[{"data":1,"prerenderedAt":543},["ShallowReactive",2],{"uc-hospital-bed-and-staff-capacity-command-center":3,"uc-regulations":334},{"useCase":4,"evidence":172,"blitsAiDeployments":244,"benchmarks":245,"indicative":254,"related":257,"indexability":332,"includeUnpublished":178},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":39,"indicativeValue":41,"macroEstimates":74,"feasibility":75,"implementation":88,"risk":122,"blitsAi":151,"faq":153,"related":166,"datePublished":167,"dateModified":167,"lastVerified":167,"changelog":168,"slug":171},"AI command center for hospital bed and staff capacity planning","Hospital capacity command center","Hospital bed capacity command center AI","An AI command center predicts patient flow and bed assignments. Hopkins raised bed use from 85% to about 94%; Humber cut the average ED wait for a bed by 34%.","published","An AI powered operations center that predicts patient admissions, discharges and transfers across a hospital or health system, and helps a team of coordinators sitting in one room sequence real time bed assignments, staffing levels and patient moves, so patients get into the right bed faster and existing capacity is used fully without adding beds.",[12,13,14,15],"hospital command center","capacity command center","patient flow AI","hospital operations center",[17],"healthcare",[19,20],"operations","analytics-and-reporting",[22,23,24],"prediction-and-scoring","classification-and-routing","anomaly-detection",[26],"internal-tools","employee-facing","assist","early-adopters","hospital operations","Johns Hopkins Hospital, like many hospitals, faced backlogs at 85% bed utilization before it\nbuilt its command center, and small delays cascade into big ones. A patient\nwaits on a gurney in the emergency department because no ward bed is ready; an operating room holds\na finished case because there is nowhere to send the patient; a referring physician sends a\ntransfer request elsewhere because nobody can say quickly whether a bed exists. Before a command\ncenter, the staff who track admissions, discharges, transport and cleaning are scattered across the\nbuilding, working from pen and paper, whiteboards and markers.\n\nThe result is that decisions can lag reality by hours: a bed that becomes available at 9am, for\nexample, might not appear on anyone's list until the afternoon huddle. Humber River Hospital's own\nstaff put the case for building a command center plainly: \"we soon realized we were going to\nexceed our new capacity by 2020\" and set out to improve capacity \"without seeking government\nfunding to expand a hospital we had just barely opened.\" Johns Hopkins Medicine reports having\n\"essentially opened 16 beds on a daily basis\" without building a new wing or adding new staff,\naccording to Jim Scheulen, its chief administrative officer for emergency medicine and capacity\nmanagement.",[],"1. **Bring the data into one model.** Live feeds from the electronic health record's admission,\n   discharge and transfer stream, the bed management system, the operating room schedule and\n   ambulance dispatch are combined into a single, continuously updated picture of the hospital.\n2. **Predict.** Machine learning models forecast occupancy by unit for the next shift, day and week,\n   and estimate which patients are likely to be discharged soon.\n3. **Prioritize and recommend.** The system flags situations that need attention, such as an\n   emergency department patient waiting past target or a unit nearing capacity, and suggests the\n   next action: which bed to assign, which porter to send, which transfer request to accept.\n4. **Coordinate in one room.** Staff from admitting, patient transport, environmental services and\n   the referral line sit together, watch the same shared displays, and act on the recommendations as\n   they appear, instead of each working from a different, stale version of the truth.\n5. **Learn and rebalance.** Actual outcomes feed back into the forecasting models, and alert\n   thresholds are retuned as the hospital's patient mix, seasonal demand and physical capacity\n   change.",[35,36,37,38],"cost-to-serve","speed","employee-productivity","customer-experience",[40],"processing-time-reduction",{"referenceOrg":42,"inputs":43,"formula":69,"currency":70,"period":71,"resultLabel":72,"caveat":73},"A 600 bed academic hospital",[44,49,56,63],{"key":45,"label":46,"low":47,"high":47,"unit":45,"note":48},"beds","Licensed beds",600,"The reference hospital.",{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"freedBedShare","Share of licensed beds freed through better patient flow",0.01,0.014,"fraction of beds","The high end matches Johns Hopkins' own reported benchmark after five years of tuning: the equivalent of 16 beds a day, about 1.4% of its 1,162 licensed beds. Humber River Health's own site reports a larger first year result, the equivalent of 35 beds against its roughly 688 bed footprint, about 5%, but that single first year figure is not used as the cap here since it is not yet a multi year benchmark. The low end is conservative against both.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"costPerBedDay","Fully loaded cost avoided per bed day of freed capacity",1500,2500,"USD per bed day","Editorial assumption for a US academic hospital's marginal cost of a staffed bed day; replace with your own.",{"key":64,"label":65,"low":66,"high":66,"unit":67,"note":68},"daysPerYear","Days per year",365,"days","Calendar year.","beds * freedBedShare * costPerBedDay * daysPerYear","USD","per year","Annual value of capacity freed without adding beds","Gross capacity value only. It leaves out the cost of building and staffing the command center itself, the software licence, and any change in case mix or payer rate that comes with treating more patients in the freed capacity.",[],{"complexity":76,"complexityNote":77,"dataPrerequisites":78,"integrations":83},"high","The technology is rarely the hard part. Integrating live feeds from the EHR, bed management, OR scheduling and ambulance dispatch into one data model, and getting departments that have never shared a dashboard to work from the same numbers in one room, is a multi year operating model change, not a software rollout.",[79,80,81,82],"Real time admission, discharge and transfer feed from the electronic health record","Bed and unit status from the bed management and environmental services systems","Operating room schedule and case status","Ambulance dispatch and inter hospital transfer request data",[84,85,86,87],"Electronic health record (admission, discharge, transfer feed)","Bed management and environmental services systems","Operating room scheduling system","Ambulance dispatch and inter hospital transfer systems",{"steps":89,"guardrails":105,"humanInTheLoop":109,"kpisToInstrument":110,"failureModes":115},[90,93,96,99,102],{"title":91,"detail":92},"Time the current patient journey before buying anything","Measure how long it actually takes today from an admit decision to a bed assignment, and from a finished OR case to a transfer, by unit and shift. This baseline is what proves the value later and tells you which bottleneck to attack first.",{"title":94,"detail":95},"Build one shared data model","Connect the EHR's ADT feed, the bed board, the OR schedule and transport systems into a single live view before adding any predictive model on top of it.",{"title":97,"detail":98},"Predict, then prioritize, one alert at a time","Start with a next shift occupancy forecast and a single at risk alert (for example, an emergency department patient waiting past target), prove it changes behaviour, then add more.",{"title":100,"detail":101},"Put every department in one room, physically or virtually","Admitting, transport, environmental services and the referral line need to see the same numbers at the same time and be empowered to act on them without escalating every decision.",{"title":103,"detail":104},"Set targets and instrument every one before scaling to more units","Agree a target for each metric (time to bed assignment, transfer acceptance rate, discharge before noon) with the unit that owns it, and only widen to more units once the first one holds.",[106,107,108],"Every bed assignment and transfer decision stays with a named clinical or administrative owner; the AI recommends, it does not assign","Escalation rules for clinically urgent transfers (stroke, trauma) bypass queue based recommendations and go straight to the relevant team","Forecast accuracy is checked against actual admissions and discharges on a regular schedule, and alert thresholds are retuned when it drifts","Coordinators in the command center act on every recommendation; nothing moves a patient, assigns a bed or accepts a transfer without a person confirming it. Unit and department leaders review forecast accuracy and override patterns regularly, and any new alert type or automated recommendation is approved before it goes live.",[111,112,113,114],"Time from admit decision to bed assignment, by unit and shift","Transfer delay from the operating room after a procedure","Ambulance and inter hospital transfer acceptance rate and decline reasons","Forecast accuracy against actual admissions and discharges",[116,119],{"title":117,"detail":118},"Optimizing the room, not the ward","Staff in the command center chase a dashboard metric that looks good centrally but does not reflect what a specific unit is experiencing. Review metrics with the units that own the work, not only centrally.",{"title":120,"detail":121},"Alert fatigue","Too many predictive alerts, or alerts that are frequently wrong, and staff start ignoring all of them, including the ones that matter. Track false alarm rate per alert type and retire or retune alerts that staff routinely dismiss.",{"euAiAct":123,"regulations":126,"guidance":132,"controls":145,"incidents":150},{"tier":124,"basis":125},"context-dependent","The tier depends on what the system is scoped to do. A design limited to occupancy and discharge forecasting and to sequencing bed assignments for patients already admitted is operational decision support for hospital logistics, outside Annex III. Annex III point 5(d) covers AI used \"to dispatch, or to establish priority in the dispatching of, emergency first response services\", including medical aid and emergency healthcare patient triage systems. On a plain reading, that point can apply when a system dispatches, or sets the priority of dispatching, ambulance or critical care transport itself (work similar to what the Johns Hopkins center's Lifeline transport staff do for helicopter and ambulance transfers), or when it assesses the clinical urgency of an emergency patient, that is, triage. Sequencing which already admitted ED patient gets the next ward bed, and deciding whether to accept an inter hospital transfer request on capacity grounds, are not listed activities under 5(d) as written; whether either counts as dispatching or triage in a given deployment is a case by case legal question, not a settled fact, and should be assessed with counsel before relying on this tier. For public hospitals, Annex III point 5(a) (access to essential public services, including healthcare) can also be relevant. Scoping the system to bed sequencing and transfer acceptance only, and keeping every ambulance dispatch and ED triage decision with clinical staff outside the AI's recommendation, is what keeps a deployment in the lower tier.",[127,128,129,130,131],"eu-ai-act","gdpr","hipaa","nist-ai-rmf","iso-42001",[133,139],{"title":134,"issuer":135,"region":136,"url":137,"note":138},"Article 6: Classification Rules for High-Risk AI Systems","Future of Life Institute","europe","https://artificialintelligenceact.eu/article/6/","Paragraph 1a addresses one route to high risk status, an AI system that is itself a safety component of a regulated product (the Annex I route): it says a system used solely for non safety related user assistance, performance optimization, service efficiency or convenience does not qualify as such a safety component. It does not decide whether a system falls under an Annex III listed use case, which is the separate route assessed above for point 5(d). A logistics and staffing tool for hospital operations is unlikely to be a product safety component either way, but the Annex III analysis above is the one that matters here.",{"title":140,"issuer":141,"region":142,"url":143,"note":144},"AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","A framework hospitals can use to map, measure and manage the risk of predictive capacity tools, including the risk that a forecast gets treated as a decision rather than a recommendation.",[146,147,148,149],"Named accountable owner for every bed assignment and transfer decision; the AI recommends only","Escalation path for clinically urgent cases that bypasses queue based recommendations","Regular comparison of forecast accuracy against actual admissions, discharges and transfers","Access logging and data governance for the shared ADT and EHR feed powering the command center",[],{"howToBuild":152},"On Blits.ai, the shared operating picture is built with an **agentic workflow** whose **custom\nfunctions** call the hospital's EHR, bed management and OR scheduling systems as REST or SQL\nintegrations, combined with a **SQL knowledge base** (PostgreSQL or SQLite) that lets the\ncoordination team query occupancy, transfer and staffing data in plain language instead of a\nfixed dashboard; a database that is not already PostgreSQL or SQLite needs a replica in one of\nthose engines first. An **AI\nagent** turns each system's status into a plain language brief and a recommended next action for\nthe shift, while a **flow** with deterministic steps encodes the escalation rules for clinically\nurgent transfers, so time critical cases are never left to a recommendation alone.\n\nThe workflow's **human in the loop approval** step, when configured to require it, keeps every\nbed and transfer decision with a named owner: nothing writes back to the hospital's systems\nwithout a person confirming it. The\noccupancy and discharge forecasts themselves come from the hospital's own or a third party's\nforecasting models, called through the custom functions, not from Blits.ai; **monitors** then run\nscheduled recurring health checks on the agent and workflow themselves, with an alert if a check\nfails or recovers, and **analytics** give the team a per bot dashboard of interactions, recognition\nrate and satisfaction for the coordination team's own use of the agent. The platform is **model\nagnostic**, so the model behind the agent's briefings can change without rebuilding the\nintegrations, and **EU data residency** keeps patient flow data in region for European health\nsystems.",[154,157,160,163],{"question":155,"answer":156},"What is a hospital capacity command center?","A control room, physical or virtual, where staff from admitting, transport, environmental services and referral intake work from one live, AI predicted view of every bed, patient and transfer in the hospital, instead of tracking patient flow by phone and whiteboard from separate departments.",{"question":158,"answer":159},"How much capacity can a command center free without adding beds?","Johns Hopkins Medicine reports opening the equivalent of 16 beds a day and improving bed utilization from 85% to about 94%, a figure reported after five years of tuning. Humber River Health reports a larger result in its first year alone, the equivalent of 35 beds. A first deployment can move faster than expected, as Humber's did, but plan the first year target from the mature, multi year benchmark rather than assume a first year result as large as Humber's.",{"question":161,"answer":162},"Does the AI decide which patient gets a bed?","No. The system forecasts occupancy and recommends an assignment or action; a person in the command center makes and confirms every bed assignment and transfer decision.",{"question":164,"answer":165},"Is this the same as clinical triage software?","No. A capacity command center manages beds, staff and patient flow across the hospital. Software that reads a medical image to flag an urgent clinical finding, such as an AI radiology worklist triage tool, is a different, separately regulated category of AI.",[],"2026-09-28",[169],{"date":167,"note":170},"First published","hospital-bed-and-staff-capacity-command-center",[173,220],{"title":174,"useCases":175,"organization":176,"vendors":180,"summary":184,"stage":185,"year":186,"channels":187,"languages":188,"metrics":190,"outcomeDisclosed":202,"sources":203,"verification":215,"grade":217,"id":218,"organizationSlug":219},"Humber River Health: Command Centre outcomes",[171],{"name":177,"anonymized":178,"country":179,"region":142,"industry":17},"Humber River Health",false,"CA",[181],{"name":182,"role":183},"GE Healthcare","platform","Humber River Hospital, in Toronto, opened Canada's first hospital command centre in November 2017, built in collaboration with GE Healthcare Partners. The NASA style control room combines live data from more than 600 connected patient rooms with predictive analytics and machine learning to prioritize risk, predict spikes in emergency visits and coordinate bed turnaround, portering and diagnostics across the hospital. Different departments monitor customizable analytic tiles on a shared wall of displays, and the hospital has since extended the program from operational functions (Generation 1) to clinical alerting (Generation 2), and was moving forward with virtual care and home monitoring (Generation 3) as of 2022.","scaled",2017,[26],[189],"en",[191,199],{"kpi":40,"value":192,"unit":193,"qualifier":194,"period":195,"claimant":196,"quote":197,"sourceUrl":198},34,"percent","exact","since implementation, reported 2022","organization","Humber also saw a decrease in wait times for inpatient diagnostics and emergency rooms, with a 34 per cent reduction in the average time a patient in the emergency department waited before being placed in a bed.","https://www.hrh.ca/2022/07/28/humber-river-hospitals-command-centre-and-generation-3/",{"kpi":40,"value":200,"unit":193,"qualifier":194,"period":195,"claimant":196,"quote":201,"sourceUrl":198},45,"In addition, Humber had a 45 per cent decrease in the time to clean inpatient beds with accurate bed planning.",true,[204,207,212],{"url":198,"title":205,"publisher":177,"date":206},"Humber River Health's Command Centre – Outcomes and Generation 3","2022-07-28",{"url":208,"title":209,"publisher":210,"date":211},"http://www.newswire.ca/news-releases/humber-river-hospital-breaking-new-ground-with-the-opening-of-canadas-first-hospital-command-centre-660975993.html","Humber River Hospital Breaking New Ground with the Opening of Canada's First Hospital Command Centre","Humber River Hospital","2017-11-30",{"url":213,"title":214,"publisher":177},"https://www.humbercommandcentre.ca","Humber River Health Command Centre",{"level":216,"checkedAt":167},"source-verified","B","humber-river-health-command-centre",null,{"title":221,"useCases":222,"organization":223,"vendors":226,"summary":228,"stage":185,"year":229,"channels":230,"languages":231,"metrics":232,"outcomeDisclosed":202,"sources":238,"verification":242,"grade":217,"id":243,"organizationSlug":219},"Johns Hopkins Medicine: Judy Reitz Capacity Command Center",[171],{"name":224,"anonymized":178,"country":225,"region":142,"industry":17},"Johns Hopkins Medicine","US",[227],{"name":182,"role":183},"Johns Hopkins Medicine and GE Healthcare built the Judy Reitz Capacity Command Center, opened in January 2016, where staff control bed assignments for all patients within The Johns Hopkins Hospital and also manage transfers to and from four Johns Hopkins Medicine member hospitals, from one control room. Staff from admitting, transport and referral intake sit together, watching software that predicts patient volumes by shift, day and week, one screen that forecasts bed occupancy rates by department, and another that shows incoming patient transfers, in place of the pen and paper, whiteboards and markers used before. The organization also reports a reduction in transfer delays out of the operating room after a procedure. More than 20 other institutions, including Duke Health and Yale New Haven Health, have since built similar centers after visiting.",2016,[26],[189],[233],{"kpi":40,"value":234,"unit":193,"qualifier":194,"period":235,"claimant":196,"quote":236,"sourceUrl":237},38,"reported at the center's fifth anniversary, 2021","A patient is assigned a bed 38% faster (or 3.5 hours faster) after a decision is made to admit him or her from the emergency department.","https://www.hopkinsmedicine.org/news/articles/2021/03/capacity-command-center-celebrates-5-years-of-improving-patient-safety-access",[239],{"url":237,"title":240,"publisher":224,"archivedUrl":241},"Capacity Command Center Celebrates 5 Years of Improving Patient Safety, Access","https://web.archive.org/web/2026/https://www.hopkinsmedicine.org/news/articles/2021/03/capacity-command-center-celebrates-5-years-of-improving-patient-safety-access",{"level":216,"checkedAt":167},"johns-hopkins-capacity-command-center",0,[246],{"kpi":40,"label":247,"unit":193,"aggregate":202,"higherIsBetter":202,"n":248,"nUpTo":244,"median":249,"min":192,"max":234,"byClaimant":250,"vendorOnly":178,"points":251},"Cycle time reduction",2,36,{"organization":248,"vendor":244,"regulator":244,"independent":244},[252,253],{"evidenceId":243,"organization":224,"value":234,"qualifier":194,"claimant":196,"grade":217,"pooled":202},{"evidenceId":218,"organization":177,"value":192,"qualifier":194,"claimant":196,"grade":217,"pooled":202},{"low":255,"high":256},3285000,7665000,[258,273,295,312],{"slug":259,"title":260,"shortTitle":261,"definition":262,"status":9,"industries":263,"functions":265,"patterns":266,"audience":267,"autonomy":28,"adoptionStage":29,"segment":268,"evidenceCount":248,"publicEvidenceCount":248,"organizations":269,"bestGrade":272,"headline":219,"lastVerified":167,"indexable":202},"smart-meter-analytics","AI analytics for smart meter and AMI data","Smart meter analytics","AI that turns the flood of readings from smart electricity, gas and water meters into usable information: it monitors meter and network health at scale, estimates which appliances drive a household's usage from the meter signal alone, flags unusual consumption, and targets efficiency and electrification programmes at the customers who will benefit most, instead of a utility treating every meter and every customer the same way.",[264],"energy-and-utilities",[19,20],[24,22],"back-office","metering-and-billing",[270,271],"Consolidated Edison (Con Edison)","Southern California Gas Company (SoCalGas)","C",{"slug":274,"title":275,"shortTitle":276,"definition":277,"status":9,"industries":278,"functions":280,"patterns":281,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":283,"publicEvidenceCount":283,"organizations":284,"bestGrade":217,"headline":288,"lastVerified":294,"indexable":202},"clinical-trial-patient-matching","AI clinical trial patient matching and prescreening","Clinical trial patient matching","AI that reads structured data and clinical notes in the health record, compares each patient with the inclusion and exclusion criteria of open clinical trials, and gives research staff and treating clinicians a ranked list of likely eligible patients with the evidence for each criterion, so that people confirm eligibility and invite the patient.",[17,279],"pharma-and-life-sciences",[19,20],[282,23],"document-processing",3,[285,286,287],"Cleveland Clinic","Mount Sinai Health System","Yale Cancer Center",{"kpi":289,"label":290,"unit":193,"n":291,"nUpTo":244,"kind":292,"value":293,"qualifier":194,"claimant":196,"organization":285,"vendorReported":178},"accuracy","Accuracy",1,"reported",100,"2026-09-27",{"slug":296,"title":297,"shortTitle":298,"definition":299,"status":9,"industries":300,"functions":302,"patterns":303,"audience":267,"autonomy":304,"adoptionStage":305,"evidenceCount":306,"publicEvidenceCount":306,"organizations":307,"bestGrade":217,"headline":219,"lastVerified":294,"indexable":202},"retail-demand-forecasting-and-replenishment","AI demand forecasting and automated replenishment for retail","Demand forecasting and replenishment","Machine learning that forecasts demand for every item in every store or fulfillment center, day by day, from sales history, promotions, prices, weather and local events, and turns the forecast into automatic store and warehouse orders within limits set by planners, who handle the exceptions.",[301],"retail-and-ecommerce",[19,20],[22,24],"supervised-agent","mainstream",4,[308,309,310,311],"Albert Heijn","Morrisons","One Stop","Walmart",{"slug":313,"title":314,"shortTitle":315,"definition":316,"status":9,"industries":317,"functions":320,"patterns":322,"audience":267,"autonomy":325,"adoptionStage":326,"segment":327,"evidenceCount":306,"publicEvidenceCount":283,"organizations":328,"bestGrade":217,"headline":219,"lastVerified":294,"indexable":202},"portfolio-drift-monitoring-and-rebalancing","AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[318,319],"wealth-and-asset-management","banking",[19,321,20],"risk-management",[24,323,22,324],"agentic-workflow","content-generation","copilot","emerging","middle-office",[329,330,331],"Morgan Stanley","SimCorp","Vanguard",{"indexable":202,"reasons":333},[],[335,341,346,353,357,363,370,377,384,391,398,404,411,418,424,429,436,442,447,453,459,465,471,476,481,488,495,500,506,514,520,526,532,537],{"id":127,"label":336,"issuer":337,"region":136,"url":338,"description":339,"useCases":340,"indexable":202},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":128,"label":342,"issuer":337,"region":136,"url":343,"description":344,"useCases":345,"indexable":202},"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":131,"label":347,"issuer":348,"region":349,"url":350,"description":351,"useCases":352,"indexable":202},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":130,"label":354,"issuer":141,"region":142,"url":143,"description":355,"useCases":356,"indexable":202},"NIST AI Risk Management Framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":358,"label":359,"issuer":337,"region":136,"url":360,"description":361,"useCases":362,"indexable":202},"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":364,"label":365,"issuer":366,"region":136,"url":367,"description":368,"useCases":369,"indexable":202},"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":371,"label":372,"issuer":373,"region":136,"url":374,"description":375,"useCases":376,"indexable":202},"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":378,"label":379,"issuer":380,"region":381,"url":382,"description":383,"useCases":249,"indexable":202},"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.",{"id":385,"label":386,"issuer":387,"region":381,"url":388,"description":389,"useCases":390,"indexable":202},"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":392,"label":393,"issuer":394,"region":349,"url":395,"description":396,"useCases":397,"indexable":202},"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":399,"label":400,"issuer":401,"region":142,"url":402,"description":403,"useCases":397,"indexable":202},"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":405,"label":406,"issuer":407,"region":136,"url":408,"description":409,"useCases":410,"indexable":202},"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":412,"label":413,"issuer":414,"region":349,"url":415,"description":416,"useCases":417,"indexable":202},"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":419,"label":420,"issuer":337,"region":136,"url":421,"description":422,"useCases":423,"indexable":202},"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":425,"label":426,"issuer":337,"region":136,"url":427,"description":428,"useCases":423,"indexable":202},"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":430,"label":431,"issuer":432,"region":142,"url":433,"description":434,"useCases":435,"indexable":202},"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":437,"label":438,"issuer":337,"region":136,"url":439,"description":440,"useCases":441,"indexable":202},"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":129,"label":443,"issuer":444,"region":142,"url":445,"description":446,"useCases":441,"indexable":202},"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":448,"label":449,"issuer":450,"region":349,"url":451,"description":452,"useCases":441,"indexable":202},"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":454,"label":455,"issuer":337,"region":136,"url":456,"description":457,"useCases":458,"indexable":202},"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":460,"label":461,"issuer":462,"region":142,"url":463,"description":464,"useCases":458,"indexable":202},"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":466,"label":467,"issuer":380,"region":381,"url":468,"description":469,"useCases":470,"indexable":202},"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":472,"label":473,"issuer":337,"region":136,"url":474,"description":475,"useCases":470,"indexable":202},"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":477,"label":478,"issuer":337,"region":136,"url":479,"description":480,"useCases":470,"indexable":202},"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":482,"label":483,"issuer":484,"region":136,"url":485,"description":486,"useCases":487,"indexable":202},"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":489,"label":490,"issuer":491,"region":142,"url":492,"description":493,"useCases":494,"indexable":202},"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":496,"label":497,"issuer":337,"region":136,"url":498,"description":499,"useCases":494,"indexable":202},"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":501,"label":502,"issuer":337,"region":136,"url":503,"description":504,"useCases":505,"indexable":202},"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":507,"label":508,"issuer":509,"region":510,"url":511,"description":512,"useCases":513,"indexable":202},"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":515,"label":516,"issuer":517,"region":136,"url":518,"description":519,"useCases":306,"indexable":202},"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":521,"label":522,"issuer":523,"region":136,"url":524,"description":525,"useCases":306,"indexable":202},"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":527,"label":528,"issuer":529,"region":381,"url":530,"description":531,"useCases":283,"indexable":202},"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":533,"label":534,"issuer":337,"region":136,"url":535,"description":536,"useCases":283,"indexable":202},"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":538,"label":539,"issuer":540,"region":142,"url":541,"description":542,"useCases":283,"indexable":202},"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.",1790598297811]