[{"data":1,"prerenderedAt":502},["ShallowReactive",2],{"uc-sepsis-and-deterioration-early-warning":3,"uc-regulations":275},{"useCase":4,"evidence":133,"blitsAiDeployments":181,"benchmarks":182,"indicative":162,"related":183,"indexability":273,"includeUnpublished":139},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":41,"macroEstimates":44,"feasibility":45,"implementation":56,"risk":93,"blitsAi":112,"faq":114,"related":127,"datePublished":128,"dateModified":128,"lastVerified":128,"changelog":129,"slug":132},"AI early warning for sepsis and in hospital clinical deterioration","Sepsis and deterioration early warning","AI sepsis and deterioration early warning","AI early warning flags sepsis early. A Johns Hopkins study found patients 20% less likely to die; Kaiser Permanente estimates 520 deaths a year prevented.","published","AI that continuously scans a hospitalized patient's vital signs, laboratory results, orders and clinical notes to flag early signs of sepsis or general clinical deterioration, often hours before it would otherwise be noticed, and alerts a nurse or rapid response team to assess the patient. The system only alerts; it never orders a test or a treatment itself.",[12,13,14,15,16],"sepsis early warning system","clinical deterioration prediction","AI rapid response trigger","early warning score automation","predictive deterioration monitoring",[18],"healthcare",[20],"operations",[22,23,24],"prediction-and-scoring","anomaly-detection","classification-and-routing",[26],"internal-tools","employee-facing","assist","mainstream","Sepsis and general clinical deterioration are both common and hard to catch early. Johns Hopkins\nreports that about 1.7 million adults develop sepsis every year in the United States and that more\nthan 250,000 of them die. Symptoms such as fever and confusion overlap with many other conditions,\ndeterioration can develop over hours rather than minutes, and a nurse or physician watching one\npatient at a time can miss the pattern across dozens of scattered vital sign and lab readings.\nTraditional early warning scores, computed by hand from a handful of vital signs at intervals,\ncatch some of this, but multivariate models that continuously read the full electronic health\nrecord can add signal a periodic score would miss, if they are validated on the hospital's own\npopulation first.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"About 1.7 million adults develop sepsis every year in the United States and more than 250,000 of them die; under the current standard of care, sepsis kills 30 percent of the people who develop it.","Sepsis detection AI has the potential to prevent thousands of deaths","https://hub.jhu.edu/2022/07/21/artificial-intelligence-sepsis-detection/",2022,"1. **Continuous scanning.** The model reads vital signs, lab results, nursing notes and orders from\n   the electronic health record, refreshed hourly or more often, for every admitted patient rather\n   than only those already flagged as high risk.\n2. **Risk scoring.** It calculates a probability of impending sepsis or deterioration and compares it\n   against a threshold tuned to the hospital's own patient population.\n3. **Alerting the right person.** When the score crosses the threshold, it alerts a bedside nurse\n   directly or, in a centralized model such as Kaiser Permanente's, a remote monitoring team who\n   reviews the case before contacting the local care team.\n4. **Bedside confirmation and action.** A clinician assesses the patient at the bedside and decides\n   on tests, antibiotics or escalation; the model suggests but never orders treatment itself.\n5. **Feedback and retuning.** Alerts that turn out to be false alarms are reviewed and used to retune\n   the threshold, because too many false alerts cause staff to stop trusting the system.",[39,40],"risk-reduction","speed",[42,43],"detection-rate-improvement","false-positive-reduction",[],{"complexity":46,"complexityNote":47,"dataPrerequisites":48,"integrations":52},"high","This is not a document assistant: it must read structured vitals, labs, orders and notes from the electronic health record in close to real time, integrate with a nurse call or paging workflow, and be validated and retuned locally, because a model built on one hospital's population and staffing pattern does not transfer cleanly to another, as external validations of other sepsis models have shown.",[49,50,51],"Streaming or hourly vital signs, laboratory results, orders and notes for every admitted patient","A clinically confirmed record of who actually developed sepsis or deteriorated, to validate and retune the model on the hospital's own population","A staffed response pathway, such as a bedside nurse, a rapid response team or a remote monitoring team, that can act on an alert within minutes",[53,54,55],"Electronic health record, for vitals, labs, notes and orders","Nurse call, paging or secure messaging system to deliver the alert","Rapid response or code team workflow for escalation",{"steps":57,"guardrails":73,"humanInTheLoop":77,"kpisToInstrument":78,"failureModes":83},[58,61,64,67,70],{"title":59,"detail":60},"Validate locally before going live","Test the model's predictions against your own hospital's historical outcomes before relying on it, because performance built on one population and one care model does not transfer automatically to another.",{"title":62,"detail":63},"Route to a named role, not just a chart","Decide exactly who receives the alert, such as a bedside nurse, a centralized monitoring team, or both, and what they must do within a set number of minutes.",{"title":65,"detail":66},"Set and retune the alert threshold","Start conservative, measure the false alert rate in practice, and adjust it; too many false alerts cause staff to ignore even the true ones.",{"title":68,"detail":69},"Build the bedside response into the workflow","An alert is only useful if it leads to a defined clinical action, such as a sepsis bundle or a rapid response call; write that pathway down and train staff on it before go live.",{"title":71,"detail":72},"Track outcomes, not only alerts","Measure what happened to alerted patients compared with similar patients who were not alerted, not just how many alerts fired.",[74,75,76],"The system only alerts; it never orders a test, a medication or a transfer itself","A clinician always assesses the patient at the bedside before any treatment decision is made","Alert thresholds are validated on the hospital's own population before go live and retuned after any major change in patient mix","A nurse or physician always evaluates the patient before any action is taken. The model's role is to shorten the time between a patient beginning to deteriorate and a clinician noticing, not to replace the clinician's judgment.",[79,80,81,82],"Time from alert to bedside assessment","False alert rate and the share of alerts staff act on","Sepsis or deterioration related mortality and ICU transfers, before and after, on a comparable population","Time to antibiotics or the relevant treatment bundle after an alert, versus without one",[84,87,90],{"title":85,"detail":86},"Alert fatigue","Too many low value alerts and staff start ignoring all of them, including the true ones. Track the false alert rate, retune the threshold, and keep the number of alerts a clinician gets per shift to a workable level.",{"title":88,"detail":89},"A model that does not transfer","A model tuned on one hospital's population and staffing pattern can perform far worse at another. An external validation of a different, widely deployed proprietary sepsis model found it caught only a third of true sepsis cases at one academic medical center; revalidate locally before go live and after any major change in patient mix.",{"title":91,"detail":92},"Alerts without an owner","An alert nobody is accountable for acting on is worse than no alert. Name the role that must respond and the maximum time to respond, and track it.",{"euAiAct":94,"regulations":96,"guidance":101,"controls":102,"incidents":107},{"tier":46,"basis":95},"Article 6(1)(a) and (b) and Annex I: a system is high risk when it is a safety component of, or itself is, a product covered by EU harmonisation legislation listed in Annex I, and that product is required to undergo a third party conformity assessment under that legislation. Software that predicts sepsis or deterioration to guide treatment typically qualifies as a Class IIa or higher medical device under the EU Medical Device Regulation, Rule 11, which brings it into the high risk tier, though MDR Article 5(5) provides an in house exemption that can apply to a tool a health institution builds and uses only within its own organization.",[97,98,99,100],"eu-ai-act","gdpr","hipaa","nist-ai-rmf",[],[103,104,105,106],"Local validation of model performance against the hospital's own outcomes before go live and after major changes","A named clinical role responsible for every alert, with a maximum response time","Ongoing monitoring of the false alert rate and the clinician response rate","Change control and revalidation when the patient population or care model changes materially",[108],{"title":109,"url":110,"note":111},"External validation at the University of Michigan found a different, widely deployed proprietary sepsis model missed most true cases","https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307","A study of 38,455 hospitalizations found the Epic Sepsis Model, a different product from the deployments described on this page, had a sensitivity of only 33% and a positive predictive value of 12%, and that its predictive performance (AUC of 0.63) was well below the range reported internally by its developer (0.76 to 0.83). It illustrates why a sepsis or deterioration model built on one hospital's population may not transfer to another. Both deployments on this page were developed on their own health system's patient data by its own researchers, rather than adopted as an off the shelf product.",{"howToBuild":113},"This use case sits outside a conversational agent, and the clinical risk score itself has to come\nfrom the hospital's own validated model, not from an LLM. On Blits.ai it is best built as a\n**custom function** that calls that model on a schedule or via an API token trigger, feeding an\n**agentic workflow with human in the loop approval** that turns each score into an alert for a\nperson to act on, never an automated clinical action. A **tool execution policy** limits what the\nworkflow can do to that single alerting action, never placing an order or messaging the patient,\nand the workflow's **run history and audit trail** keep a full log of every alert and the approval\nor rejection that followed it.\n\nBecause the risk model is the hospital's own, validated on its own population, **test suites**\nshould replay historical cases from that hospital before any change to the workflow goes live, and\nthe platform's **model agnostic routing** lets a health system swap the underlying LLM used\nelsewhere in the workflow without rebuilding the alerting logic around it.",[115,118,121,124],{"question":116,"answer":117},"How much earlier does AI detect sepsis than standard care?","Johns Hopkins reports that its Targeted Real-Time Early Warning System, TREWS, detected the most severe sepsis cases an average of nearly six hours earlier than the prior standard of care, in a study of 590,000 patients treated by more than 4,000 clinicians at five hospitals.",{"question":119,"answer":120},"Do these systems replace clinical judgment?","No. Every deployment described here only alerts; a nurse or physician still assesses the patient at the bedside and decides on treatment. Kaiser Permanente's Advance Alert Monitor routes alerts to a virtual nursing team that contacts the bedside team, who make the clinical call.",{"question":122,"answer":123},"Can an early warning model be trusted straight out of the box?","No. An external validation of a different, widely used proprietary sepsis model at the University of Michigan found it caught only a third of true sepsis cases and had a positive predictive value of just 12%. Both deployments described here were developed on their own health system's patient data by its own researchers, not bought and deployed as an off the shelf product.",{"question":125,"answer":126},"What is the evidence that these systems save lives?","Kaiser Permanente Northern California's physician researchers estimate that its Advance Alert Monitor program prevents an average of 520 deaths a year over a three and a half year study period, published in the New England Journal of Medicine. Johns Hopkins reports that patients were 20% less likely to die of sepsis because of TREWS, in a study of 590,000 patients treated by more than 4,000 clinicians at five hospitals.",[],"2026-09-29",[130],{"date":128,"note":131},"First published","sepsis-and-deterioration-early-warning",[134,163],{"title":135,"useCases":136,"organization":137,"vendors":142,"summary":146,"stage":147,"year":36,"channels":148,"languages":149,"metrics":151,"outcomeDisclosed":152,"sources":153,"verification":158,"grade":160,"id":161,"organizationSlug":162},"Johns Hopkins: TREWS sepsis early warning system",[132],{"name":138,"anonymized":139,"country":140,"region":141,"industry":18},"Johns Hopkins Medicine",false,"US","north-america",[143],{"name":144,"role":145},"Bayesian Health","platform","Johns Hopkins developed the Targeted Real-Time Early Warning System, TREWS, which combines a hospitalized patient's medical history with current symptoms and lab results, scouring medical records and clinical notes to flag sepsis risk. Bayesian Health, a company spun off from Johns Hopkins, led and managed the deployment across five hospitals. Over a two year study of 590,000 patients treated by more than 4,000 clinicians, published in Nature Medicine and Nature Digital Medicine in 2022, the system detected the most severe sepsis cases an average of nearly six hours earlier than the prior standard of care, and researchers found patients were 20 percent less likely to die of sepsis when the system was used.","scaled",[26],[150],"en",[],true,[154],{"url":35,"title":34,"publisher":155,"date":156,"archivedUrl":157},"Johns Hopkins University, The Hub","2022-07-21","https://web.archive.org/web/2026/https://hub.jhu.edu/2022/07/21/artificial-intelligence-sepsis-detection/",{"level":159,"checkedAt":128},"source-verified","B","johns-hopkins-trews-sepsis-early-warning",null,{"title":164,"useCases":165,"organization":166,"vendors":168,"summary":169,"stage":147,"year":36,"channels":170,"languages":171,"metrics":172,"outcomeDisclosed":152,"sources":173,"verification":179,"grade":160,"id":180,"organizationSlug":162},"Kaiser Permanente Northern California: Advance Alert Monitor early warning system",[132],{"name":167,"anonymized":139,"country":140,"region":141,"industry":18},"Kaiser Permanente Northern California",[],"Kaiser Permanente Northern California built the Advance Alert Monitor, AAM, a predictive model developed by its own Division of Research that scans almost 100 elements from the electronic health record hourly for patients in medical and surgical units and transitional care units across its 21 Northern California hospitals, giving clinicians a 12 hour lead time before clinical deterioration. A specialized team of Virtual Quality Nurse Consultants monitors the model's output and contacts the patient's local care team when it fires, handling more than 16,000 alerts a year. A physician researcher analysis published in the New England Journal of Medicine found the program prevented an average of 520 deaths a year over a three and a half year study period, and the program has been recognized by The Joint Commission and the National Quality Forum, and honored with the 2021 John M. Eisenberg Award for Local Level Innovation in Patient Safety and Quality.",[26],[150],[],[174],{"url":175,"title":176,"publisher":177,"date":178},"https://divisionofresearch.kaiserpermanente.org/national-recognition-for-kaiser-permanente-early-alert-system/","National recognition for Kaiser Permanente early alert system","Kaiser Permanente Division of Research","2022-04-19",{"level":159,"checkedAt":128},"kaiser-permanente-advance-alert-monitor",0,[],[184,207,225,246],{"slug":185,"title":186,"shortTitle":187,"definition":188,"status":9,"industries":189,"functions":190,"patterns":192,"audience":27,"autonomy":28,"adoptionStage":193,"segment":194,"evidenceCount":195,"publicEvidenceCount":195,"organizations":196,"bestGrade":160,"headline":198,"lastVerified":206,"indexable":152},"hospital-bed-and-staff-capacity-command-center","AI command center for hospital bed and staff capacity planning","Hospital capacity command center","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.",[18],[20,191],"analytics-and-reporting",[22,24,23],"early-adopters","hospital operations",2,[197,138],"Humber River Health",{"kpi":199,"label":200,"unit":201,"n":195,"nUpTo":181,"kind":202,"value":203,"qualifier":204,"claimant":205,"organization":138,"vendorReported":139},"processing-time-reduction","Cycle time reduction","percent","reported",38,"exact","organization","2026-09-28",{"slug":208,"title":209,"shortTitle":210,"definition":211,"status":9,"industries":212,"functions":213,"patterns":214,"audience":27,"autonomy":28,"adoptionStage":29,"segment":216,"evidenceCount":195,"publicEvidenceCount":195,"organizations":217,"bestGrade":160,"headline":220,"lastVerified":206,"indexable":152},"radiology-worklist-triage","AI prioritization of radiology and imaging worklists","Radiology worklist triage","An AI system that analyzes a medical image immediately after a scan, flags time sensitive findings such as a brain bleed, a stroke causing large vessel occlusion or a pulmonary embolism, and reorders the radiologist's worklist and notifies the care team so the most urgent cases are read and acted on first, while a radiologist confirms every finding before it changes a patient's treatment.",[18],[20],[215,24,23],"computer-vision","emergency and inpatient imaging",[218,219],"Adventist Health + Rideout","Sheba Medical Center",{"kpi":199,"label":200,"unit":201,"n":221,"nUpTo":181,"kind":202,"value":222,"qualifier":223,"claimant":224,"organization":218,"vendorReported":152},1,44,"approximately","vendor",{"slug":226,"title":227,"shortTitle":228,"definition":229,"status":9,"industries":230,"functions":233,"patterns":235,"audience":27,"autonomy":238,"adoptionStage":193,"segment":239,"evidenceCount":240,"publicEvidenceCount":195,"organizations":241,"bestGrade":244,"headline":162,"lastVerified":245,"indexable":152},"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.",[231,232],"banking","payments",[234,20],"fraud-prevention",[236,24,237,22],"agentic-workflow","summarization","supervised-agent","middle-office",3,[242,243],"Coast","SEB","C","2026-09-27",{"slug":247,"title":248,"shortTitle":249,"definition":250,"status":9,"industries":251,"functions":255,"patterns":257,"audience":27,"autonomy":238,"adoptionStage":29,"evidenceCount":260,"publicEvidenceCount":261,"organizations":262,"bestGrade":160,"headline":269,"lastVerified":245,"indexable":152},"it-service-desk-resolution-agent","AI agent for IT service desk resolution","IT service desk resolution","An AI agent in Microsoft Teams, Slack or the intranet that takes the high volume IT support queue, such as password and MFA resets, account unlocks, VPN, device and software requests, and resolves common requests by acting in the identity and IT service management systems, handing the rest to the right resolver group with the context attached.",[252,231,253,254,18],"cross-industry","technology","retail-and-ecommerce",[256,20],"it-and-engineering",[258,236,259,24],"conversational-agent","rag-knowledge-assistant",8,6,[263,264,265,266,267,268],"7-Eleven Vietnam","Bank of America","Equinix","IBM","Mercari US","Vituity",{"kpi":270,"label":271,"unit":201,"n":195,"nUpTo":181,"kind":202,"value":272,"qualifier":204,"claimant":224,"organization":267,"vendorReported":152},"employee-adoption","Employee adoption",94,{"indexable":152,"reasons":274},[],[276,283,288,296,302,309,315,322,330,337,344,351,357,363,369,376,382,389,395,401,407,414,419,426,431,436,441,447,454,459,467,474,480,486,491,496],{"id":97,"label":277,"issuer":278,"region":279,"url":280,"description":281,"useCases":282,"indexable":152},"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.",230,{"id":98,"label":284,"issuer":278,"region":279,"url":285,"description":286,"useCases":287,"indexable":152},"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.",207,{"id":289,"label":290,"issuer":291,"region":292,"url":293,"description":294,"useCases":295,"indexable":152},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":100,"label":297,"issuer":298,"region":141,"url":299,"description":300,"useCases":301,"indexable":152},"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.",92,{"id":303,"label":304,"issuer":305,"region":279,"url":306,"description":307,"useCases":308,"indexable":152},"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.",71,{"id":310,"label":311,"issuer":278,"region":279,"url":312,"description":313,"useCases":314,"indexable":152},"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":316,"label":317,"issuer":318,"region":279,"url":319,"description":320,"useCases":321,"indexable":152},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":323,"label":324,"issuer":325,"region":326,"url":327,"description":328,"useCases":329,"indexable":152},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":331,"label":332,"issuer":333,"region":326,"url":334,"description":335,"useCases":336,"indexable":152},"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":338,"label":339,"issuer":340,"region":141,"url":341,"description":342,"useCases":343,"indexable":152},"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.",22,{"id":345,"label":346,"issuer":347,"region":292,"url":348,"description":349,"useCases":350,"indexable":152},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":352,"label":353,"issuer":278,"region":279,"url":354,"description":355,"useCases":356,"indexable":152},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":358,"label":359,"issuer":360,"region":279,"url":361,"description":362,"useCases":356,"indexable":152},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":99,"label":364,"issuer":365,"region":141,"url":366,"description":367,"useCases":368,"indexable":152},"HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":370,"label":371,"issuer":372,"region":292,"url":373,"description":374,"useCases":375,"indexable":152},"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":377,"label":378,"issuer":278,"region":279,"url":379,"description":380,"useCases":381,"indexable":152},"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":383,"label":384,"issuer":385,"region":141,"url":386,"description":387,"useCases":388,"indexable":152},"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":390,"label":391,"issuer":392,"region":141,"url":393,"description":394,"useCases":388,"indexable":152},"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":396,"label":397,"issuer":278,"region":279,"url":398,"description":399,"useCases":400,"indexable":152},"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":402,"label":403,"issuer":404,"region":292,"url":405,"description":406,"useCases":400,"indexable":152},"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":408,"label":409,"issuer":410,"region":141,"url":411,"description":412,"useCases":413,"indexable":152},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":415,"label":416,"issuer":278,"region":279,"url":417,"description":418,"useCases":413,"indexable":152},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":420,"label":421,"issuer":422,"region":279,"url":423,"description":424,"useCases":425,"indexable":152},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",10,{"id":427,"label":428,"issuer":325,"region":326,"url":429,"description":430,"useCases":425,"indexable":152},"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":432,"label":433,"issuer":278,"region":279,"url":434,"description":435,"useCases":425,"indexable":152},"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":437,"label":438,"issuer":278,"region":279,"url":439,"description":440,"useCases":425,"indexable":152},"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":442,"label":443,"issuer":278,"region":279,"url":444,"description":445,"useCases":446,"indexable":152},"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.",9,{"id":448,"label":449,"issuer":450,"region":141,"url":451,"description":452,"useCases":453,"indexable":152},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":455,"label":456,"issuer":278,"region":279,"url":457,"description":458,"useCases":261,"indexable":152},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":460,"label":461,"issuer":462,"region":463,"url":464,"description":465,"useCases":466,"indexable":152},"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":468,"label":469,"issuer":470,"region":279,"url":471,"description":472,"useCases":473,"indexable":152},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":475,"label":476,"issuer":477,"region":279,"url":478,"description":479,"useCases":473,"indexable":152},"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":481,"label":482,"issuer":483,"region":326,"url":484,"description":485,"useCases":240,"indexable":152},"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":487,"label":488,"issuer":278,"region":279,"url":489,"description":490,"useCases":240,"indexable":152},"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":492,"label":493,"issuer":278,"region":279,"url":494,"description":495,"useCases":240,"indexable":152},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":497,"label":498,"issuer":499,"region":141,"url":500,"description":501,"useCases":240,"indexable":152},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683489854]