[{"data":1,"prerenderedAt":627},["ShallowReactive",2],{"uc-retail-store-and-kiosk-assistant":3,"uc-regulations":420},{"useCase":4,"evidence":201,"blitsAiDeployments":295,"benchmarks":296,"indicative":311,"related":314,"indexability":418,"includeUnpublished":207},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":23,"channels":28,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":51,"macroEstimates":91,"feasibility":92,"implementation":106,"risk":149,"blitsAi":178,"faq":180,"related":190,"datePublished":196,"dateModified":196,"lastVerified":196,"changelog":197,"slug":200},"AI assistant for telecom retail stores, from associate copilot to digital human kiosk","Retail store and kiosk assistant","AI assistant for telecom stores and kiosks","Telecom store assistants answer staff on offers and devices. Salesforce reports 95% accuracy at launch for Bouygues Telecom; T-Mobile's app has over 83,000 users.","published","An AI assistant for telecom shops that gives store associates quick, sourced answers on plans, promotions, devices and the customer's account during the conversation, and that can also greet and serve customers directly on an in store screen or kiosk, sometimes as a digital human, handing them to an associate when they are ready to buy or need help.",[12,13,14,15,16],"store associate copilot","retail assistant for telecom","digital human kiosk","in store AI assistant","shop floor product assistant",[18],"telecommunications",[20,21,22],"sales","customer-service","knowledge-management",[24,25,26,27],"rag-knowledge-assistant","digital-human","conversational-agent","recommendation-and-personalization",[29,30,31,32],"kiosk","internal-tools","agent-desktop","web-chat","employee-facing","assist","early-adopters","front-office","Telecom stores sell technical products whose offers keep changing: new devices, discounts and\ntrade in values, and plans whose eligibility depends on the customer's contract. At T-Mobile, a\ndaily promotions report sent to retail representatives had become complex and hard to search,\ntrade in values sat in other systems, and device details meant a visit to manufacturers'\nwebsites, so representatives often left the customer conversation to find an answer. At Bouygues\nTelecom, contact centre reps sifted through more than 500 articles, sometimes skimming up to 12\npages for one inquiry, and turned to supervisors or colleagues when unsure, which gave customers\ninconsistent answers.\n\nMany customers, meanwhile, doubt their own technical knowledge and may feel embarrassed to ask a\nperson for help, as UneeQ notes in its Deutsche Telekom case study; unsure buyers tend to pick the\ncheapest option and can be disappointed. A static screen in a store can show a brochure, but it\ncannot answer a question about the customer's own situation.",[],"1. **Answer the associate in seconds.** On a tablet or the store system, the associate asks in\n   plain language about a promotion, a device comparison or a policy, and gets a summarised answer\n   from approved content and live data, with the source.\n2. **Bring the customer's context.** After the customer is identified, the assistant shows their\n   plan, contract end date, open cases and eligible offers, so the conversation starts informed.\n3. **Build comparisons to show.** It assembles device and plan comparisons that the associate can\n   show or send to the customer.\n4. **Serve customers at the screen.** On a kiosk or a digital human screen, customers ask\n   questions, compare options and check eligibility themselves, and the assistant calls an\n   associate or books a slot when they want to buy.\n5. **Keep every channel consistent.** The same knowledge serves the contact centre, online sales\n   and stores, so a customer hears the same answer everywhere.",[41,42,43],"employee-productivity","revenue-growth","customer-experience",[45,46,47,48,49,50],"accuracy","users-served","time-saved-per-task","conversion-rate-uplift","customer-satisfaction","employee-adoption",{"referenceOrg":52,"inputs":53,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"An operator with 300 stores",[54,59,66,72,80],{"key":55,"label":56,"low":57,"high":57,"unit":55,"note":58},"stores","Stores",300,"The reference operator.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"lookupsPerDay","Product and account lookups per store per day",20,40,"lookups per store per day","Editorial assumption, replace with your own store activity.",{"key":67,"label":68,"low":57,"high":69,"unit":70,"note":71},"openDays","Trading days per year",360,"days per year","Editorial assumption.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78,"sourceUrl":79},"hoursSaved","Associate time saved per lookup",0.03,0.07,"hours per lookup","Editorial assumption of about two to four minutes per lookup. Salesforce reports that Bouygues Telecom's Iris answers in seconds a task that once took minutes of manual searching.","https://www.salesforce.com/customer-stories/bouygues-telecom/agentic-service-faqs/",{"key":81,"label":82,"low":62,"high":83,"unit":84,"note":85},"hourlyCost","Fully loaded associate cost",30,"USD per hour","Editorial assumption, replace with your own cost.","stores * lookupsPerDay * openDays * hoursSaved * hourlyCost","USD","per year","Associate time released","Values only the associate time saved on lookups. It leaves out higher conversion and basket size from better informed conversations, shorter queues, faster onboarding of new staff, and the cost of the AI, devices, kiosks and integrations. Released time is only a saving if staffing or sales change as a result.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"medium","An associate copilot over a clean knowledge base is quick to deliver. Account context and live promotions need CRM and catalogue integration, and a customer facing digital human adds hardware, speech in a noisy store, accessibility and privacy design.",[96,97,98,99],"One current source for promotions, prices and trade in values with start and end dates","Device specifications from manufacturers or a product data feed","Knowledge articles rewritten for retrieval, with owners and review dates","Store and customer data access rules per role",[101,102,103,104,105],"Product catalogue and promotions system","CRM and customer account data","Point of sale and order systems","Store tablets, kiosks or digital human screens","Queue management or appointment booking in store",{"steps":107,"guardrails":123,"humanInTheLoop":129,"kpisToInstrument":130,"failureModes":136},[108,111,114,117,120],{"title":109,"detail":110},"Clean the knowledge first","Rewrite and standardise the articles associates use, as Bouygues Telecom did with Salesforce for its more than 500 articles, and give each an owner and review date.",{"title":112,"detail":113},"Set an accuracy bar before launch","Agree a minimum accuracy on a test set of real associate questions and launch only when the assistant meets it. Bouygues Telecom required its agent to exceed 90% accuracy or outperform a supervisor.",{"title":115,"detail":116},"Start with associates, then customers","Associates can judge and correct answers; customers cannot. Launch the copilot first and add a customer facing kiosk or digital human once answers are reliable.",{"title":118,"detail":119},"Design the kiosk for a real shop floor","Test speech in store noise, offer touch and text alternatives, avoid showing personal data on a public screen, and make calling an associate one tap.",{"title":121,"detail":122},"Share content across channels","Use one knowledge source for stores, contact centre and online sales, so an update reaches every channel at once.",[124,125,126,127,128],"Answers only from approved content and live tools, with the source shown to associates","Prices and promotions only from the catalogue, with end dates enforced","No personal account data on a public screen without identification and privacy screening","AI disclosure on every customer facing screen and digital human","No emotion recognition or camera analysis of customers without a lawful basis and clear notice","Associates decide what to tell and sell to the customer and remain responsible for the sale. Content owners approve every article and promotion the assistant uses, and store managers report wrong answers, which are reviewed weekly.",[131,132,133,134,135],"Answer accuracy on a weekly checked sample of associate questions","Weekly active associates as a share of store staff","Time to answer common questions, before and after","Conversion and basket size in stores with and without the assistant","Kiosk conversations handed to associates and resulting sales",[137,140,143,146],{"title":138,"detail":139},"Outdated promotions","Associates quote an ended offer from the assistant. Keep promotions in a tool with end dates, not in documents.",{"title":141,"detail":142},"A gimmick instead of a tool","A digital human draws attention but cannot answer real questions. Measure conversations that lead to help or a sale, not visits.",{"title":144,"detail":145},"Privacy on the shop floor","Account details appear on a screen others can see. Design the screen and identification flow for a public space.",{"title":147,"detail":148},"Different answers per channel","The store says one thing and the app another. Use one knowledge source for all channels.",{"euAiAct":150,"regulations":153,"guidance":159,"controls":171,"incidents":177},{"tier":151,"basis":152},"limited","A digital human or kiosk that talks to customers must be designed so that they are told they are interacting with an AI system (Article 50(1)). An associate copilot over product content is not listed in Annex III and is minimal risk. Inferring the emotions of employees at work is prohibited (Article 5(1)(f)); emotion recognition of customers by camera is high risk under Annex III point 1(c), biometric categorisation by sensitive or protected attributes is high risk under Annex III point 1(b), and categorisation that infers race, political opinions, religion or sexual orientation is prohibited under Article 5(1)(g). Both need separate legal review.",[154,155,156,157,158],"eu-ai-act","gdpr","telecom-consumer-rules","eecc","eu-accessibility-act",[160,166],{"title":161,"issuer":162,"region":163,"url":164,"note":165},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context, which includes digital humans on store screens.",{"title":167,"issuer":168,"region":163,"url":169,"note":170},"Customers to get clearer broadband information","Ofcom","https://www.ofcom.org.uk/phones-and-broadband/bills-and-charges/customers-to-get-clearer-broadband-information","UK providers must give clear information about the broadband technology before a customer buys, including in person, so in store assistants must use the same approved descriptions.",[172,173,174,175,176],"AI disclosure on every customer facing screen","Content ownership and review dates for all articles and promotions","Role based access to customer data in the store","Accuracy tests before launch and after every content or model change","Privacy impact assessment for kiosks and any camera or microphone in store",[],{"howToBuild":179},"On Blits.ai the associate copilot is an **AI agent** over a **knowledge base** of product,\npromotion and policy content with hybrid retrieval and document version control, with **custom\nfunctions** for live promotions, trade in values and the customer's account. Associates use it\nthrough the web widget on store tablets or in **Microsoft Teams**, and rich in chat cards show\ndevice and plan options that the associate can show the customer.\n\nFor customers, the same agent runs as a **digital human**: a photorealistic avatar with lip\nsynced speech, streamed to an in store screen, with **voice** input and text as an alternative.\n**Human handover** escalates to a staff member, **guardrails** check answers against the\noperator's policies, **test suites** of real associate questions are rerun after every content\nchange, and the platform is model agnostic with EU and UAE data residency.",[181,184,187],{"question":182,"answer":183},"How accurate does a store assistant need to be?","Set the bar before launch. Bouygues Telecom required its Iris agent to exceed 90% accuracy or outperform a supervisor before it entered the contact centre; Salesforce reports it reached 95% on day one, and Iris has since been extended to 500 retail stores.",{"question":185,"answer":186},"Should we start with a digital human or an associate copilot?","Starting with associates carries less risk, because they can catch mistakes; the documented deployments at T-Mobile (PromoGenius, over 83,000 unique users among retail and call centre staff) and Bouygues Telecom (Iris) are both associate tools. Customer facing digital humans, such as Deutsche Telekom's Selena, who explains home broadband options, and Max, who answers questions on the Telekom website and app, guide customers to products; in a store, plan a clear handover to an associate when the customer is ready to buy.",{"question":188,"answer":189},"What privacy issues come with kiosks?","Screens in a public space should not show account details without identification and privacy design, microphones and cameras need a clear legal basis and notice, and inferring the emotions of employees at work is prohibited under Article 5 of the EU AI Act.",[191,192,193,194,195],"plan-upgrade-and-sales-assistant","live-agent-assist","order-to-activation-and-esim-onboarding-assistant","atm-and-self-service-device-assistance","enterprise-knowledge-search","2026-09-27",[198],{"date":196,"note":199},"First published","retail-store-and-kiosk-assistant",[202,245,272],{"title":203,"useCases":204,"organization":205,"vendors":209,"summary":213,"stage":214,"year":215,"channels":216,"languages":217,"metrics":219,"outcomeDisclosed":232,"sources":233,"verification":239,"grade":242,"id":243,"organizationSlug":244},"Bouygues Telecom: Iris answer agent for contact centre reps and store associates",[200],{"name":206,"anonymized":207,"country":208,"region":163,"industry":18},"Bouygues Telecom",false,"FR",[210],{"name":211,"role":212},"Salesforce","platform","Bouygues Telecom built Iris, an employee facing agent on Salesforce Agentforce, over a rewritten knowledge base of more than 500 articles and a unified customer profile. Its 6,000 service reps ask Iris questions about billing, technical issues and promotions during calls, and the same answers are now available to associates in 500 retail stores. Before launch, Bouygues Telecom required Iris to exceed 90% accuracy or outperform a supervisor; Salesforce reports 95% on day one. Reps stay in control of what the customer hears.","scaled",2026,[31,30],[218],"fr",[220,227],{"kpi":45,"value":221,"unit":222,"qualifier":223,"period":224,"claimant":225,"quote":226,"sourceUrl":79},95,"percent","exact","at launch, against a 90% threshold","vendor","Iris cleared it on day one, reaching 95% accuracy.",{"kpi":46,"value":228,"unit":229,"qualifier":223,"period":230,"claimant":225,"quote":231,"sourceUrl":79},6000,"count","contact centre service reps using Iris","Today, 90% of the 6,000 service reps who use Iris rate it four or five stars — a clear signal of trust in the answers it delivers.",true,[234,236],{"url":79,"title":235,"publisher":211},"Bouygues Telecom delivers answers in seconds with agentic service",{"url":237,"title":238,"publisher":211},"https://www.salesforce.com/customer-stories/bouygues-telecom/","Bouygues Telecom serves more, faster as an Agentic Enterprise",{"level":240,"checkedAt":241},"source-verified","2026-09-26","C","bouygues-telecom-iris-service-agent",null,{"title":246,"useCases":247,"organization":248,"vendors":252,"summary":255,"stage":214,"year":256,"channels":257,"languages":258,"metrics":260,"outcomeDisclosed":232,"sources":267,"verification":270,"grade":242,"id":271,"organizationSlug":244},"T-Mobile: PromoGenius app and product agent for retail and care staff",[200,191],{"name":249,"anonymized":207,"country":250,"region":251,"industry":18},"T-Mobile","US","north-america",[253],{"name":254,"role":212},"Microsoft","T-Mobile built PromoGenius on Power Apps to give retail and call centre representatives one place for current promotions, discounts and trade in values, used on iPads on the shop floor. An agent built in Copilot Studio reads more than 20 device makers' websites, answers technical questions in natural language during a customer conversation and builds comparison tables that can be shown to the customer. Microsoft reports over 83,000 unique users and 500,000 launches a month for the app, which supports all T-Mobile retail stores and call centres.",2025,[30,29],[259],"en",[261],{"kpi":46,"value":262,"unit":229,"qualifier":263,"period":264,"claimant":225,"quote":265,"sourceUrl":266},83000,"at-least","unique users, retail and call centre staff","The app, called PromoGenius, is the second most popular app at T-Mobile, supporting all T-Mobile retail outlets and call centers, with over 83,000 unique users and 500,000 launches a month.","https://www.microsoft.com/en/customers/story/23087-t-mobile-usa-microsoft-copilot-studio",[268],{"url":266,"title":269,"publisher":254},"T-Mobile drives more effective customer conversations with Microsoft Power Apps and Copilot Studio",{"level":240,"checkedAt":241},"t-mobile-promogenius-retail-agent",{"title":273,"useCases":274,"organization":275,"vendors":278,"summary":281,"stage":282,"year":283,"channels":284,"languages":286,"metrics":288,"outcomeDisclosed":207,"sources":289,"verification":293,"grade":242,"id":294,"organizationSlug":244},"Deutsche Telekom: digital human sales and service assistants",[200],{"name":276,"anonymized":207,"country":277,"region":163,"industry":18},"Deutsche Telekom","DE",[279],{"name":280,"role":212},"UneeQ","Deutsche Telekom uses a family of UneeQ digital humans that speak German and guide customers to products: Selena explains home broadband options after asking about the customer's home and lifestyle, Max answers customer questions on the Telekom website and app, and Mia works at events such as MWC. The vendor presents them as a way to give hesitant customers confidence in technical purchases. The case study shows conversion, cart abandonment and rating tiles without readable figures, so no metric is recorded. The case study does not describe screens in Telekom shops; the kiosk channel reflects Mia's use at events.","production",2024,[32,285,29],"mobile-app",[287],"de",[],[290],{"url":291,"title":292,"publisher":280},"https://www.digitalhumans.com/case-studies/deutsche-telekom-case-study","UneeQ Case Study, Deutsche Telekom",{"level":240,"checkedAt":241},"deutsche-telekom-uneeq-digital-humans",0,[297,305],{"kpi":46,"label":298,"unit":229,"aggregate":207,"higherIsBetter":232,"n":299,"nUpTo":295,"median":300,"min":228,"max":262,"byClaimant":301,"vendorOnly":232,"points":302},"Users served",2,44500,{"organization":295,"vendor":299,"regulator":295,"independent":295},[303,304],{"evidenceId":271,"organization":249,"value":262,"qualifier":263,"claimant":225,"grade":242,"pooled":232},{"evidenceId":243,"organization":206,"value":228,"qualifier":223,"claimant":225,"grade":242,"pooled":232},{"kpi":45,"label":306,"unit":222,"aggregate":232,"higherIsBetter":232,"n":307,"nUpTo":295,"median":221,"min":221,"max":221,"byClaimant":308,"vendorOnly":232,"points":309},"Accuracy",1,{"organization":295,"vendor":307,"regulator":295,"independent":295},[310],{"evidenceId":243,"organization":206,"value":221,"qualifier":223,"claimant":225,"grade":242,"pooled":232},{"low":312,"high":313},1080000,9072000,[315,342,372,390,403],{"slug":191,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":320,"patterns":322,"audience":325,"autonomy":326,"adoptionStage":35,"segment":36,"evidenceCount":327,"publicEvidenceCount":327,"organizations":328,"bestGrade":337,"headline":338,"lastVerified":241,"indexable":232},"AI assistant for telecom plan upgrades, add ons and sales","Plan upgrade and sales assistant","An AI assistant that helps existing and prospective customers choose, compare and buy the right mobile, broadband or TV plan, device or extra, in the app, in messaging, on the phone or through a human advisor, using the customer's usage and eligibility and the operator's current offers, and that completes the order or passes a ready quote to a person.",[18],[20,21,321],"marketing",[27,26,24,323,324],"voice-agent","agentic-workflow","customer-facing","supervised-agent",9,[329,330,331,332,249,333,334,335,336],"Reliance Jio","Mobily","Orange France","Singtel","Telenet","Verizon","Virgin Media O2","Vodafone","B",{"kpi":48,"label":339,"unit":222,"n":307,"nUpTo":295,"kind":340,"value":341,"qualifier":223,"claimant":225,"organization":333,"vendorReported":232},"Conversion uplift","reported",75,{"slug":192,"title":343,"shortTitle":344,"definition":345,"status":9,"industries":346,"functions":353,"patterns":355,"audience":33,"autonomy":34,"adoptionStage":359,"evidenceCount":360,"publicEvidenceCount":361,"organizations":362,"bestGrade":337,"headline":368,"lastVerified":196,"indexable":232},"Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[347,348,349,18,350,351,352],"cross-industry","banking","insurance","healthcare","retail-and-ecommerce","technology",[21,354],"operations",[356,24,357,358],"speech-analytics","summarization","content-generation","mainstream",7,5,[363,364,365,366,367],"DBS Bank","Definity","Oportun","SEB","SIGNAL IDUNA",{"kpi":369,"label":370,"unit":222,"n":299,"nUpTo":295,"kind":340,"value":371,"qualifier":223,"claimant":225,"organization":364,"vendorReported":232},"productivity-gain","Productivity gain",15,{"slug":193,"title":373,"shortTitle":374,"definition":375,"status":9,"industries":376,"functions":377,"patterns":379,"audience":325,"autonomy":326,"adoptionStage":382,"segment":36,"evidenceCount":383,"publicEvidenceCount":383,"organizations":384,"bestGrade":337,"headline":385,"lastVerified":241,"indexable":232},"AI assistant for telecom order to activation and eSIM onboarding","Order to activation and eSIM onboarding","An AI assistant that takes a new or existing customer from order to a working service: it collects and checks the order details, guides number porting, eSIM download or SIM activation and installation appointments, tracks the order and fixes or escalates the step that is stuck, on messaging, app, web or phone.",[18],[20,378,21,354],"onboarding-and-kyc",[26,324,380,381],"classification-and-routing","document-processing","emerging",3,[329,332,334],{"kpi":386,"label":387,"unit":222,"n":307,"nUpTo":295,"kind":340,"value":388,"qualifier":223,"claimant":389,"organization":332,"vendorReported":207},"automation-rate","Automation rate",76,"organization",{"slug":194,"title":391,"shortTitle":392,"definition":393,"status":9,"industries":394,"functions":395,"patterns":396,"audience":325,"autonomy":326,"adoptionStage":382,"segment":36,"evidenceCount":307,"publicEvidenceCount":307,"organizations":397,"bestGrade":337,"headline":399,"lastVerified":196,"indexable":232},"AI agent for ATM and self service device assistance","ATM and device assistance","An AI agent that helps customers with problems at or around ATMs and other self service devices, such as a withdrawal that did not pay out, a retained card, a blocked PIN or finding a working machine with cash, over the app, chat or phone, and that opens and tracks the claim or hands it to a person when it cannot be resolved.",[348],[21,354],[26,323,324],[398],"NatWest Group",{"kpi":400,"label":401,"unit":222,"n":307,"nUpTo":295,"kind":340,"value":402,"qualifier":223,"claimant":389,"organization":398,"vendorReported":207},"customer-satisfaction-uplift","Satisfaction uplift",150,{"slug":195,"title":404,"shortTitle":405,"definition":406,"status":9,"industries":407,"functions":411,"patterns":412,"audience":33,"autonomy":34,"adoptionStage":359,"evidenceCount":413,"publicEvidenceCount":413,"organizations":414,"bestGrade":337,"headline":244,"lastVerified":196,"indexable":232},"AI enterprise knowledge search for employees","Enterprise knowledge search","An assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own policies, procedures, product manuals and research, with citations to the source documents and only from documents the employee is allowed to see.",[347,348,408,349,409,410],"wealth-and-asset-management","government","professional-services",[22,354,21],[24,26,357],4,[415,416,367,417],"Bank of America","Morgan Stanley","Wells Fargo",{"indexable":232,"reasons":419},[],[421,426,431,439,446,452,459,466,474,481,487,493,500,506,512,517,524,529,535,540,545,551,557,562,567,573,580,585,591,598,604,610,616,621],{"id":154,"label":422,"issuer":162,"region":163,"url":423,"description":424,"useCases":425,"indexable":232},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":155,"label":427,"issuer":162,"region":163,"url":428,"description":429,"useCases":430,"indexable":232},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":432,"label":433,"issuer":434,"region":435,"url":436,"description":437,"useCases":438,"indexable":232},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":440,"label":441,"issuer":442,"region":251,"url":443,"description":444,"useCases":445,"indexable":232},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":447,"label":448,"issuer":162,"region":163,"url":449,"description":450,"useCases":451,"indexable":232},"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":453,"label":454,"issuer":455,"region":163,"url":456,"description":457,"useCases":458,"indexable":232},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":460,"label":461,"issuer":462,"region":163,"url":463,"description":464,"useCases":465,"indexable":232},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":467,"label":468,"issuer":469,"region":470,"url":471,"description":472,"useCases":473,"indexable":232},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":475,"label":476,"issuer":477,"region":470,"url":478,"description":479,"useCases":480,"indexable":232},"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":482,"label":483,"issuer":484,"region":435,"url":485,"description":486,"useCases":62,"indexable":232},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":488,"label":489,"issuer":490,"region":251,"url":491,"description":492,"useCases":62,"indexable":232},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":494,"label":495,"issuer":496,"region":163,"url":497,"description":498,"useCases":499,"indexable":232},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":501,"label":502,"issuer":503,"region":435,"url":504,"description":505,"useCases":371,"indexable":232},"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.",{"id":507,"label":508,"issuer":162,"region":163,"url":509,"description":510,"useCases":511,"indexable":232},"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":513,"label":514,"issuer":162,"region":163,"url":515,"description":516,"useCases":511,"indexable":232},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":518,"label":519,"issuer":520,"region":251,"url":521,"description":522,"useCases":523,"indexable":232},"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":158,"label":525,"issuer":162,"region":163,"url":526,"description":527,"useCases":528,"indexable":232},"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":530,"label":531,"issuer":532,"region":251,"url":533,"description":534,"useCases":528,"indexable":232},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":156,"label":536,"issuer":537,"region":435,"url":538,"description":539,"useCases":528,"indexable":232},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":157,"label":541,"issuer":162,"region":163,"url":542,"description":543,"useCases":544,"indexable":232},"European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":546,"label":547,"issuer":548,"region":251,"url":549,"description":550,"useCases":544,"indexable":232},"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":552,"label":553,"issuer":469,"region":470,"url":554,"description":555,"useCases":556,"indexable":232},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":558,"label":559,"issuer":162,"region":163,"url":560,"description":561,"useCases":556,"indexable":232},"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":563,"label":564,"issuer":162,"region":163,"url":565,"description":566,"useCases":556,"indexable":232},"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":568,"label":569,"issuer":570,"region":163,"url":571,"description":572,"useCases":327,"indexable":232},"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.",{"id":574,"label":575,"issuer":576,"region":251,"url":577,"description":578,"useCases":579,"indexable":232},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":581,"label":582,"issuer":162,"region":163,"url":583,"description":584,"useCases":579,"indexable":232},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":586,"label":587,"issuer":162,"region":163,"url":588,"description":589,"useCases":590,"indexable":232},"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":592,"label":593,"issuer":594,"region":595,"url":596,"description":597,"useCases":361,"indexable":232},"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":599,"label":600,"issuer":601,"region":163,"url":602,"description":603,"useCases":413,"indexable":232},"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":605,"label":606,"issuer":607,"region":163,"url":608,"description":609,"useCases":413,"indexable":232},"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":611,"label":612,"issuer":613,"region":470,"url":614,"description":615,"useCases":383,"indexable":232},"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":617,"label":618,"issuer":162,"region":163,"url":619,"description":620,"useCases":383,"indexable":232},"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":622,"label":623,"issuer":624,"region":251,"url":625,"description":626,"useCases":383,"indexable":232},"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.",1790598297544]