[{"data":1,"prerenderedAt":574},["ShallowReactive",2],{"uc-drive-thru-voice-ordering-agent":3,"uc-regulations":351},{"useCase":4,"evidence":193,"blitsAiDeployments":253,"benchmarks":254,"indicative":261,"related":264,"indexability":349,"includeUnpublished":199},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":48,"macroEstimates":82,"feasibility":83,"implementation":95,"risk":139,"blitsAi":165,"faq":167,"related":180,"datePublished":181,"dateModified":182,"lastVerified":182,"changelog":183,"slug":192},"AI voice agent for drive through and phone order taking","Drive through voice ordering","Drive through voice AI ordering, explained","Wendy's reports its FreshAi voice agent completed 86% of pilot orders without staff stepping in, on average. See how it works, what it can earn and where it fails.","published","A conversational voice agent that takes the customer's spoken order at a drive through speaker or on a restaurant phone line, understands menu customizations and slang, applies real time stock and pricing, offers a relevant upsell, and sends the finished order straight to the point of sale, only calling a team member when the conversation goes outside what it can handle.",[12,13,14,15],"AI drive through ordering","voice ordering bot","conversational drive through assistant","restaurant phone ordering AI",[17],"travel-and-hospitality",[19,20],"customer-service","sales",[22,23],"voice-agent","recommendation-and-personalization",[25],"voice","customer-facing","supervised-agent","early-adopters","front-of-house","At the drive through speaker, a human order taker has seconds to parse an order over engine noise,\na crackling speaker and a regional accent, remember dozens of possible customizations, and still\noffer an upsell, while a line of cars builds up behind. Phone ordering has the same pressure without\nthe noise. Presto's\nown announcement of its Checkers & Rally's rollout cites a Franchise Times estimate that more than\n80% of quick service restaurant sales come through the drive through, which is why speed of\nservice and order accuracy at that single window matter directly to a restaurant's revenue.\n\nOn a short shift, a voice agent that takes the order reliably can free the person who would have\ntaken it to bag orders, run the window or coach a new hire instead.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"A Presto Automation press release cites a Franchise Times estimate that over 80% of quick service restaurant sales are generated from the drive through.","Checkers & Rally's and Presto Announce Largest Ever Rollout of Drive-Thru A.I. Voice Assistant in the Hospitality Industry","https://presto.com/checkers-rallys-and-presto-announce-largest-ever-rollout-of-drive-thru-a-i-voice-assistant-in-the-hospitality-industry/",2022,"1. **Listen through noise and casual speech.** The agent runs streaming speech recognition tuned\n   for the drive through environment: engine and traffic noise, conversation inside the car and\n   regional accents. Generative systems built for this job are designed to handle the huge number\n   of ways a real customer phrases an order, casual conversation included, rather than a fixed set\n   of recognized phrases.\n2. **Resolve the order against the live menu.** Every item, price, combo rule and limited time\n   offer comes from the restaurant's own point of sale and inventory feed, so the agent never\n   offers an item that is 86'd (out of stock) at that specific store.\n3. **Ask only what is missing.** Size, sauce, spice level and combo choices are confirmed with\n   short follow up questions, the same way a trained order taker would, rather than a rigid script.\n4. **Offer one relevant upsell.** A single, contextual suggestion (a size upgrade, a drink, a\n   dessert) is offered once, on the items the customer has already chosen.\n5. **Confirm and transmit.** The agent reads the order back, sends it to the point of sale and\n   kitchen display exactly as a person would key it in, so the kitchen sees one consistent order\n   format regardless of who or what took it.\n6. **Hand over cleanly.** Anything the agent is not confident about (an unusual request, a\n   complaint, a large or clearly abusive order, a payment problem) goes to a team member at the\n   window with the partial order already on screen.",[39,40,41,42],"cost-to-serve","customer-experience","revenue-growth","employee-productivity",[44,45,46,47],"containment-rate","handling-time-reduction","revenue-uplift","customer-satisfaction-uplift",{"referenceOrg":49,"inputs":50,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A quick service chain with 500 corporate and franchised drive through restaurants",[51,56,63,70],{"key":52,"label":53,"low":54,"high":54,"unit":52,"note":55},"restaurants","Drive through restaurants on the platform",500,"The reference chain.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"ordersPerRestaurantPerDay","Drive through orders per restaurant per day",200,400,"orders per restaurant per day","Editorial assumption for a mid sized quick service drive through. Replace with your own transaction count.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"automationShare","Share of orders the agent completes without a staff member stepping in",0.5,0.86,"fraction of orders","Conservative against the evidence on this page. Wendy's reports an average of 86% of pilot orders handled without team member intervention; the low end covers a first launch, a noisier market or the phone channel.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"upsellGainPerAutomatedOrder","Extra revenue per automated order from a consistent upsell offer",0.1,0.3,"USD per order","Editorial assumption. Replace with your own average check data from an A/B test of the upsell prompt.","restaurants * ordersPerRestaurantPerDay * 365 * automationShare * upsellGainPerAutomatedOrder","USD","per year","Additional annual revenue from consistent AI upselling on automated orders","Upsell revenue only, counted on every automated order as if staff never upsold before, which overstates the incremental gain wherever staff already upsell well. It also leaves out the technology, integration and support cost, any labor savings from freeing staff to run the window, and any change in average speed of service.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":90},"medium","The speech recognition is the visible challenge, but the real work is the same as any point of sale integration: a live feed of menu, price and 86'd items per restaurant, and a reliable path to hand the order to the kitchen exactly as if a person had keyed it in.",[87,88,89],"Live menu, pricing, combo rules and limited time offers per restaurant","Real time stock or \"86 list\" feed so the agent never offers what is unavailable","Recorded or transcribed real orders per market, including accents and slang, to tune recognition",[91,92,93,94],"Point of sale system, to receive the finished order in the restaurant's normal format","Kitchen display system, so cooks see one consistent order regardless of who took it","Digital menu board, to show what the agent heard back to the customer","Payment terminal, when payment is also taken at the speaker",{"steps":96,"guardrails":115,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":126},[97,100,103,106,109,112],{"title":98,"detail":99},"Pilot in one market before any rollout","Run the agent in a small number of company owned restaurants first, with a live audio feed a supervisor can listen to, before offering it to franchisees.",{"title":101,"detail":102},"Define exactly when it hands over","Write down the triggers for handover: repeated misunderstanding, a complaint, a clearly abusive or joke order, a payment failure, and any request the menu data does not cover.",{"title":104,"detail":105},"Tune per market, not once","Accents, ambient noise and local slang differ by restaurant. Collect real failed orders from each market and retrain or reprompt against them before wider rollout.",{"title":107,"detail":108},"Cap the upsell","Offer one relevant upsell per order, never repeat it if declined, and measure whether it lifts average check or lifts complaints before turning it up.",{"title":110,"detail":111},"Keep a human always reachable at the window","The team member at the window should see the order building in real time and be able to take over mid order without the customer repeating themselves.",{"title":113,"detail":114},"Verify the vendor's own numbers","Ask exactly how a reported automation rate is measured, whether any off site human reviews or corrects orders, and get it in writing before it goes into a business case.",[116,117,118,119],"Hard stop and handover on abusive, absurd or clearly prank orders (for example implausible quantities)","No item, price or combo offered outside the live menu feed for that specific restaurant","Upsell frequency capped at one offer per order, never repeated if declined","Masking of payment card data at the gateway when payment is taken by voice","A team member is always available to take over at the window without the customer repeating the order, and operations staff review a daily sample of completed and handed over orders to catch misheard items before they reach a customer.",[122,123,124,125],"Containment or automation rate by daypart, defined the same way every time it is reported","Order accuracy verified against what the customer actually receives at the window","Average check size on automated orders versus staff taken orders on the same menu","Handover reasons, so recurring gaps in the menu data or recognition get fixed",[127,130,133,136],{"title":128,"detail":129},"Misheard items reaching the window","Noise, accents or menu ambiguity produce a wrong item that is not caught before pickup. Mitigate with an order readback the customer can correct and a final visual check at the window.",{"title":131,"detail":132},"A reported automation rate that hides human labor","The US Securities and Exchange Commission found that one voice ordering vendor's proprietary units required substantial off site human order takers, and that its reported automation rate in fact excluded that off site help. Treat any vendor's automation rate as a claim to verify, not a fact.",{"title":134,"detail":135},"Upselling that annoys rather than lifts revenue","A pushy or repeated upsell prompt lowers satisfaction even when it lifts average check in the short term. Track satisfaction and complaint mentions alongside check size.",{"title":137,"detail":138},"A pilot that never scales","Results in one quiet, well maintained pilot restaurant do not predict a noisy, understaffed one. Expand market by market and re measure before each expansion.",{"euAiAct":140,"regulations":143,"guidance":147,"controls":154,"incidents":160},{"tier":141,"basis":142},"limited","Article 50(1): customers must be told they are dealing with an AI system, unless that is obvious from the point of view of a natural person who is reasonably well informed, observant and circumspect, given the circumstances and context of use. Taking a food order is not an Annex III use, so this stays a transparency obligation, not a high risk one.",[144,145,146],"eu-ai-act","gdpr","pci-dss",[148],{"title":149,"issuer":150,"region":151,"url":152,"note":153},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","People must be informed that they are interacting with an AI system unless this is obvious from the context, which is relevant to how a drive through voice agent identifies itself.",[155,156,157,158,159],"Clear, consistent disclosure that the customer is speaking with an AI voice system","Written, verifiable definition of any automation rate before it is used internally or externally","Daily human review of a sample of completed and handed over orders","Change control before any new upsell prompt or menu category goes live","PCI DSS scope review wherever payment is taken by voice, and a check for US state biometric or voice privacy laws (for example Illinois BIPA) before storing or matching a caller's voice",[161],{"title":162,"url":163,"note":164},"SEC cease and desist order against Presto Automation over drive through voice AI claims","https://www.sec.gov/files/litigation/admin/2025/33-11352.pdf","In January 2025 the US Securities and Exchange Commission found that, from 2021 to 2023, Presto Automation made materially misleading statements about its Presto Voice product for drive through ordering: units powered by its proprietary technology required substantial human order takers based abroad, and its reported automation rate in fact referred only to orders completed without on site restaurant staff, not without any human help.",{"howToBuild":166},"Blits.ai has no drive through speaker integration today, so on Blits.ai this maps to the phone\nordering half of this use case: a **voice agent** on the **telephony channel**, with real time\nstreaming speech to text and text to speech, backed by a small set of **custom functions** that\nread live menu, price and stock data from the restaurant's point of sale and write the finished\norder back to it. A **knowledge base** holds the current menu and combo rules per location so\nthe agent never offers what a specific restaurant does not have, and a **flow** encodes the\nfixed steps every order needs (confirm items, offer one upsell, read back, transmit) around the\nopen ended conversation.\n\n**Guardrails** block abusive or out of policy requests before they reach the model. **PII\nmasking** and **credit card number detection** apply to the call's transcribed text, not to the\naudio itself, so a caller who reads out a card number should be routed to DTMF capture or a\npayment link instead. **Human handover** passes an unclear or escalated order to a team member\nby call transfer or by live takeover from the admin console. **Test suites** check order\ntranscripts (text) on every change before it goes live, and the separate speech to text quality\ncheck utility compares STT providers on recorded noisy or accented audio. **Analytics** give per\nbot dashboards, conversation logs and custom calculated statistics; containment rate and\nhandover reason reporting, along with breakdowns by restaurant or daypart and tracking upsell\nperformance, need custom reporting on top of the platform. The platform is model agnostic, so\nthe underlying speech and language models can be swapped as they improve.",[168,171,174,177],{"question":169,"answer":170},"What share of drive through orders can a voice agent complete without staff help?","Wendy's reports that during its FreshAi pilot, orders handled without a restaurant team member stepping in averaged 86%. Results depend heavily on menu complexity, market noise and how the rate is defined, so ask any vendor exactly how they measure it.",{"question":172,"answer":173},"Is drive through voice ordering high risk under the EU AI Act?","Usually not. Taking a food order is not one of the high risk uses listed in Annex III, so it falls under the Article 50 transparency duty: customers must be able to tell they are speaking with an AI system.",{"question":175,"answer":176},"Can a vendor's reported \"automation rate\" be trusted at face value?","Verify the definition first. The US SEC found that one vendor's reported automation rate excluded substantial off site human order takers, so the same words can describe very different levels of automation between vendors.",{"question":178,"answer":179},"Does voice ordering replace the person at the drive through window?","Not on Wendy's own account. Wendy's describes FreshAi as an assistant, not a replacement, meant to let crew members focus on preparing and serving food and building the relationships that bring customers back. A sensible design still keeps a team member able to take over when the conversation goes outside what the agent can handle, but that handover point is editorial advice here, not something Wendy's states.",[],"2026-09-29","2026-09-30",[184,186,188,190],{"date":182,"note":185},"Published after review by an automated review workflow (independent skeptic review).",{"date":182,"note":187},"Fixed adversarial review blockers: dropped the problem section's unsourced superlative; narrowed the Blits.ai section's PII masking, card detection, test suite and handover claims to what the platform actually covers for a phone call (transcribed text, DTMF or payment link, call transfer, live takeover), and to the analytics the dashboard actually reports (per bot dashboards, conversation logs, custom statistics, not containment or handover reason metrics). Also corrected the EU AI Act Article 50 basis to the regulation's own \"obvious\" test, added GDPR to risk.regulations, corrected Wendy's product name to \"FreshAi\" throughout, trimmed the metaDescription and FAQ 4 to what Wendy's source states.",{"date":182,"note":189},"Unpublished by an automated review workflow (independent skeptic review).",{"date":181,"note":191},"First published","drive-thru-voice-ordering-agent",[194,231],{"title":195,"useCases":196,"organization":197,"vendors":202,"summary":206,"stage":207,"year":208,"channels":209,"languages":210,"metrics":212,"outcomeDisclosed":221,"sources":222,"verification":226,"grade":228,"id":229,"organizationSlug":230},"Wendy's: FreshAI generative AI drive through voice ordering",[192],{"name":198,"anonymized":199,"country":200,"region":201,"industry":17},"The Wendy's Company",false,"US","north-america",[203],{"name":204,"role":205},"Google Cloud","platform","Wendy's partnered with Google Cloud to build FreshAI, a generative AI voice assistant for drive through ordering, built to handle casual conversation and the more than 200 billion ways Wendy's says a customer can order a Dave's Double, something Wendy's says a traditional rule based system could not do. After months of testing in Columbus, Ohio, FreshAI was active across four restaurants owned by the company in the Columbus, OH market at the time of this update, with franchisee pilots planned for 2024. Wendy's measures success as the share of orders completed without a team member stepping in.","pilot",2023,[25],[211],"en",[213],{"kpi":44,"value":214,"unit":215,"qualifier":216,"period":217,"claimant":218,"quote":219,"sourceUrl":220},86,"percent","exact","during the Columbus, Ohio pilot","organization","Our accuracy during the pilot, measured as the percentage of orders successfully handled by Wendy's FreshAi without restaurant team member intervention, averaged 86% and we would expect the average to only to increase.","https://www.wendys.com/blog/drive-thru-innovation-wendys-freshai",true,[223],{"url":220,"title":224,"publisher":198,"date":225},"Leading Drive-Thru Innovation with Wendy's FreshAi","2023-12-11",{"level":227,"checkedAt":181},"source-verified","B","wendys-freshai-drive-thru-voice-ordering",null,{"title":232,"useCases":233,"organization":234,"vendors":236,"summary":239,"stage":207,"year":36,"channels":240,"languages":241,"metrics":242,"outcomeDisclosed":199,"sources":243,"verification":250,"grade":251,"id":252,"organizationSlug":230},"Checkers & Rally's: Presto drive through voice ordering rollout",[192],{"name":235,"anonymized":199,"country":200,"region":201,"industry":17},"Checkers Drive-In Restaurants",[237],{"name":238,"role":205},"Presto Automation","In January 2022 Checkers Drive In Restaurants, operator of the Checkers and Rally's drive through chains, selected Presto as the exclusive automated voice ordering provider for its restaurants owned by the company, and Presto's systems were scheduled to be deployed across all of them during 2022, a rollout the companies called the largest of its kind in the hospitality industry at the time. The decision followed a four month, multi location pilot in 2021 in which Presto's voice ordering completed most orders with minimal staff intervention, a figure Presto itself reported. In January 2025 the US Securities and Exchange Commission found that this same press release was one of the statements that materially misled investors, because it did not adequately disclose that the voice AI technology powering Presto Voice was owned and operated by a third party (\"Supplier A\", referenced in the release only as a partner, Hi Auto), not Presto's own technology. This record therefore no longer carries the vendor's automation figure as a metric; see `verification.note` and this use case's risk section for the SEC order.",[25],[211],[],[244,246],{"url":35,"title":34,"publisher":238,"date":245},"2022-01-10",{"url":163,"title":247,"publisher":248,"date":249},"In the Matter of Presto Automation Inc., Securities Act Release No. 11352","US Securities and Exchange Commission","2025-01-14",{"level":227,"checkedAt":181},"C","checkers-rallys-presto-drive-thru-voice-ordering",0,[255],{"kpi":44,"label":256,"unit":215,"aggregate":221,"higherIsBetter":221,"n":257,"nUpTo":253,"median":214,"min":214,"max":214,"byClaimant":258,"vendorOnly":199,"points":259},"Containment rate",1,{"organization":257,"vendor":253,"regulator":253,"independent":253},[260],{"evidenceId":229,"organization":198,"value":214,"qualifier":216,"claimant":218,"grade":228,"pooled":221},{"low":262,"high":263},1825000,18834000,[265,286,314,337],{"slug":266,"title":267,"shortTitle":268,"definition":269,"status":9,"industries":270,"functions":273,"patterns":275,"audience":26,"autonomy":27,"adoptionStage":278,"segment":279,"evidenceCount":280,"publicEvidenceCount":280,"organizations":281,"bestGrade":228,"headline":230,"lastVerified":285,"indexable":221},"proactive-outbound-engagement-agent","AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[271,272],"banking","payments",[274,20,19],"marketing",[276,22,277,23],"conversational-agent","agentic-workflow","emerging","front-office",3,[282,283,284],"Bank of America","Capital One","Commonwealth Bank of Australia","2026-09-27",{"slug":287,"title":288,"shortTitle":289,"definition":290,"status":9,"industries":291,"functions":293,"patterns":294,"audience":26,"autonomy":27,"adoptionStage":28,"segment":279,"evidenceCount":296,"publicEvidenceCount":296,"organizations":297,"bestGrade":228,"headline":307,"lastVerified":313,"indexable":221},"plan-upgrade-and-sales-assistant","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.",[292],"telecommunications",[20,19,274],[23,276,295,22,277],"rag-knowledge-assistant",9,[298,299,300,301,302,303,304,305,306],"Reliance Jio","Mobily","Orange France","Singtel","T-Mobile","Telenet","Verizon","Virgin Media O2","Vodafone",{"kpi":308,"label":309,"unit":215,"n":257,"nUpTo":253,"kind":310,"value":311,"qualifier":216,"claimant":312,"organization":303,"vendorReported":221},"conversion-rate-uplift","Conversion uplift","reported",75,"vendor","2026-09-26",{"slug":315,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":320,"patterns":321,"audience":26,"autonomy":27,"adoptionStage":28,"evidenceCount":296,"publicEvidenceCount":322,"organizations":323,"bestGrade":228,"headline":332,"lastVerified":313,"indexable":221},"travel-and-hotel-booking-concierge","AI travel and hotel booking concierge","Travel and hotel booking concierge","A customer facing AI assistant that turns an open travel question into a concrete trip by searching live inventory for flights, hotels, rentals, cruises and activities, comparing options and answering questions about the property and the booking, then completes or hands off the booking and supports the traveller with changes and questions before and during the stay.",[17],[20,19],[276,23,295,277],8,[324,325,326,327,328,329,330,331],"Airbnb","AutoCamp","Booking.com","GHT Hotels","Holland America Line","Marriott International","Priceline","Trip.com",{"kpi":333,"label":334,"unit":215,"n":280,"nUpTo":253,"kind":335,"value":336,"qualifier":216,"claimant":230,"organization":230,"vendorReported":199},"automation-rate","Automation rate","median",88,{"slug":338,"title":339,"shortTitle":340,"definition":341,"status":9,"industries":342,"functions":344,"patterns":345,"audience":26,"autonomy":27,"adoptionStage":28,"segment":346,"evidenceCount":257,"publicEvidenceCount":257,"organizations":347,"bestGrade":228,"headline":230,"lastVerified":285,"indexable":221},"conversational-insurance-quote-and-buy","Conversational AI for insurance quote and buy","Conversational quote and buy","A customer facing AI agent that sells insurance directly in a conversation: it asks the rating questions in plain language, explains cover options, returns a price from the insurer's rating engine, handles objections and takes payment to bind the policy, with a licensed human available for advice and anything outside its limits.",[343],"insurance",[20,19],[276,277,23,22],"distribution",[348],"Lemonade",{"indexable":221,"reasons":350},[],[352,356,361,369,376,383,389,396,404,411,417,423,429,435,442,449,455,462,467,473,480,487,492,497,502,509,514,519,526,531,539,546,552,558,563,568],{"id":144,"label":353,"issuer":150,"region":151,"url":152,"description":354,"useCases":355,"indexable":221},"EU AI Act","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.",250,{"id":145,"label":357,"issuer":150,"region":151,"url":358,"description":359,"useCases":360,"indexable":221},"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.",223,{"id":362,"label":363,"issuer":364,"region":365,"url":366,"description":367,"useCases":368,"indexable":221},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":370,"label":371,"issuer":372,"region":201,"url":373,"description":374,"useCases":375,"indexable":221},"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.",95,{"id":377,"label":378,"issuer":379,"region":151,"url":380,"description":381,"useCases":382,"indexable":221},"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.",73,{"id":384,"label":385,"issuer":150,"region":151,"url":386,"description":387,"useCases":388,"indexable":221},"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.",67,{"id":390,"label":391,"issuer":392,"region":151,"url":393,"description":394,"useCases":395,"indexable":221},"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":397,"label":398,"issuer":399,"region":400,"url":401,"description":402,"useCases":403,"indexable":221},"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":405,"label":406,"issuer":407,"region":400,"url":408,"description":409,"useCases":410,"indexable":221},"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":146,"label":412,"issuer":413,"region":365,"url":414,"description":415,"useCases":416,"indexable":221},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":418,"label":419,"issuer":420,"region":201,"url":421,"description":422,"useCases":416,"indexable":221},"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":424,"label":425,"issuer":150,"region":151,"url":426,"description":427,"useCases":428,"indexable":221},"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":430,"label":431,"issuer":432,"region":151,"url":433,"description":434,"useCases":428,"indexable":221},"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":436,"label":437,"issuer":438,"region":201,"url":439,"description":440,"useCases":441,"indexable":221},"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":443,"label":444,"issuer":445,"region":365,"url":446,"description":447,"useCases":448,"indexable":221},"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":450,"label":451,"issuer":150,"region":151,"url":452,"description":453,"useCases":454,"indexable":221},"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":456,"label":457,"issuer":458,"region":201,"url":459,"description":460,"useCases":461,"indexable":221},"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":463,"label":464,"issuer":150,"region":151,"url":465,"description":466,"useCases":461,"indexable":221},"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.",{"id":468,"label":469,"issuer":470,"region":201,"url":471,"description":472,"useCases":461,"indexable":221},"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":474,"label":475,"issuer":476,"region":365,"url":477,"description":478,"useCases":479,"indexable":221},"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.",12,{"id":481,"label":482,"issuer":483,"region":201,"url":484,"description":485,"useCases":486,"indexable":221},"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":488,"label":489,"issuer":150,"region":151,"url":490,"description":491,"useCases":486,"indexable":221},"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":493,"label":494,"issuer":150,"region":151,"url":495,"description":496,"useCases":486,"indexable":221},"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":498,"label":499,"issuer":150,"region":151,"url":500,"description":501,"useCases":486,"indexable":221},"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":503,"label":504,"issuer":505,"region":151,"url":506,"description":507,"useCases":508,"indexable":221},"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":510,"label":511,"issuer":399,"region":400,"url":512,"description":513,"useCases":508,"indexable":221},"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":515,"label":516,"issuer":150,"region":151,"url":517,"description":518,"useCases":508,"indexable":221},"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":520,"label":521,"issuer":522,"region":201,"url":523,"description":524,"useCases":525,"indexable":221},"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":527,"label":528,"issuer":150,"region":151,"url":529,"description":530,"useCases":525,"indexable":221},"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.",{"id":532,"label":533,"issuer":534,"region":535,"url":536,"description":537,"useCases":538,"indexable":221},"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":540,"label":541,"issuer":542,"region":151,"url":543,"description":544,"useCases":545,"indexable":221},"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.",4,{"id":547,"label":548,"issuer":549,"region":151,"url":550,"description":551,"useCases":545,"indexable":221},"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":553,"label":554,"issuer":555,"region":400,"url":556,"description":557,"useCases":280,"indexable":221},"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":559,"label":560,"issuer":150,"region":151,"url":561,"description":562,"useCases":280,"indexable":221},"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":564,"label":565,"issuer":150,"region":151,"url":566,"description":567,"useCases":280,"indexable":221},"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":569,"label":570,"issuer":571,"region":201,"url":572,"description":573,"useCases":280,"indexable":221},"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.",1790783087679]