[{"data":1,"prerenderedAt":528},["ShallowReactive",2],{"uc-building-energy-optimization":3,"uc-regulations":313},{"useCase":4,"evidence":149,"blitsAiDeployments":216,"benchmarks":217,"indicative":232,"related":235,"indexability":311,"includeUnpublished":155},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":21,"channels":23,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":40,"indicativeValue":43,"macroEstimates":72,"feasibility":73,"implementation":84,"risk":117,"blitsAi":128,"faq":130,"related":143,"datePublished":144,"dateModified":144,"lastVerified":144,"changelog":145,"slug":148},"AI for building HVAC and energy optimization","Building energy optimization","AI HVAC optimization for building energy","BrainBox AI reports a 15.8% cut in HVAC related electricity at Cammeby's International and 10% HVAC energy savings at Loyola's Schreiber Center.","published","AI that continuously predicts a building's heating, cooling and ventilation needs and adjusts setpoints, equipment sequencing and start times in real time through the existing building management system, instead of following fixed schedules, so the building uses less energy without a person retuning it by hand.",[12,13,14,15],"autonomous HVAC optimization","AI building energy management","smart building energy control","automated emissions reduction",[17,18],"real-estate","cross-industry",[20],"operations",[22],"prediction-and-scoring",[24,25],"internal-tools","api","back-office","autonomous","early-adopters","hvac-optimization","Space heating is the single largest energy end use in US commercial buildings, at about 32% of\ntotal energy use in 2018, with ventilation adding roughly another 10% (US EIA). A building that\nruns this equipment on fixed schedules and setpoints does not adapt as occupancy, weather and\nelectricity prices change hour to hour. According to BrainBox AI, rising energy costs and\nregulations led Cammeby's International, a real estate investment company, to look at AI for its\n32 storey office property in New York City's financial district.\n\nFacility teams can tune a building management system by hand, but re tuning every zone\ncontinuously as conditions change takes staff time. Without that continuous attention, a building\ncan end up running wider, more conservative margins than a continuously optimized building would\nneed, and can miss the chance to shift load to the hours when the local electricity grid is\ncleanest.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"Space heating was about 32% of total US commercial building energy use in 2018, the largest single end use; ventilation and lighting were next at about 10% each (US EIA).","Use of energy in commercial buildings","https://www.eia.gov/energyexplained/use-of-energy/commercial-buildings.php",2018,"1. **Connect to the existing building management system.** An edge device or cloud connection\n   reads live data points, such as temperatures, valve positions and equipment status, typically\n   over the BACnet protocol, without replacing hardware.\n2. **Learn the building's thermal behaviour.** The AI models how each zone responds to outside\n   weather, occupancy and equipment changes, specific to that building rather than a generic\n   template.\n3. **Predict and act ahead of time.** The system forecasts the next hours of demand and adjusts\n   valve positions, fan speeds and equipment sequencing continuously, cooling or heating the\n   building before the need arrives.\n4. **Shift load where a grid signal is available.** With a marginal emissions or price signal, the\n   AI pre cools the building during the hours when the grid is cleanest, then lets the\n   temperature drift during the hours when the grid relies more on fossil fuels, so the\n   building's thermal mass carries the load instead of the equipment.\n5. **Report through the existing tools.** Facility engineers watch the AI's decisions through\n   customised graphics inside their own building management system, rather than a separate\n   interface, and can adjust or override a setpoint if needed.",[39],"compliance",[41,42],"energy-savings","cost-savings",{"referenceOrg":44,"inputs":45,"formula":67,"currency":68,"period":69,"resultLabel":70,"caveat":71},"An office building with 300,000 square feet of AI controlled HVAC",[46,53,60],{"key":47,"label":48,"low":49,"high":50,"unit":51,"note":52},"squareFeet","Square feet under AI control",150000,500000,"square feet","Editorial assumption. Cammeby's International's controlled space was 251,104 square feet (BrainBox AI), within this range.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"hvacCostPerSqFt","HVAC energy cost per square foot per year",0.75,1.5,"USD per square foot per year","Editorial assumption. BrainBox AI's Cammeby's International figures ($42,951 saved from a 15.8% reduction in HVAC related electricity, across 251,104 square feet over an 11 month period) imply a total HVAC electricity cost of about $271,800 over that period ($42,951 / 0.158), or roughly $1.08 per square foot over the 11 months and about $1.18 per square foot annualised, assuming the dollar saving is proportional to the 15.8% consumption reduction; replace with your own utility spend.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"savingsShare","Share of HVAC energy cost saved",0.05,0.1,"fraction of HVAC energy cost","At or below the two reported results on this page (a 15.8% reduction in HVAC related electricity consumption at Cammeby's International and a 10% savings in HVAC related energy at Loyola University's Schreiber Center, both BrainBox AI), to allow for a different building's baseline and scope of HVAC energy cost.","squareFeet * hvacCostPerSqFt * savingsShare","USD","per year","Annual HVAC energy cost avoided","HVAC energy cost avoided only. It leaves out the software subscription, any emissions credit or compliance benefit, and the risk that an aggressive deployment without a hard comfort band produces occupant complaints.",[],{"complexity":74,"complexityNote":75,"dataPrerequisites":76,"integrations":80},"medium","The AI needs a working connection to the building's existing controls, commonly BACnet, and a controls contractor's cooperation to expose the right points. In a typical rollout, most of the effort is integration and change management with the building's engineering team, rather than the AI modelling itself.",[77,78,79],"Live read and write access to the building management system's HVAC data points","A record of the HVAC equipment and how zones map to it","At least a few weeks of historical operating data to calibrate the thermal model",[81,82,83],"Building management system, typically over BACnet or an equivalent protocol","Weather forecast data","Optionally, a grid emissions or price signal for load shifting",{"steps":85,"guardrails":101,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":110},[86,89,92,95,98],{"title":87,"detail":88},"Start with the highest value system","At Cammeby's International, the building's own team set out to target savings particularly in its chilled water loop, according to BrainBox AI's case study. Naming the equipment that carries the most load up front gives an early, defensible result to point to, even though the rollout itself went floor by floor across all the connected equipment, air handling units, variable air valves, fans and both the hot and chilled water systems, rather than one system at a time.",{"title":90,"detail":91},"Work with the controls contractor, not around them","Program the AI's decisions into graphics the facility team already uses inside its own building management system. At Cammeby's International, BrainBox AI rolled out floor by floor in close cooperation with the building's Chief Engineer, so daily operations were not disrupted.",{"title":93,"detail":94},"Measure a real baseline first","Establish a measured baseline period before claiming a saving, so the figure holds up against normal seasonal swings. BrainBox AI reported Cammeby's International's 15.8% reduction over an 11 month results period in 2023, without disclosing how the baseline itself was built.",{"title":96,"detail":97},"Add a grid or price signal once the basics work","Once core optimization is stable, a marginal emissions or price signal, as Loyola University's Schreiber Center used with WattTime, lets the building shift load to cleaner or cheaper hours by pre cooling ahead of time and drifting during the dirtier hours.",{"title":99,"detail":100},"Keep occupant comfort as a hard constraint","Set temperature and ventilation bands the AI may not cross regardless of the savings opportunity, and track comfort complaints alongside the energy numbers.",[102,103,104],"Hard temperature and ventilation bands the AI cannot exceed, enforced in the building management system itself, not only in the AI's own logic","No changes to life safety, fire, or code required ventilation and pressure sequences","A facility engineer can see and override every AI decision through the existing building management system graphics","Facility engineers watch the AI's live decisions through their own building management system graphics and can override any setpoint, and a reliability or sustainability team reviews measured savings against the baseline on a schedule.",[107,108,109],"HVAC related energy consumption and cost, measured against a comparable prior period rather than a single year over year comparison","Occupant comfort complaints during and after rollout","Emissions avoided when a grid signal drives load shifting",[111,114],{"title":112,"detail":113},"Savings measured against the wrong baseline","Weather and occupancy swing year to year; a saving claimed against a single prior year, rather than a normalised baseline, will not survive scrutiny.",{"title":115,"detail":116},"Comfort complaints erase trust in the program","Aggressive pre cooling or setpoint drift without a hard comfort band produces complaints that get the whole program switched off; keep comfort bands non negotiable from day one.",{"euAiAct":118,"regulations":121,"guidance":123,"controls":124,"incidents":127},{"tier":119,"basis":120},"minimal","Optimizing HVAC equipment is not listed in Annex III. Point 2 covers AI safety components in the management and operation of critical infrastructure such as the supply of water, gas, heating or electricity, and a building's own HVAC controller does not manage infrastructure at that level. The system also does not decide credit, employment, access to essential services or another high risk use. Under Article 6(1), an AI system that is a safety component of a product covered by Annex I harmonisation legislation, such as the Machinery Regulation, and that needs third party conformity assessment, would be high risk regardless of Annex III; this is why the guardrails on this page keep the AI out of fire, life safety and code required ventilation sequences rather than letting it override them.",[122],"eu-ai-act",[],[125,126],"A documented safe operating envelope, covering temperature, humidity and ventilation rate, that the AI cannot leave, enforced independently of the AI's own decisions","A logged history of every setpoint or sequence change the AI made, so a disputed comfort complaint or an unusual energy month can be traced to a specific decision",[],{"howToBuild":129},"The HVAC optimization model itself, the part that predicts a building's thermal response and\nwrites setpoints back to the building management system, runs on the building automation\nvendor's own control platform; Blits.ai is not a building management system. What fits on\nBlits.ai is the layer facility teams use to understand and govern it: an SQL knowledge base over\nthe building's point data and energy logs lets an agent answer a facility manager's question\nabout why a zone ran outside its normal range or what changed overnight, in plain language\ninstead of a raw trend graph.\n\nAgentic tasks can watch for a condition, such as a zone repeatedly hitting its comfort band\nlimit, or query an SQL knowledge base on a schedule to compare the optimization model's own\npredictions against actual consumption, and draft a ticket for the facility engineer with the\nrelevant data attached, with human in the loop confirmation before anything escalates to the\ncontrols contractor. Monitors run scheduled checks that the facility agent still answers\ncorrectly, and the platform's EU and UAE data residency options suit a portfolio that spans\nregions.",[131,134,137,140],{"question":132,"answer":133},"How much energy does AI HVAC optimization actually save?","BrainBox AI reports an 11 month measured 15.8% reduction in HVAC related electricity consumption at Cammeby's International's office tower in Manhattan, saving $42,951 and avoiding 37.14 tonnes of CO2 equivalent. At Loyola University's Schreiber Center in Chicago, a year long project found a 10% savings in HVAC related energy and 10% lower HVAC related CO2e emissions. Results vary by building age, climate and how the baseline is measured.",{"question":135,"answer":136},"Does it need new hardware?","Both deployments on this page connected to the building's existing management system over BACnet, but Cammeby's International needed a new edge device: BrainBox AI's case study says the solution was deployed through its own edge device, communicating over BACnet IP. At Loyola University's Schreiber Center, BrainBox AI says it integrated its system with the building's existing HVAC controls via BACnet, with no edge device mentioned.",{"question":138,"answer":139},"Can it help a building use more renewable energy?","Indirectly. Loyola University's Schreiber Center paired HVAC optimization with WattTime's marginal emissions signal to shift some cooling to the hours when the local grid relies more on renewables. BrainBox AI reports a 15% average reduction in HVAC emissions during marginal emissions events, including the following four hour drift period.",{"question":141,"answer":142},"Is this high risk under the EU AI Act?","Usually not. HVAC optimization is not listed in Annex III's high risk categories, including point 2 on critical infrastructure for water, gas, heating or electricity supply, since a building's own HVAC controller does not manage infrastructure at that level, and it is not a safety component of a product under Annex I harmonisation legislation such as the Machinery Regulation. If it did perform that kind of safety function, Article 6(1) could make it high risk regardless of Annex III, which is why a well designed deployment keeps it out of fire, life safety and code required ventilation sequences.",[],"2026-09-28",[146],{"date":144,"note":147},"First published","building-energy-optimization",[150,190],{"title":151,"useCases":152,"organization":153,"vendors":159,"summary":165,"stage":166,"year":167,"channels":168,"languages":169,"metrics":171,"outcomeDisclosed":180,"sources":181,"verification":185,"grade":187,"id":188,"organizationSlug":189},"Loyola University's Schreiber Center: AI HVAC optimization and automated emissions reduction",[148],{"name":154,"anonymized":155,"country":156,"region":157,"industry":158},"Loyola University Chicago",false,"US","north-america","education",[160,163],{"name":161,"role":162},"BrainBox AI","platform",{"name":164,"role":162},"WattTime","Loyola University's Schreiber Center, a LEED Gold certified, 10 storey mixed use building that houses Loyola's Quinlan School of Business in Chicago, ran a year long proof of concept combining BrainBox AI's HVAC optimization with WattTime's Automated Emissions Reduction signal, which pre cools the building during low emissions events, when the local grid has surplus renewable energy, and lets it drift when the grid relies more on fossil fuels. The project was carried out with UC Berkeley's Center for the Built Environment, and BrainBox AI points to a fuller study in an ASHRAE guide on the role of grid interactivity in decarbonization.","pilot",2024,[24],[170],"en",[172],{"kpi":41,"value":173,"unit":174,"qualifier":175,"period":176,"claimant":177,"quote":178,"sourceUrl":179},10,"percent","exact","over the year long proof of concept","vendor","we were able to achieve a 10% savings in HVAC-related energy and a 10% reduction in HVAC-related CO2e emissions through our AI for HVAC solution","https://brainboxai.com/en/case-studies/leveraging-ai-and-aer-for-sustainable-excellence-loyola-universitys-schreiber-center",true,[182],{"url":179,"title":183,"publisher":161,"archivedUrl":184},"Leveraging AI and AER for Sustainable Excellence: Loyola University's Schreiber Center","https://web.archive.org/web/20240514171126/https://brainboxai.com/en/case-studies/leveraging-ai-and-aer-for-sustainable-excellence-loyola-universitys-schreiber-center",{"level":186,"checkedAt":144},"source-verified","C","loyola-university-schreiber-center-brainbox-ai",null,{"title":191,"useCases":192,"organization":193,"vendors":195,"summary":197,"stage":198,"year":199,"channels":200,"languages":201,"metrics":202,"outcomeDisclosed":180,"sources":211,"verification":214,"grade":187,"id":215,"organizationSlug":189},"Cammeby's International: AI HVAC optimization with BrainBox AI",[148],{"name":194,"anonymized":155,"country":156,"region":157,"industry":17},"Cammeby's International",[196],{"name":161,"role":162},"Cammeby's International, a real estate investment company, deployed BrainBox AI's AI Control solution across a 32 storey, 386,315 square foot office building in New York City's financial district, built in 1983, controlling 251,104 square feet of air handling units, variable air valves, outdoor and exhaust fans, and the hot and chilled water systems. Over an 11 month period in 2023, the deployment cut HVAC related electricity consumption, cost and emissions without disrupting daily operations, according to the building's facility engineers.","production",2023,[24],[170],[203,208],{"kpi":41,"value":204,"unit":174,"qualifier":175,"period":205,"claimant":177,"quote":206,"sourceUrl":207},15.8,"over an 11 month period in 2023","Our AI Control solution drove a 15.8% reduction in HVAC-related electricity consumption, saving $42,951, and mitigating 37.14 tCO2eq.","https://brainboxai.com/en/case-studies/cammebys-achieves-15.8-reduction-in-hvac-energy-use-and-costs",{"kpi":42,"value":209,"unit":210,"currency":68,"qualifier":175,"period":205,"claimant":177,"quote":206,"sourceUrl":207},42951,"currency",[212],{"url":207,"title":213,"publisher":161},"Cammeby's International achieves 15.8% reduction in HVAC energy use and costs",{"level":186,"checkedAt":144},"cammebys-international-brainbox-ai-hvac",0,[218,226],{"kpi":41,"label":219,"unit":174,"aggregate":180,"higherIsBetter":180,"n":220,"nUpTo":216,"median":221,"min":173,"max":204,"byClaimant":222,"vendorOnly":180,"points":223},"Energy savings",2,12.9,{"organization":216,"vendor":220,"regulator":216,"independent":216},[224,225],{"evidenceId":215,"organization":194,"value":204,"qualifier":175,"claimant":177,"grade":187,"pooled":180},{"evidenceId":188,"organization":154,"value":173,"qualifier":175,"claimant":177,"grade":187,"pooled":180},{"kpi":42,"label":227,"unit":210,"currency":68,"aggregate":155,"higherIsBetter":180,"n":228,"nUpTo":216,"median":209,"min":209,"max":209,"byClaimant":229,"vendorOnly":180,"points":230},"Cost savings",1,{"organization":216,"vendor":228,"regulator":216,"independent":216},[231],{"evidenceId":215,"organization":194,"value":209,"qualifier":175,"claimant":177,"grade":187,"pooled":180},{"low":233,"high":234},5625,75000,[236,261,281,296],{"slug":237,"title":238,"shortTitle":239,"definition":240,"status":9,"industries":241,"functions":244,"patterns":246,"audience":250,"autonomy":251,"adoptionStage":28,"evidenceCount":252,"publicEvidenceCount":253,"organizations":254,"bestGrade":259,"headline":189,"lastVerified":260,"indexable":180},"outbound-reminder-and-confirmation-agent","AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[18,242,243],"healthcare","government",[245,20],"customer-service",[247,248,249,22],"voice-agent","conversational-agent","agentic-workflow","customer-facing","supervised-agent",5,4,[255,256,257,258],"Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","U.S. Department of Veterans Affairs","WellSpan Health","B","2026-09-26",{"slug":262,"title":263,"shortTitle":264,"definition":265,"status":9,"industries":266,"functions":268,"patterns":271,"audience":273,"autonomy":251,"adoptionStage":274,"segment":275,"evidenceCount":276,"publicEvidenceCount":276,"organizations":277,"bestGrade":259,"headline":189,"lastVerified":144,"indexable":180},"property-valuation-support","AI support for property valuation and appraisal","Property valuation support","AI, most often an automated valuation model, that estimates a property's market value from comparable sales, property characteristics and location data, and either offers to replace a full appraisal within set limits or gives a professional valuer a first pass estimate, the closest comparable sales and a reliability score, so the valuer's time goes to the properties that need a person's judgment.",[17,267,243],"banking",[269,270,20],"lending-and-credit","case-management",[22,272],"classification-and-routing","employee-facing","mainstream","lending",3,[278,279,280],"Fannie Mae","Riverside County Assessor-County Clerk-Recorder","Valuation Office Agency",{"slug":282,"title":283,"shortTitle":284,"definition":285,"status":9,"industries":286,"functions":287,"patterns":289,"audience":250,"autonomy":251,"adoptionStage":28,"evidenceCount":276,"publicEvidenceCount":276,"organizations":291,"bestGrade":259,"headline":189,"lastVerified":295,"indexable":180},"apartment-leasing-and-resident-service-agent","AI agent for apartment leasing inquiries and resident service","Leasing and resident service agent","An AI agent that answers rental prospects and residents by chat, text, email and phone for a property manager: it answers questions about apartments and policies, books tours, takes maintenance requests, sends renewal and payment reminders, and hands anything that needs judgment to leasing or service staff.",[17],[245,288,20],"sales",[248,247,249,290],"rag-knowledge-assistant",[292,293,294],"Asset Living","AvalonBay Communities","Equity Residential","2026-09-27",{"slug":297,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":303,"patterns":305,"audience":273,"autonomy":251,"adoptionStage":28,"segment":307,"evidenceCount":276,"publicEvidenceCount":220,"organizations":308,"bestGrade":187,"headline":189,"lastVerified":295,"indexable":180},"fraud-alert-triage","AI agent for fraud alert triage","Fraud alert triage","An AI agent that works the fraud alert queue behind the scenes as the analyst's first pass, without contacting the customer: it enriches each alert with customer, device and payment context, closes clear false positives under documented rules, merges duplicates, and routes genuine risk to an analyst with a drafted rationale.",[267,302],"payments",[304,20],"fraud-prevention",[249,272,306,22],"summarization","middle-office",[309,310],"Coast","SEB",{"indexable":180,"reasons":312},[],[314,321,327,335,342,348,355,362,370,377,384,390,397,404,410,415,422,428,434,440,446,452,457,462,467,474,481,486,492,499,505,511,517,522],{"id":122,"label":315,"issuer":316,"region":317,"url":318,"description":319,"useCases":320,"indexable":180},"EU AI Act","European Union","europe","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":322,"label":323,"issuer":316,"region":317,"url":324,"description":325,"useCases":326,"indexable":180},"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":328,"label":329,"issuer":330,"region":331,"url":332,"description":333,"useCases":334,"indexable":180},"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":336,"label":337,"issuer":338,"region":157,"url":339,"description":340,"useCases":341,"indexable":180},"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":343,"label":344,"issuer":316,"region":317,"url":345,"description":346,"useCases":347,"indexable":180},"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":349,"label":350,"issuer":351,"region":317,"url":352,"description":353,"useCases":354,"indexable":180},"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":356,"label":357,"issuer":358,"region":317,"url":359,"description":360,"useCases":361,"indexable":180},"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":363,"label":364,"issuer":365,"region":366,"url":367,"description":368,"useCases":369,"indexable":180},"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":371,"label":372,"issuer":373,"region":366,"url":374,"description":375,"useCases":376,"indexable":180},"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":378,"label":379,"issuer":380,"region":331,"url":381,"description":382,"useCases":383,"indexable":180},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":385,"label":386,"issuer":387,"region":157,"url":388,"description":389,"useCases":383,"indexable":180},"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":391,"label":392,"issuer":393,"region":317,"url":394,"description":395,"useCases":396,"indexable":180},"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":398,"label":399,"issuer":400,"region":331,"url":401,"description":402,"useCases":403,"indexable":180},"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":405,"label":406,"issuer":316,"region":317,"url":407,"description":408,"useCases":409,"indexable":180},"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":411,"label":412,"issuer":316,"region":317,"url":413,"description":414,"useCases":409,"indexable":180},"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":416,"label":417,"issuer":418,"region":157,"url":419,"description":420,"useCases":421,"indexable":180},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":423,"label":424,"issuer":316,"region":317,"url":425,"description":426,"useCases":427,"indexable":180},"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":429,"label":430,"issuer":431,"region":157,"url":432,"description":433,"useCases":427,"indexable":180},"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":435,"label":436,"issuer":437,"region":331,"url":438,"description":439,"useCases":427,"indexable":180},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":441,"label":442,"issuer":316,"region":317,"url":443,"description":444,"useCases":445,"indexable":180},"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":447,"label":448,"issuer":449,"region":157,"url":450,"description":451,"useCases":445,"indexable":180},"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":453,"label":454,"issuer":365,"region":366,"url":455,"description":456,"useCases":173,"indexable":180},"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":458,"label":459,"issuer":316,"region":317,"url":460,"description":461,"useCases":173,"indexable":180},"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":463,"label":464,"issuer":316,"region":317,"url":465,"description":466,"useCases":173,"indexable":180},"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":468,"label":469,"issuer":470,"region":317,"url":471,"description":472,"useCases":473,"indexable":180},"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":475,"label":476,"issuer":477,"region":157,"url":478,"description":479,"useCases":480,"indexable":180},"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":482,"label":483,"issuer":316,"region":317,"url":484,"description":485,"useCases":480,"indexable":180},"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":487,"label":488,"issuer":316,"region":317,"url":489,"description":490,"useCases":491,"indexable":180},"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":493,"label":494,"issuer":495,"region":496,"url":497,"description":498,"useCases":252,"indexable":180},"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":500,"label":501,"issuer":502,"region":317,"url":503,"description":504,"useCases":253,"indexable":180},"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":506,"label":507,"issuer":508,"region":317,"url":509,"description":510,"useCases":253,"indexable":180},"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":512,"label":513,"issuer":514,"region":366,"url":515,"description":516,"useCases":276,"indexable":180},"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":518,"label":519,"issuer":316,"region":317,"url":520,"description":521,"useCases":276,"indexable":180},"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":523,"label":524,"issuer":525,"region":157,"url":526,"description":527,"useCases":276,"indexable":180},"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.",1790598299273]