[{"data":1,"prerenderedAt":571},["ShallowReactive",2],{"uc-hotel-revenue-management-copilot":3,"uc-regulations":358},{"useCase":4,"evidence":168,"blitsAiDeployments":237,"benchmarks":238,"indicative":248,"related":251,"indexability":356,"includeUnpublished":174},{"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":32,"valueDrivers":33,"kpis":37,"indicativeValue":42,"macroEstimates":69,"feasibility":70,"implementation":84,"risk":121,"blitsAi":147,"faq":149,"related":162,"datePublished":163,"dateModified":163,"lastVerified":163,"changelog":164,"slug":167},"AI copilot for hotel revenue management","Hotel revenue management copilot","Hotel revenue management AI copilot","An AI copilot forecasts hotel demand and prices. RIMC Hotels & Resorts Group reports a 28.44% RevPAR gain at its Polish property; Duetto reports 33% at Hôtel Swexan.","published","An employee facing AI system that forecasts demand for a hotel or portfolio by date, room type and segment, recommends or automatically adjusts room prices and availability controls within limits a revenue manager sets, and scores group and event enquiries for true profitability, so a small revenue team can run pricing that used to need daily manual adjustment in the property management system.",[12,13,14,15],"hotel revenue management AI","AI dynamic pricing for hotels","hotel demand forecasting copilot","revenue management system AI",[17],"travel-and-hospitality",[19,20],"product-and-pricing","analytics-and-reporting",[22,23],"prediction-and-scoring","agentic-workflow",[25],"internal-tools","employee-facing","supervised-agent","early-adopters","revenue-management","A hotel's price should change with demand: a room that rents cheaply on a quiet Tuesday can be\nworth much more on a night a conference fills the market. Before a revenue management system,\nthat means one person watching pickup, competitor rates and events across every room type and segment, and\nupdating prices by hand in the property management system. RIMC Hotels & Resorts Group describes\nthis before it adopted Duetto: pricing managed manually, with adjustments made through\nthe property management system, a process it calls time consuming and inefficient, especially for\npricing and capacity control.\n\nThe problem compounds with scale and complexity. Hôtel Swexan runs a 134 room luxury property\nwith more than 20 room types, eight premium suites and five food and beverage outlets, with one\nperson, Director of Revenue Jessica Schiele, responsible for the whole pricing strategy. Before an\nAI assisted revenue management system, every hour she spent pushing rates by hand was an hour not\nspent on analysis or strategy, and macro level tools such as closing the whole house or a blanket\nminimum stay were the only practical levers, because granular, segment level rules took too long\nto maintain manually.",[],"1. **Pull the data continuously.** The system reads live occupancy, rate and booking pace from the\n   property management or central reservation system, plus competitor rates and, where used,\n   group and food and beverage data.\n2. **Forecast demand.** It predicts occupancy and demand by date, room type and segment, updating\n   the forecast as new bookings and cancellations arrive.\n3. **Recommend or execute a price.** Within a floor, a ceiling and a maximum daily rate change the\n   revenue manager configures, it either recommends a rate for approval or pushes it directly to\n   the property management system.\n4. **Score group and event enquiries.** For a group or function space request, it weighs room\n   revenue, food and beverage minimums, room rental and the displacement of other business to\n   recommend a profitable quote, not just an available rate.\n5. **Flag what needs a person.** Forecast misses, a recommendation outside the configured range, a\n   new room type or segment and any group quote below the profitability threshold go to the\n   revenue manager to review or approve.",[34,35,36],"revenue-growth","employee-productivity","speed",[38,39,40,41],"revenue-uplift","forecast-accuracy","time-saved-per-task","productivity-gain",{"referenceOrg":43,"inputs":44,"formula":64,"currency":65,"period":66,"resultLabel":67,"caveat":68},"A hotel group with 20 properties and average annual room revenue of USD 3 million per property",[45,50,57],{"key":46,"label":47,"low":48,"high":48,"unit":46,"note":49},"properties","Properties in the portfolio",20,"The reference group.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"roomRevenuePerProperty","Annual room revenue per property before the change",2000000,4000000,"USD per property per year","Editorial assumption, replace with your own room revenue.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"revparUplift","RevPAR uplift attributable to AI assisted revenue management",0.03,0.1,"fraction of room revenue","Conservative against the evidence on this page. RIMC Hotels & Resorts Group reports a 28.44% RevPAR increase at its Polish property, with no period or baseline stated for that figure, and, separately, an increase in RevPAR at every hotel in its portfolio compared to the previous year since adopting Duetto in 2022. Duetto's case study reports 33% total hotel RevPAR growth at Hôtel Swexan, 2025 versus 2024, with no date given for when Hôtel Swexan itself went live on Duetto. RIMC's own account describes its pricing before Duetto as fully manual, adjusted through the property management system, with no revenue management system of any kind in place before 2022: a group already running a mature revenue management system and adding only an AI forecasting layer on top of it should expect a smaller further gain than a group's first system delivered. Neither hotel's own case study uses the words AI or machine learning about its pricing; both run Duetto's revenue management platform, and Duetto's own product page for that platform, GameChanger, describes pairing data with \"AI-driven rate recommendations\" (checked with usecases:source, 2026-09-28).","properties * roomRevenuePerProperty * revparUplift","USD","per year","Additional room revenue from AI assisted pricing","Gross room revenue uplift only. It leaves out the cost of the revenue management system and its integration, any change in distribution cost or food and beverage revenue, and the fact that part of a RevPAR gain in any single case can come from market conditions rather than the system itself.",[],{"complexity":71,"complexityNote":72,"dataPrerequisites":73,"integrations":78},"medium","The forecasting and pricing logic itself is largely the vendor's; the work is integrating the property management or central reservation system, a channel manager and a competitor rate feed cleanly enough that the forecast is trustworthy, and agreeing the floor, ceiling and approval rules before turning on any automatic pricing.",[74,75,76,77],"At least one to two years of historical occupancy, rate and booking pace by room type and segment","A competitor set and a rate shopping feed","Group, event and food and beverage data if group profitability scoring is in scope","Agreed rate floors, ceilings and maximum daily change per room type and segment",[79,80,81,82,83],"Property management system or central reservation system","Channel manager and distribution system","Competitor rate shopping tool","Point of sale or catering system for group and food and beverage profitability","Business intelligence or reporting tool for the revenue manager's dashboard",{"steps":85,"guardrails":101,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":111},[86,89,92,95,98],{"title":87,"detail":88},"Connect the data before you connect the automation","Integrate the property management system, channel manager and rate shopping feed first, and run the forecast in shadow mode against actual outcomes before any price is pushed automatically.",{"title":90,"detail":91},"Set the guardrails first","Agree the rate floor, ceiling and maximum single day change per room type and segment with the revenue manager before switching on automatic pricing, not after.",{"title":93,"detail":94},"Start with one segment or room type","Prove forecast accuracy and RevPAR impact on transient, individual bookings before extending automation to groups, suites or a new property.",{"title":96,"detail":97},"Add group and event profitability scoring once transient pricing is stable","Bring in food and beverage minimums, room rental and displacement so any team member, not just the revenue manager, can quote a group at a profitable rate.",{"title":99,"detail":100},"Review overrides every week","Look at every case where the revenue manager overrode the recommendation, and why; a pattern of overrides in the same direction means the forecast or the guardrails need adjusting.",[102,103,104],"Automatic pricing bounded by a floor, a ceiling and a maximum single day rate change, set by the revenue manager","Any recommendation outside the configured range, or for a new room type or segment, requires human approval before it goes live","Group and event quotes below the agreed profitability threshold route to a person, not straight to the quote","The revenue manager sets and periodically revisits the pricing thresholds and rules, approves anything the system flags as outside them, and owns the final call in unusual situations such as a citywide event, a competitor's distress pricing or a local disruption the forecast has not seen before.",[107,108,109,110],"Forecast accuracy against actual occupancy and pickup, by room type and segment","RevPAR, ADR and occupancy against the competitor set, before and after","Override rate: how often the revenue manager changes or rejects a recommendation, and why","Time the revenue team spends on manual pricing and monitoring tasks",[112,115,118],{"title":113,"detail":114},"A forecast blind to what has not happened before","A model trained on historical patterns misses a new event, a cancellation wave or a local disruption. Keep an anomaly alert and a fast human override path for exactly these moments.",{"title":116,"detail":117},"Automated pricing that moves against a competitor's algorithm","Automated systems in the same market can drift in either direction: repeatedly undercutting each other compresses rates for everyone, and, when several hotels lean on the same vendor's pricing recommendations, the arrangement can itself become an antitrust question. Two federal appeals courts have reached different outcomes on different shared pricing software complaints: the Ninth Circuit upheld the dismissal of a Sherman Act suit over shared Las Vegas Strip hotel pricing software in 2025, and the Third Circuit revived a similar suit over Atlantic City casino hotel pricing software in 2026, based on that complaint's own allegations (see the incidents on this page). Enforce both a rate floor and a ceiling, and review competitor reactions, not just your own pickup.",{"title":119,"detail":120},"Chasing short term pickup at the expense of the base","The system discounts aggressively on a soft looking date and displaces higher value corporate or group business that would have booked later. Weigh displacement, not only occupancy, in the recommendation.",{"euAiAct":122,"regulations":125,"guidance":127,"controls":134,"incidents":138},{"tier":123,"basis":124},"minimal","A demand forecasting and pricing tool used by hotel staff is not listed in Annex III and is not a system that decides on a natural person's access to an essential service; it prices a hotel room, not a person. It is not customer facing, so the Article 50 transparency duty for conversational AI does not apply. The AI literacy obligation on staff who use AI systems (Article 4) still applies.",[126],"eu-ai-act",[128],{"title":129,"issuer":130,"region":131,"url":132,"note":133},"Article 4, AI literacy","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Providers and deployers must take measures to ensure staff and other people who operate and use an AI system on their behalf have a sufficient level of AI literacy.",[135,136,137],"Rate floor, ceiling and maximum daily change configured per room type and segment, reviewed periodically","Every automatic price change logged with the forecast and inputs that produced it, for audit","Regular comparison of AI recommended rates against revenue manager overrides to catch systematic bias",[139,143],{"title":140,"url":141,"note":142},"Gibson v. Cendyn Group: Ninth Circuit affirms dismissal of a Las Vegas Strip hotel pricing algorithm suit","https://cdn.ca9.uscourts.gov/datastore/opinions/2025/08/15/24-3576.pdf","A Sherman Act Section 1 class action alleged that competing hotels on the Las Vegas Strip fixed room prices by all licensing Cendyn Group's algorithmic pricing software. The Ninth Circuit's summary states it is \"affirming the district court's dismissal,\" holding that several competitors independently choosing the same pricing software, followed by higher prices, does not by itself show an anticompetitive agreement. The US Department of Justice's Antitrust Division appeared as amicus curiae; the opinion does not state which side the government argued for. Decided August 15, 2025 (No. 24-3576, 9th Cir.).",{"title":144,"url":145,"note":146},"Cornish-Adebiyi v. Caesars Entertainment: Third Circuit revives an Atlantic City hotel pricing algorithm suit","https://www2.ca3.uscourts.gov/opinarch/243006p.pdf","A parallel Sherman Act Section 1 suit against Atlantic City casino hotels that used the same Cendyn Group pricing software was dismissed by the district court in 2024. On July 29, 2026 the Third Circuit reversed that dismissal and remanded, writing that it will \"reverse the District Court's dismissal of the Complaint,\" and holding that the complaint's well pleaded allegations of a hub and spoke agreement run through Cendyn's shared pricing software are sufficient to proceed (No. 24-3006, 3rd Cir.). The opinion cites Gibson v. Cendyn Group, the Ninth Circuit's ruling in the Las Vegas Strip case, without disagreeing with it: the two courts reached different outcomes on different complaints with different allegations, not opposite readings of the same rule.",{"howToBuild":148},"On Blits.ai this is an **agentic workflow**, not a conversation. It runs on a schedule, and\n**custom functions** (REST calls, SQL queries and custom code in an isolated JavaScript sandbox)\nconnect it to the property management or central reservation system, the channel manager, a\nrate shopping feed and an external forecasting or revenue management engine that produces the\ndemand forecast and a recommended rate. A **SQL knowledge base** holds the structured\noccupancy, rate and booking pace history those calls read. Within a floor and ceiling the\nrevenue manager configures, a custom function pushes the recommended rate back to the property\nmanagement system.\n\n**Human in the loop confirmation** holds any recommendation above the configured threshold, or\nfor a new room type or segment, for a person to approve or reject before it reaches the\nproperty management system. Every run is recorded in the workflow's run history with a full\naudit trail, and **test suites** run against the workflow before a change to its configuration\ngoes live. The platform is **model agnostic**, so the LLM behind the workflow's own reasoning\ncan be swapped between providers without rebuilding it, and **EU and UAE data residency** keeps\nrate and booking data in region for groups that require it.",[150,153,156,159],{"question":151,"answer":152},"How much RevPAR gain can an AI revenue management copilot deliver?","It depends heavily on the starting point. RIMC Hotels & Resorts Group reports a 28.44% RevPAR increase at its Polish property, with no period or baseline stated for that figure, and, separately, a RevPAR increase across its whole portfolio compared to the previous year since adopting Duetto in 2022; Duetto's case study reports 33% total hotel RevPAR growth at Hôtel Swexan, 2025 versus 2024. Treat both as single case study results rather than a guaranteed uplift: RIMC moved from pricing it describes as fully manual, adjusted through the property management system, with no revenue management system in place before 2022, and the Swexan case study does not state when its own Duetto deployment began relative to that comparison. Neither hotel's own case study calls its pricing AI or machine learning; both run Duetto's revenue management platform, which Duetto's own product page describes as pairing data with \"AI-driven rate recommendations\".",{"question":154,"answer":155},"Does the AI set prices on its own?","It depends on the deployment. Hôtel Swexan's case study describes automated pricing strategies that execute around the clock \"without manual intervention,\" based on occupancy thresholds and segment logic the Director of Revenue, Jessica Schiele, defines in advance, with no need to change rates by hand. The oversight in that setup is Schiele setting and revising those rules and running a daily check the case study describes as a few minutes rather than a few hours, not a person approving each price. Setting an explicit rate floor, a ceiling and a maximum daily change before switching on automatic pricing, as the implementation guidance on this page recommends, is good practice, but neither cited deployment states those specific limits publicly.",{"question":157,"answer":158},"What data does a hotel need before it can use one?","At least a year or two of historical occupancy, rate and booking pace by room type and segment, a defined competitor set with rate shopping data, and, for group profitability scoring, food and beverage and function space data.",{"question":160,"answer":161},"Is this the same as a hotel guest chatbot?","No. A revenue management copilot is an internal tool for the revenue team that sets prices; it does not talk to guests. A guest facing booking or service assistant is a separate use case.",[],"2026-09-28",[165],{"date":163,"note":166},"First published","hotel-revenue-management-copilot",[169,213],{"title":170,"useCases":171,"organization":172,"vendors":177,"summary":181,"stage":182,"year":183,"channels":184,"languages":185,"metrics":187,"outcomeDisclosed":201,"sources":202,"verification":208,"grade":210,"id":211,"organizationSlug":212},"Hotel Swexan: Duetto revenue management for a one person team",[167],{"name":173,"anonymized":174,"country":175,"region":176,"industry":17},"Hôtel Swexan",false,"US","north-america",[178],{"name":179,"role":180},"Duetto","platform","Hôtel Swexan, a 134 room luxury property in Dallas with more than 20 room types, eight premium suites and five food and beverage outlets, runs its entire revenue management function with one person, Director of Revenue Jessica Schiele. Before Duetto, the hotel relied on a remote third party consultant with no visibility into daily operations, and rate changes were slow with no room for granular, segment level pricing. Duetto now forecasts occupancy, recommends demand driven rates by room type and segment, executes automated pricing strategies around the clock based on occupancy thresholds and segment logic Schiele defines, and scores group and event enquiries for profitability (room rate, food and beverage minimums, room rental and displacement) so any team member can quote a profitable group rate. Comparing 2025 with 2024, Duetto's case study reports double digit RevPAR growth in every segment.","production",2025,[25],[186],"en",[188,197],{"kpi":38,"value":189,"unit":190,"qualifier":191,"period":192,"baseline":193,"claimant":194,"quote":195,"sourceUrl":196},33,"percent","exact","2025 versus 2024","Total hotel RevPAR, 2024","vendor","Total hotel RevPAR grew 33% year on year — a result that reflects more than favourable market conditions.","https://www.duettocloud.com/en-us/success-stories/hotel-swexan-drives-exceptional-revpar-growth-with-duetto",{"kpi":38,"value":198,"unit":190,"qualifier":191,"period":192,"baseline":199,"claimant":194,"quote":200,"sourceUrl":196},45,"Suite RevPAR, 2024","Suite RevPAR grew 45% year on year — occupancy up 29%, ADR up 13%.",true,[203,205],{"url":196,"title":204,"publisher":179},"Hôtel Swexan drives exceptional RevPAR growth with Duetto",{"url":206,"title":207,"publisher":179},"https://www.duettocloud.com/en-us/platform/gamechanger","GameChanger: Predictive Analytics Software",{"level":209,"checkedAt":163},"source-verified","C","hotel-swexan-duetto-revenue-management",null,{"title":214,"useCases":215,"organization":216,"vendors":219,"summary":221,"stage":182,"year":222,"channels":223,"languages":224,"metrics":225,"outcomeDisclosed":201,"sources":231,"verification":235,"grade":210,"id":236,"organizationSlug":212},"RIMC Hotels & Resorts Group: Duetto revenue management",[167],{"name":217,"anonymized":174,"country":218,"region":131,"industry":17},"RIMC Hotels & Resorts Group","DE",[220],{"name":179,"role":180},"RIMC Hotels & Resorts Group, a hotel association headquartered in Hamburg with business, city and holiday hotels across several countries, replaced manual pricing, made through the property management system and time consuming for pricing and capacity control, with Duetto's cloud revenue management system in 2022. Duetto forecasts demand and recommends real time, demand driven room prices across the portfolio, in place of the group's previous manual adjustments. Head of Revenue Henning Möhn describes an increase in RevPAR at every Duetto hotel in the portfolio and a 28.44 percent RevPAR increase at the group's Polish property, plus daily time saved on pricing and monitoring per hotel.",2022,[25],[],[226],{"kpi":38,"value":227,"unit":190,"qualifier":191,"claimant":228,"quote":229,"sourceUrl":230},28.44,"organization","We've seen a 28.44% increase in RevPAR for our Polish property and an overall RevPAR increase across our portfolio.","https://www.duettocloud.com/en-us/success-stories/duetto-drives-28-increase-in-revpar-for-rimc-hotels-resorts-group",[232,234],{"url":230,"title":233,"publisher":179},"28% increase in RevPAR for RIMC Hotels & Resorts Group",{"url":206,"title":207,"publisher":179},{"level":209,"checkedAt":163},"rimc-hotels-resorts-duetto-revenue-management",0,[239],{"kpi":38,"label":240,"unit":190,"aggregate":201,"higherIsBetter":201,"n":241,"nUpTo":237,"median":242,"min":227,"max":189,"byClaimant":243,"vendorOnly":174,"points":245},"Revenue uplift",2,30.72,{"organization":244,"vendor":244,"regulator":237,"independent":237},1,[246,247],{"evidenceId":211,"organization":173,"value":189,"qualifier":191,"claimant":194,"grade":210,"pooled":201},{"evidenceId":236,"organization":217,"value":227,"qualifier":191,"claimant":228,"grade":210,"pooled":201},{"low":249,"high":250},1200000,8000000,[252,279,304,332],{"slug":253,"title":254,"shortTitle":255,"definition":256,"status":9,"industries":257,"functions":259,"patterns":261,"audience":26,"autonomy":264,"adoptionStage":28,"segment":265,"evidenceCount":266,"publicEvidenceCount":266,"organizations":267,"bestGrade":273,"headline":274,"lastVerified":278,"indexable":201},"insurance-pricing-and-actuarial-copilot","AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[258],"insurance",[19,260,20],"risk-management",[22,262,23,263],"code-generation","summarization","copilot","pricing",5,[268,269,270,271,272],"Accelerant Holdings","Europ Assistance","Generali France","Kinsale Capital Group","MAIF","B",{"kpi":41,"label":275,"unit":276,"n":244,"nUpTo":237,"kind":277,"value":266,"qualifier":191,"claimant":228,"organization":270,"vendorReported":174},"Productivity gain","multiplier","reported","2026-09-26",{"slug":280,"title":281,"shortTitle":282,"definition":283,"status":9,"industries":284,"functions":288,"patterns":290,"audience":26,"autonomy":264,"adoptionStage":28,"segment":293,"evidenceCount":266,"publicEvidenceCount":294,"organizations":295,"bestGrade":273,"headline":300,"lastVerified":303,"indexable":201},"deal-sourcing-and-due-diligence-assistant","AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","An AI assistant that screens the market for acquisition or investment targets, builds company profiles, and speeds up due diligence by reading data room documents, extracting key terms and risks and drafting the investment or diligence memo, for the deal team to verify and decide.",[285,286,287],"capital-markets","wealth-and-asset-management","professional-services",[20,289,260],"legal",[291,263,292,23,22],"document-processing","rag-knowledge-assistant","front-office",4,[296,297,298,299],"Datasite","EQT","Freshfields","Rogo",{"kpi":41,"label":275,"unit":190,"n":237,"nUpTo":244,"kind":277,"value":301,"qualifier":302,"claimant":194,"organization":296,"vendorReported":201},80,"up-to","2026-09-27",{"slug":305,"title":306,"shortTitle":307,"definition":308,"status":9,"industries":309,"functions":315,"patterns":318,"audience":26,"autonomy":321,"adoptionStage":28,"segment":322,"evidenceCount":266,"publicEvidenceCount":266,"organizations":323,"bestGrade":273,"headline":329,"lastVerified":303,"indexable":201},"treasury-cash-flow-forecasting","AI cash flow forecasting for corporate treasury","Treasury cash forecasting","Machine learning and conversational analytics, offered by some banks inside their cash management platforms, that categorise a company's cash flows, forecast positions across accounts and currencies, and answer treasurers' questions in plain language, so the treasury team decides on funding and idle balances with better information and less spreadsheet work.",[310,311,312,313,314],"banking","cross-industry","logistics-and-transportation","retail-and-ecommerce","manufacturing",[316,317,20],"treasury","finance-and-accounting",[22,319,320,23],"classification-and-routing","conversational-agent","assist","specialized-businesses",[324,325,326,327,328],"Amtrak","Bank of America","Domino's Pizza","JPMorgan Chase","Prysmian",{"kpi":41,"label":275,"unit":190,"n":241,"nUpTo":244,"kind":277,"value":330,"qualifier":331,"claimant":228,"organization":327,"vendorReported":174},90,"approximately",{"slug":333,"title":334,"shortTitle":335,"definition":336,"status":9,"industries":337,"functions":339,"patterns":341,"audience":26,"autonomy":27,"adoptionStage":28,"segment":344,"evidenceCount":266,"publicEvidenceCount":266,"organizations":345,"bestGrade":273,"headline":351,"lastVerified":303,"indexable":201},"network-planning-and-capacity-optimization","AI for mobile network planning and capacity optimization","Network planning and capacity","Machine learning that forecasts where and when a mobile network will run out of capacity, recommends where to add cells, spectrum or hardware, and continuously tunes radio parameters so existing capacity carries more traffic, with planners approving investments and major changes.",[338],"telecommunications",[340,20],"network-operations",[22,342,343,23],"recommendation-and-personalization","anomaly-detection","network",[346,347,348,349,350],"Deutsche Telekom","NTT DOCOMO","stc Group","Telefónica España","Vodafone",{"kpi":352,"label":353,"unit":190,"n":244,"nUpTo":237,"kind":277,"value":354,"qualifier":355,"claimant":228,"organization":346,"vendorReported":174},"processing-time-reduction","Cycle time reduction",95,"at-least",{"indexable":201,"reasons":357},[],[359,363,369,377,384,390,397,404,412,419,425,431,438,445,451,456,463,469,475,481,487,493,499,504,509,516,523,528,534,541,547,553,560,565],{"id":126,"label":360,"issuer":130,"region":131,"url":132,"description":361,"useCases":362,"indexable":201},"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.",197,{"id":364,"label":365,"issuer":130,"region":131,"url":366,"description":367,"useCases":368,"indexable":201},"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.",180,{"id":370,"label":371,"issuer":372,"region":373,"url":374,"description":375,"useCases":376,"indexable":201},"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":378,"label":379,"issuer":380,"region":176,"url":381,"description":382,"useCases":383,"indexable":201},"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":385,"label":386,"issuer":130,"region":131,"url":387,"description":388,"useCases":389,"indexable":201},"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":391,"label":392,"issuer":393,"region":131,"url":394,"description":395,"useCases":396,"indexable":201},"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":398,"label":399,"issuer":400,"region":131,"url":401,"description":402,"useCases":403,"indexable":201},"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":405,"label":406,"issuer":407,"region":408,"url":409,"description":410,"useCases":411,"indexable":201},"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":413,"label":414,"issuer":415,"region":408,"url":416,"description":417,"useCases":418,"indexable":201},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":420,"label":421,"issuer":422,"region":373,"url":423,"description":424,"useCases":48,"indexable":201},"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":426,"label":427,"issuer":428,"region":176,"url":429,"description":430,"useCases":48,"indexable":201},"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":432,"label":433,"issuer":434,"region":131,"url":435,"description":436,"useCases":437,"indexable":201},"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":439,"label":440,"issuer":441,"region":373,"url":442,"description":443,"useCases":444,"indexable":201},"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":446,"label":447,"issuer":130,"region":131,"url":448,"description":449,"useCases":450,"indexable":201},"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":452,"label":453,"issuer":130,"region":131,"url":454,"description":455,"useCases":450,"indexable":201},"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":457,"label":458,"issuer":459,"region":176,"url":460,"description":461,"useCases":462,"indexable":201},"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":464,"label":465,"issuer":130,"region":131,"url":466,"description":467,"useCases":468,"indexable":201},"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":470,"label":471,"issuer":472,"region":176,"url":473,"description":474,"useCases":468,"indexable":201},"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":476,"label":477,"issuer":478,"region":373,"url":479,"description":480,"useCases":468,"indexable":201},"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":482,"label":483,"issuer":130,"region":131,"url":484,"description":485,"useCases":486,"indexable":201},"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.",11,{"id":488,"label":489,"issuer":490,"region":176,"url":491,"description":492,"useCases":486,"indexable":201},"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":494,"label":495,"issuer":407,"region":408,"url":496,"description":497,"useCases":498,"indexable":201},"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":500,"label":501,"issuer":130,"region":131,"url":502,"description":503,"useCases":498,"indexable":201},"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":505,"label":506,"issuer":130,"region":131,"url":507,"description":508,"useCases":498,"indexable":201},"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":510,"label":511,"issuer":512,"region":131,"url":513,"description":514,"useCases":515,"indexable":201},"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.",9,{"id":517,"label":518,"issuer":519,"region":176,"url":520,"description":521,"useCases":522,"indexable":201},"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":524,"label":525,"issuer":130,"region":131,"url":526,"description":527,"useCases":522,"indexable":201},"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":529,"label":530,"issuer":130,"region":131,"url":531,"description":532,"useCases":533,"indexable":201},"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":535,"label":536,"issuer":537,"region":538,"url":539,"description":540,"useCases":266,"indexable":201},"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":542,"label":543,"issuer":544,"region":131,"url":545,"description":546,"useCases":294,"indexable":201},"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":548,"label":549,"issuer":550,"region":131,"url":551,"description":552,"useCases":294,"indexable":201},"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":554,"label":555,"issuer":556,"region":408,"url":557,"description":558,"useCases":559,"indexable":201},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":561,"label":562,"issuer":130,"region":131,"url":563,"description":564,"useCases":559,"indexable":201},"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":566,"label":567,"issuer":568,"region":176,"url":569,"description":570,"useCases":559,"indexable":201},"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.",1790598297921]