url":386,"description":387,"useCases":388,"indexable":176},"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":390,"label":391,"issuer":392,"region":194,"url":393,"description":394,"useCases":388,"indexable":176},"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":396,"label":397,"issuer":398,"region":165,"url":399,"description":400,"useCases":401,"indexable":176},"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":403,"label":404,"issuer":405,"region":323,"url":406,"description":407,"useCases":408,"indexable":176},"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":410,"label":411,"issuer":309,"region":194,"url":412,"description":413,"useCases":414,"indexable":176},"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":416,"label":417,"issuer":418,"region":165,"url":419,"description":420,"useCases":421,"indexable":176},"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":423,"label":424,"issuer":425,"region":165,"url":426,"description":427,"useCases":421,"indexable":176},"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":429,"label":430,"issuer":309,"region":194,"url":431,"description":432,"useCases":433,"indexable":176},"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":435,"label":436,"issuer":437,"region":323,"url":438,"description":439,"useCases":433,"indexable":176},"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":441,"label":442,"issuer":443,"region":165,"url":444,"description":445,"useCases":446,"indexable":176},"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":448,"label":449,"issuer":309,"region":194,"url":450,"description":451,"useCases":446,"indexable":176},"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":453,"label":454,"issuer":455,"region":194,"url":456,"description":457,"useCases":458,"indexable":176},"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":460,"label":461,"issuer":357,"region":358,"url":462,"description":463,"useCases":458,"indexable":176},"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":465,"label":466,"issuer":309,"region":194,"url":467,"description":468,"useCases":458,"indexable":176},"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":470,"label":471,"issuer":309,"region":194,"url":472,"description":473,"useCases":458,"indexable":176},"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":475,"label":476,"issuer":309,"region":194,"url":477,"description":478,"useCases":479,"indexable":176},"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":481,"label":482,"issuer":483,"region":165,"url":484,"description":485,"useCases":486,"indexable":176},"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":488,"label":489,"issuer":309,"region":194,"url":490,"description":491,"useCases":492,"indexable":176},"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":494,"label":495,"issuer":496,"region":497,"url":498,"description":499,"useCases":292,"indexable":176},"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":501,"label":502,"issuer":503,"region":194,"url":504,"description":505,"useCases":232,"indexable":176},"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":507,"label":508,"issuer":509,"region":194,"url":510,"description":511,"useCases":232,"indexable":176},"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":513,"label":514,"issuer":515,"region":358,"url":516,"description":517,"useCases":247,"indexable":176},"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":519,"label":520,"issuer":309,"region":194,"url":521,"description":522,"useCases":247,"indexable":176},"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":524,"label":525,"issuer":309,"region":194,"url":526,"description":527,"useCases":247,"indexable":176},"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":529,"label":530,"issuer":531,"region":165,"url":532,"description":533,"useCases":247,"indexable":176},"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.",1790683494495]