[{"data":1,"prerenderedAt":579},["ShallowReactive",2],{"uc-nursing-voice-documentation":3,"uc-regulations":358},{"useCase":4,"evidence":168,"blitsAiDeployments":236,"benchmarks":237,"indicative":259,"related":262,"indexability":356,"includeUnpublished":174},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":42,"indicativeValue":47,"macroEstimates":83,"feasibility":84,"implementation":93,"risk":130,"blitsAi":146,"faq":148,"related":161,"datePublished":163,"dateModified":163,"lastVerified":163,"changelog":164,"slug":167},"AI voice charting and end of shift note drafting for nurses","Nursing voice documentation","AI voice charting for nurse documentation","AI lets nurses chart by voice and draft end of shift notes. Epic reports an 85% note time cut at Mercy; Aiva reports overtime fell by half at Cedars-Sinai.","published","AI that lets a nurse document patient observations by voice at the bedside directly into the electronic health record, and that drafts a structured end of shift care plan note from the patient's chart for the nurse to review, edit and sign, so documentation happens during the shift instead of after it.",[12,13,14,15,16],"AI voice charting for nurses","ambient nursing documentation","nurse voice assistant","AI end of shift note generation","nursing documentation automation",[18],"healthcare",[20],"operations",[22,23],"content-generation","classification-and-routing",[25,26],"internal-tools","mobile-app","employee-facing","copilot","early-adopters","Nurses spend a large share of every shift on documentation rather than at the bedside. Cedars-Sinai\nsays studies show they spend up to 40 percent of their shift on documentation alone, which\ncontributes to burnout and staffing pressure. Much of it happens after the fact: a nurse finishes a\nround of observations, moves to a workstation to type them in, and often finishes the shift's\npaperwork late, on overtime hours. An end of shift note written from memory at the end of a\nlong shift is also less complete and less useful to the next shift than one built while the patient's\nstatus is fresh.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"Studies show nurses spend up to 40 percent of their shift on documentation alone, contributing to burnout and staffing challenges.","Artificial Intelligence Lightens Administrative Burden on Nurses","https://www.cedars-sinai.org/newsroom/artificial-intelligence-lightens-burden-on-nurses/",2025,"1. **Voice capture at the bedside.** A nurse presses a button on a hospital issued phone and speaks\n   an observation in plain language, such as a pain score or an intake amount.\n2. **Structured mapping.** The system parses the speech and maps it to the right fields and\n   flowsheet rows in the electronic health record, sometimes filling several fields from one\n   sentence.\n3. **Nurse confirmation.** The nurse reviews the parsed entry on screen and confirms it before it is\n   written to the record; nothing is saved without that confirmation.\n4. **End of shift note drafting.** Separately, the system reads the shift's vitals, medication\n   administration, orders and flowsheet entries and drafts a structured end of shift care plan note\n   built around the goals set at the start of the shift.\n5. **Review and sign.** The nurse reviews, edits and signs the draft note before it becomes part of\n   the permanent record and is handed to the next shift.",[39,40,41],"employee-productivity","cost-to-serve","customer-experience",[43,44,45,46],"handling-time-reduction","processing-time-reduction","cost-reduction","customer-satisfaction-uplift",{"referenceOrg":48,"inputs":49,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A hospital with 500 nursing full time equivalents documenting at the bedside",[50,56,64,71],{"key":51,"label":52,"low":53,"high":53,"unit":54,"note":55},"nurses","Nursing full time equivalents using the tool",500,"FTEs","The reference size.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62,"sourceUrl":63},"minutesPerShiftSaved","Minutes saved per nurse per 12 hour shift on end of shift notes",4,10,"minutes per shift","Epic's own average is about one minute saved per note; the low end applies that to an editorial assumption of about 4 end of shift notes per nurse per shift, replace with your own note volume. The high end uses Epic's own upper figure, 8 to 10 minutes per 12 hour shift, which Epic reports only \"for some nurses\", not as the typical result.","https://www.epic.com/epic/post/nurses-write-notes-85-faster-with-epic-ai/",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"shiftsPerYear","12 hour shifts worked per nurse per year",150,180,"shifts per year","Editorial assumption for a full time nurse working about three 12 hour shifts a week; replace with your own roster.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"costPerNurseHour","Fully loaded nurse cost per hour",55,85,"USD per hour","Editorial assumption; replace with your own fully loaded nursing cost.","nurses * minutesPerShiftSaved * shiftsPerYear / 60 * costPerNurseHour","USD","per year","Annual nursing time cost avoided from faster end of shift documentation","Counts only the end of shift note saving Epic reports. It leaves out any time saved on bedside flowsheet charting during the shift, where Cedars-Sinai's pilot with Aiva reports a separate reduction in the time between an observation and its documentation, and in incidental overtime, the cost of the software itself, and any change in note quality.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":90},"medium","Voice to field mapping only works well when it is tightly scoped to the flowsheet rows and note templates the hospital already uses in its electronic health record. The practical work is agreeing that mapping, testing it against real bedside speech including accents and background noise, and building nurses' trust that confirming a parsed entry is fast rather than another chore.",[88,89],"A defined set of flowsheet fields and note templates the tool is allowed to write to","Access to the shift's vitals, medication administration record, orders and prior notes for the end of shift draft",[91,92],"Electronic health record, for flowsheets, the medication administration record and notes","Hospital issued mobile device or workstation on wheels for voice capture",{"steps":94,"guardrails":110,"humanInTheLoop":114,"kpisToInstrument":115,"failureModes":120},[95,98,101,104,107],{"title":96,"detail":97},"Start with one unit and a narrow field set","Pilot on a single medical surgical or similar unit with a defined list of flowsheet rows, as Cedars-Sinai did on a 48 bed unit, before expanding hospital wide.",{"title":99,"detail":100},"Require confirmation before anything is written","Show the nurse the parsed entry and require an explicit confirmation before it is saved to the record; never write directly from speech without that step.",{"title":102,"detail":103},"Build the end of shift note around the shift's own goals","Structure the draft note around the patient goals set at the start of the shift, not only a list of tasks completed, so it is useful to the next shift.",{"title":105,"detail":106},"Track rejection and correction rates","Monitor how often nurses reject or heavily edit a parsed entry or a drafted note, and use that to fix the mapping or the prompt, not only to judge adoption.",{"title":108,"detail":109},"Extend only after the first unit proves out","Expand flowsheet coverage and additional units only once documentation timeliness, overtime and nurse feedback show the first unit benefited.",[111,112,113],"Nothing is written to the patient record without the nurse confirming the parsed entry or signing the drafted note","The tool only writes to a defined, agreed list of flowsheet fields and note types, not free text clinical judgment","Voice recordings and transcripts are handled under the same privacy and security rules as the rest of the electronic health record","A nurse confirms every voice captured entry before it reaches the record and reviews, edits and signs every end of shift note. The tool never finalizes a clinical entry on its own.",[116,117,118,119],"Documentation timeliness, the time from observation to charted entry","Incidental overtime hours per nurse per month","Rejection or heavy edit rate on parsed entries and drafted notes","Nurse reported satisfaction and patient experience scores on the pilot unit versus a comparable unit",[121,124,127],{"title":122,"detail":123},"Misheard entries in a noisy ward","Background noise or an accent causes a wrong field or value to be parsed. Always show the parsed entry for confirmation and track correction rates by unit and shift.",{"title":125,"detail":126},"A note that reads like a task list, not a story","A drafted note that only lists tasks completed is less useful to the next shift than one built around the patient's goals and trajectory. Structure prompts around goals and flag when a patient is not progressing as expected.",{"title":128,"detail":129},"Coverage gaps push nurses back to the keyboard","If the tool only covers some flowsheet rows, nurses end up using two systems and lose the time saving. Prioritize the rows nurses use most before expanding breadth.",{"euAiAct":131,"regulations":134,"guidance":140,"controls":141,"incidents":145},{"tier":132,"basis":133},"context-dependent","Bedside voice charting only transcribes and maps a nurse's own observations for the nurse to confirm, which is not listed in Annex III and is usually minimal risk. The end of shift note drafter generates a structured document from the patient's chart, so the provider of that generative text can owe the Article 50(2) duty to mark the output as AI generated, unless an exception such as an assistive function for standard editing applies. Neither deployment makes a clinical decision or profiles the patient today, but a version that summarized or flagged clinical risk, rather than only structuring what already happened, could need assessment as medical device software under Article 6(1) and Annex I of the EU Medical Device Regulation. Health data captured by voice or generated in a note falls under GDPR Article 9 in every case.",[135,136,137,138,139],"eu-ai-act","gdpr","hipaa","nist-ai-rmf","iso-42001",[],[142,143,144],"Confirmation required before any parsed entry is written to the record","A defined, agreed list of fields and note types the tool may write to","Nurse review, edit and signature required on every drafted note before it is final",[],{"howToBuild":147},"On Blits.ai the bedside voice capture uses a **voice** enabled channel, such as a push to talk\nwidget on a hospital issued phone, to take the transcribed observation and map it to a small,\nexplicitly agreed set of fields through a **custom function** inside an **agentic workflow**, always\nending in a confirmation step before anything is written back to the hospital's systems. The\nworkflow is configured so that it never writes to the electronic health record without that human\nconfirmation.\n\nThe end of shift note is a **flow** that pulls the shift's structured data through a **custom\nfunction** and drafts a note with a **knowledge base** scoped to the hospital's own note template\nand terminology, which the nurse reviews in the same interface. **PII masking** protects patient\nidentifiers if any content is sent to a third party model, and **analytics** and per run history\ntrack confirmation and rejection rates by unit so a mapping that is not working gets fixed quickly.",[149,152,155,158],{"question":150,"answer":151},"Does AI voice charting replace manual documentation entirely?","No. At Cedars-Sinai, a nurse confirms each parsed voice entry before it is filed to the record. At Mercy, a nurse reviews, edits and signs each Art drafted end of shift note before it becomes part of the record. In both deployments the tool changes when and how the entry is made, not who is accountable for it.",{"question":153,"answer":154},"How much time does it actually save?","Reported savings vary by what is measured. Epic reports that nurses using its Art tool for end of shift notes at Mercy cut average documentation time from 3.5 minutes to about 32 seconds, an 85% reduction. Aiva Health reports an 81% reduction in the time between a nursing observation and its documentation, and a 50% reduction in incidental overtime, in a pilot at Cedars-Sinai Medical Center.",{"question":156,"answer":157},"Does it affect patient experience scores?","Aiva Health reports that patient satisfaction on Cedars-Sinai's pilot unit rose by more than a third on Press Ganey's nursing related questions, which it attributes to nurses spending more time at the bedside and less time at a computer.",{"question":159,"answer":160},"What should a hospital pilot before expanding widely?","Start on one unit with a narrow, agreed set of flowsheet fields, as Cedars-Sinai did on a 48 bed surgical unit, and track documentation timeliness, overtime and the rate at which nurses reject or correct what the tool captures before expanding further.",[162],"ambient-clinical-documentation","2026-09-29",[165],{"date":163,"note":166},"First published","nursing-voice-documentation",[169,204],{"title":170,"useCases":171,"organization":172,"vendors":177,"summary":181,"stage":182,"year":183,"channels":184,"languages":185,"metrics":187,"outcomeDisclosed":194,"sources":195,"verification":199,"grade":201,"id":202,"organizationSlug":203},"Mercy: Epic Art AI for nurse end of shift documentation",[167],{"name":173,"anonymized":174,"country":175,"region":176,"industry":18},"Mercy",false,"US","north-america",[178],{"name":179,"role":180},"Epic Systems","platform","Mercy, one of the 15 largest health systems in the U.S., rolled out Epic's Art AI assistant so nurses can generate end of shift care plan notes from the patient's chart instead of writing them from scratch. Art pulls vitals, medication administration records, flowsheets, orders and prior notes into a structured draft built around the goals set for the shift, which the nurse reviews and edits before signing.","production",2026,[25],[186],"en",[188],{"kpi":43,"value":75,"unit":189,"qualifier":190,"period":191,"claimant":192,"quote":193,"sourceUrl":63},"percent","exact","per end of shift note","vendor","At Mercy, one of the 15 largest health systems in the U.S., average end-of-shift documentation time fell from 3.5 minutes per note to about 32 seconds—an 85% reduction—while the number of notes completed fully and on-time increased by 225%.",true,[196],{"url":63,"title":197,"publisher":198},"Nurses Write Notes 85% Faster with Epic AI","Epic",{"level":200,"checkedAt":163},"source-verified","C","mercy-health-epic-art-nursing-documentation",null,{"title":205,"useCases":206,"organization":207,"vendors":209,"summary":212,"stage":213,"year":36,"channels":214,"languages":215,"metrics":216,"outcomeDisclosed":194,"sources":229,"verification":234,"grade":201,"id":235,"organizationSlug":203},"Cedars-Sinai: Aiva voice charting pilot for nurses",[167],{"name":208,"anonymized":174,"country":175,"region":176,"industry":18},"Cedars-Sinai Medical Center",[210],{"name":211,"role":180},"Aiva Health","Cedars-Sinai piloted the Aiva Nurse Assistant, a HIPAA compliant mobile app that lets nurses on a 48 bed surgical unit document patient observations by voice directly into Epic. Aiva's founder and CEO, Sumeet Bhatia, says Cedars-Sinai was the first health system to launch Aiva Nurse Assistant, developed with its own nurses through its Accelerator Program. Aiva Health's case study of the pilot, which involved more than 120 nurses, reports large reductions in the time between an observation and its documentation and in nurses' incidental overtime, alongside a rise in patient experience scores on nursing related questions. Cedars-Sinai's own newsroom separately confirms the pilot, the 48 bed surgical unit, voice dictation into 50 of the most commonly used Epic fields, and that a clinician validates each entry before it is filed to the record.","pilot",[25,26],[186],[217,222,226],{"kpi":44,"value":218,"unit":189,"qualifier":190,"period":219,"claimant":192,"quote":220,"sourceUrl":221},81,"pilot, more than 120 nurses","A pilot at Cedars-Sinai, involving over 120 nurses, reported an 81% reduction in time between nurse interventions or observations and documentation to the correct flowsheet rows, directly attributed to real-time ambient charting via Aiva's application.","https://www.aivahealth.com/blog/elevating-workflow-in-epic-aivas-ambient-documentation-for-nurses",{"kpi":45,"value":223,"unit":189,"qualifier":224,"period":213,"claimant":192,"quote":225,"sourceUrl":221},50,"at-least","The ability to complete charting during shifts significantly reduces the necessity for nurses to stay late, cutting incidental OT by more than half.",{"kpi":46,"value":227,"unit":189,"qualifier":190,"period":213,"claimant":192,"quote":228,"sourceUrl":221},37,"37% Higher Patient Satisfaction: Increased nurse presence at the bedside and reduced computer time directly correlate with improved patient perception of care quality.",[230,232],{"url":221,"title":231,"publisher":211},"Elevating Workflow in Epic: Aiva's Ambient Documentation for Nurses",{"url":35,"title":34,"publisher":233},"Cedars-Sinai",{"level":200,"checkedAt":163},"cedars-sinai-aiva-nurse-voice-charting",0,[238,244,249,254],{"kpi":45,"label":239,"unit":189,"aggregate":194,"higherIsBetter":194,"n":240,"nUpTo":236,"median":223,"min":223,"max":223,"byClaimant":241,"vendorOnly":194,"points":242},"Cost reduction",1,{"organization":236,"vendor":240,"regulator":236,"independent":236},[243],{"evidenceId":235,"organization":208,"value":223,"qualifier":224,"claimant":192,"grade":201,"pooled":194},{"kpi":44,"label":245,"unit":189,"aggregate":194,"higherIsBetter":194,"n":240,"nUpTo":236,"median":218,"min":218,"max":218,"byClaimant":246,"vendorOnly":194,"points":247},"Cycle time reduction",{"organization":236,"vendor":240,"regulator":236,"independent":236},[248],{"evidenceId":235,"organization":208,"value":218,"qualifier":190,"claimant":192,"grade":201,"pooled":194},{"kpi":43,"label":250,"unit":189,"aggregate":194,"higherIsBetter":194,"n":240,"nUpTo":236,"median":75,"min":75,"max":75,"byClaimant":251,"vendorOnly":194,"points":252},"Handling time reduction",{"organization":236,"vendor":240,"regulator":236,"independent":236},[253],{"evidenceId":202,"organization":173,"value":75,"qualifier":190,"claimant":192,"grade":201,"pooled":194},{"kpi":46,"label":255,"unit":189,"aggregate":194,"higherIsBetter":194,"n":240,"nUpTo":236,"median":227,"min":227,"max":227,"byClaimant":256,"vendorOnly":194,"points":257},"Satisfaction uplift",{"organization":236,"vendor":240,"regulator":236,"independent":236},[258],{"evidenceId":235,"organization":208,"value":227,"qualifier":190,"claimant":192,"grade":201,"pooled":194},{"low":260,"high":261},275000,1275000,[263,285,308,337],{"slug":162,"title":264,"shortTitle":265,"definition":266,"status":9,"industries":267,"functions":268,"patterns":270,"audience":27,"autonomy":28,"adoptionStage":273,"evidenceCount":274,"publicEvidenceCount":274,"organizations":275,"bestGrade":279,"headline":280,"lastVerified":284,"indexable":194},"AI ambient scribe for clinical documentation","Ambient clinical documentation","An AI scribe that listens, with the patient's consent, to the conversation between a clinician and a patient and drafts the clinical note, and often the letter or after visit summary, for the clinician to review, edit and sign in the health record. It documents; it does not diagnose or decide on treatment.",[18],[20,269],"knowledge-management",[271,272,22],"speech-analytics","summarization","mainstream",3,[276,277,278],"Great Ormond Street Hospital for Children NHS Foundation Trust","Kaiser Permanente","US Department of Veterans Affairs, Veterans Health Administration","B",{"kpi":43,"label":250,"unit":189,"n":240,"nUpTo":236,"kind":281,"value":282,"qualifier":190,"claimant":283,"organization":276,"vendorReported":174},"reported",8.2,"organization","2026-09-27",{"slug":286,"title":287,"shortTitle":288,"definition":289,"status":9,"industries":290,"functions":292,"patterns":295,"audience":27,"autonomy":28,"adoptionStage":29,"segment":293,"evidenceCount":298,"publicEvidenceCount":298,"organizations":299,"bestGrade":279,"headline":305,"lastVerified":284,"indexable":194},"health-prior-authorization-and-claims-adjudication","AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI that reads prior authorization requests, medical claims and appeals with their clinical and billing documents, extracts diagnoses, treatments and costs, checks them against the policy and published clinical criteria, and prepares a summary and recommendation for a clinician or adjudicator, who makes every adverse decision.",[291,18],"insurance",[293,294,20],"claims","case-management",[296,272,297,23,22],"document-processing","rag-knowledge-assistant",5,[300,301,302,303,304],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","ICICI Lombard","Manulife",{"kpi":43,"label":250,"unit":189,"n":306,"nUpTo":236,"kind":281,"value":223,"qualifier":307,"claimant":192,"organization":300,"vendorReported":194},2,"approximately",{"slug":309,"title":310,"shortTitle":311,"definition":312,"status":9,"industries":313,"functions":318,"patterns":320,"audience":27,"autonomy":323,"adoptionStage":273,"evidenceCount":324,"publicEvidenceCount":325,"organizations":326,"bestGrade":279,"headline":333,"lastVerified":284,"indexable":194},"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.",[314,315,316,317,18],"cross-industry","banking","technology","retail-and-ecommerce",[319,20],"it-and-engineering",[321,322,297,23],"conversational-agent","agentic-workflow","supervised-agent",8,6,[327,328,329,330,331,332],"7-Eleven Vietnam","Bank of America","Equinix","IBM","Mercari US","Vituity",{"kpi":334,"label":335,"unit":189,"n":306,"nUpTo":236,"kind":281,"value":336,"qualifier":190,"claimant":192,"organization":331,"vendorReported":194},"employee-adoption","Employee adoption",94,{"slug":338,"title":339,"shortTitle":340,"definition":341,"status":9,"industries":342,"functions":344,"patterns":346,"audience":27,"autonomy":347,"adoptionStage":29,"evidenceCount":274,"publicEvidenceCount":274,"organizations":348,"bestGrade":279,"headline":352,"lastVerified":284,"indexable":194},"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.",[18,343],"pharma-and-life-sciences",[20,345],"analytics-and-reporting",[296,23],"assist",[349,350,351],"Cleveland Clinic","Mount Sinai Health System","Yale Cancer Center",{"kpi":353,"label":354,"unit":189,"n":240,"nUpTo":236,"kind":281,"value":355,"qualifier":190,"claimant":283,"organization":349,"vendorReported":174},"accuracy","Accuracy",100,{"indexable":194,"reasons":357},[],[359,366,371,378,384,391,397,403,410,417,424,431,437,443,449,456,462,469,475,481,487,494,499,505,510,515,520,526,533,538,545,551,557,563,568,573],{"id":135,"label":360,"issuer":361,"region":362,"url":363,"description":364,"useCases":365,"indexable":194},"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":136,"label":367,"issuer":361,"region":362,"url":368,"description":369,"useCases":370,"indexable":194},"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":139,"label":372,"issuer":373,"region":374,"url":375,"description":376,"useCases":377,"indexable":194},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":138,"label":379,"issuer":380,"region":176,"url":381,"description":382,"useCases":383,"indexable":194},"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":385,"label":386,"issuer":387,"region":362,"url":388,"description":389,"useCases":390,"indexable":194},"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":392,"label":393,"issuer":361,"region":362,"url":394,"description":395,"useCases":396,"indexable":194},"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":398,"label":399,"issuer":400,"region":362,"url":401,"description":402,"useCases":223,"indexable":194},"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.",{"id":404,"label":405,"issuer":406,"region":407,"url":408,"description":409,"useCases":227,"indexable":194},"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":411,"label":412,"issuer":413,"region":407,"url":414,"description":415,"useCases":416,"indexable":194},"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":418,"label":419,"issuer":420,"region":176,"url":421,"description":422,"useCases":423,"indexable":194},"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":425,"label":426,"issuer":427,"region":374,"url":428,"description":429,"useCases":430,"indexable":194},"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":432,"label":433,"issuer":361,"region":362,"url":434,"description":435,"useCases":436,"indexable":194},"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":438,"label":439,"issuer":440,"region":362,"url":441,"description":442,"useCases":436,"indexable":194},"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":137,"label":444,"issuer":445,"region":176,"url":446,"description":447,"useCases":448,"indexable":194},"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":450,"label":451,"issuer":452,"region":374,"url":453,"description":454,"useCases":455,"indexable":194},"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":457,"label":458,"issuer":361,"region":362,"url":459,"description":460,"useCases":461,"indexable":194},"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":463,"label":464,"issuer":465,"region":176,"url":466,"description":467,"useCases":468,"indexable":194},"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":470,"label":471,"issuer":472,"region":176,"url":473,"description":474,"useCases":468,"indexable":194},"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":476,"label":477,"issuer":361,"region":362,"url":478,"description":479,"useCases":480,"indexable":194},"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":482,"label":483,"issuer":484,"region":374,"url":485,"description":486,"useCases":480,"indexable":194},"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":488,"label":489,"issuer":490,"region":176,"url":491,"description":492,"useCases":493,"indexable":194},"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":495,"label":496,"issuer":361,"region":362,"url":497,"description":498,"useCases":493,"indexable":194},"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":500,"label":501,"issuer":502,"region":362,"url":503,"description":504,"useCases":60,"indexable":194},"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.",{"id":506,"label":507,"issuer":406,"region":407,"url":508,"description":509,"useCases":60,"indexable":194},"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":511,"label":512,"issuer":361,"region":362,"url":513,"description":514,"useCases":60,"indexable":194},"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":516,"label":517,"issuer":361,"region":362,"url":518,"description":519,"useCases":60,"indexable":194},"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":521,"label":522,"issuer":361,"region":362,"url":523,"description":524,"useCases":525,"indexable":194},"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":527,"label":528,"issuer":529,"region":176,"url":530,"description":531,"useCases":532,"indexable":194},"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":534,"label":535,"issuer":361,"region":362,"url":536,"description":537,"useCases":325,"indexable":194},"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":539,"label":540,"issuer":541,"region":542,"url":543,"description":544,"useCases":298,"indexable":194},"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":546,"label":547,"issuer":548,"region":362,"url":549,"description":550,"useCases":59,"indexable":194},"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":552,"label":553,"issuer":554,"region":362,"url":555,"description":556,"useCases":59,"indexable":194},"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":558,"label":559,"issuer":560,"region":407,"url":561,"description":562,"useCases":274,"indexable":194},"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":564,"label":565,"issuer":361,"region":362,"url":566,"description":567,"useCases":274,"indexable":194},"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":569,"label":570,"issuer":361,"region":362,"url":571,"description":572,"useCases":274,"indexable":194},"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":574,"label":575,"issuer":576,"region":176,"url":577,"description":578,"useCases":274,"indexable":194},"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.",1790683494817]