[{"data":1,"prerenderedAt":618},["ShallowReactive",2],{"uc-medical-coding-automation":3,"uc-regulations":404},{"useCase":4,"evidence":189,"blitsAiDeployments":291,"benchmarks":292,"indicative":314,"related":317,"indexability":402,"includeUnpublished":195},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":39,"indicativeValue":46,"macroEstimates":81,"feasibility":82,"implementation":94,"risk":137,"blitsAi":169,"faq":171,"related":181,"datePublished":184,"dateModified":184,"lastVerified":184,"changelog":185,"slug":188},"AI medical coding for clinical encounters","Medical coding automation","Autonomous medical coding with AI","AI assigns diagnosis and procedure codes from clinical notes and routes doubtful cases to coders. Your Health reports it codes 95.5% of encounters automatically.","published","AI that reads the clinical documentation of an encounter and assigns the diagnosis and procedure codes (such as ICD-10, CPT and HCPCS) needed for billing and reporting, either as suggestions for a certified coder or autonomously for encounters it can code with high confidence, sending the rest to coders with the reasons.",[12,13,14,15],"autonomous coding","computer assisted coding","AI medical coder","automated charge capture from clinical notes",[17],"healthcare",[19,20],"finance-and-accounting","operations",[22,23],"classification-and-routing","document-processing",[25,26],"internal-tools","api","back-office","supervised-agent","early-adopters","Every patient encounter has to be translated into standard codes before a provider can bill for\nit. Certified coders read the notes and choose among tens of thousands of diagnosis and procedure\ncodes, modifiers and add ons, following payer rules that change every year. Coders are scarce,\nbacklogs delay cash, and inconsistent coding causes claim denials, rework and lost revenue.\n\nCoding also carries compliance risk in both directions. Undercoding loses revenue that the\ndocumentation supports; overcoding, especially of diagnoses that raise risk adjustment payments,\nleads to audits, repayments and fraud allegations. Computer assisted coding has suggested codes for\nyears; newer systems code the simpler encounters on their own, which raises the question of who\nchecks their work.",[],"1. **Receive the signed documentation.** When an encounter is closed, the notes, orders and results\n   are sent to the coding engine.\n2. **Assign codes.** The model proposes diagnosis and procedure codes, modifiers and sequencing, with\n   the text that supports each code and a confidence level.\n3. **Apply rules.** Payer edits, coding guidelines and the organization's own policies are checked,\n   and documentation gaps are flagged for the clinician.\n4. **Route by confidence.** High confidence encounters of approved types are released to billing\n   automatically; the rest go to a coder's worklist with the suggested codes.\n5. **Audit and learn.** Coders and auditors sample automated encounters, and denials and audit\n   findings feed back into rules and models.",[34,35,36,37,38],"cost-to-serve","speed","compliance","revenue-growth","employee-productivity",[40,41,42,43,44,45],"automation-rate","accuracy","error-reduction","productivity-gain","processing-time-reduction","cost-reduction",{"referenceOrg":47,"inputs":48,"formula":76,"currency":77,"period":78,"resultLabel":79,"caveat":80},"A physician group with 1 million coded encounters a year",[49,55,62,69],{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54},"encounters","Encounters coded per year",1000000,"encounters per year","The reference physician group.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"automationShare","Share of encounters coded without a coder",0.5,0.85,"fraction of encounters","Conservative against the 95.5% encounter level automation that Your Health reports on this page. The lower range is an editorial caution, not a sourced figure: one self reported result is thin evidence, and it does not say how \"automated\" is defined or measured.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"minutesPerEncounter","Coder minutes per encounter today",2,5,"minutes per encounter","Editorial assumption for professional fee coding. Replace with your own productivity data.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"costPerHour","Fully loaded cost per coder hour",35,60,"USD per hour","Editorial assumption covering internal and outsourced coders. Replace with your own.","encounters * automationShare * minutesPerEncounter / 60 * costPerHour","USD","per year","Coder effort released","Coder effort only. It leaves out the effect on denials, days to bill and cash, the revenue effect of more complete coding (which must be supported by documentation), audit and compliance costs, and the licence cost, often charged per encounter.",[],{"complexity":83,"complexityNote":84,"dataPrerequisites":85,"integrations":89},"medium","Mature vendors exist and integrate with common record systems. The work is in choosing which encounter types may be released without a coder, calibrating to payer mix and local guidelines, and building the audit loop that compliance teams need.",[86,87,88],"Historical encounters with documentation and final codes for calibration and testing","Current code sets, payer rules and the organization's coding policies","Denial and audit history by code and specialty",[90,91,92,93],"Electronic health record for signed documentation","Practice management or billing system for charges and claims","Coder worklist and query tools for clinician documentation questions","Denial management and audit systems",{"steps":95,"guardrails":111,"humanInTheLoop":117,"kpisToInstrument":118,"failureModes":124},[96,99,102,105,108],{"title":97,"detail":98},"Start with suggestions, then release by encounter type","Run the engine as a suggestion tool first, measure agreement with coders per encounter type, and allow automatic release only where agreement stays high.",{"title":100,"detail":101},"Set confidence thresholds with compliance","Agree with compliance which encounter types, specialties and codes may be released automatically and which, such as risk adjustment diagnoses, always need a coder.",{"title":103,"detail":104},"Close the documentation loop","Route documentation gaps back to clinicians as queries instead of coding around them, so codes stay supported by the record.",{"title":106,"detail":107},"Audit automated encounters continuously","Sample automated encounters every week against a coder's review, track accuracy by code family and feed errors back.",{"title":109,"detail":110},"Watch denials and payer feedback","Compare denial rates and reasons before and after, per payer, and investigate any rise in high value codes.",[112,113,114,115,116],"Only encounter types with proven accuracy are released without a coder","Every automated code is supported by text in the signed documentation, stored with the claim","Risk adjustment diagnoses and high value procedures reviewed by a certified coder","Documentation gaps sent to the clinician as a query, never filled in by the AI","Weekly audit sample of automated encounters with results reported to compliance","Certified coders handle every encounter below the confidence threshold and all excluded code families, and they audit a sample of automated encounters. Compliance owns the release rules, and clinicians answer documentation queries.",[119,120,121,122,123],"Share of encounters released without a coder, by specialty","Coding accuracy on a weekly audit sample","Denials due to coding, per payer","Days from encounter to claim","Shift in the distribution of evaluation and management levels and risk adjustment scores",[125,128,131,134],{"title":126,"detail":127},"Upcoding at scale","The model systematically selects higher levels or adds unsupported diagnoses, which creates overpayment and fraud exposure. Monitor code distributions and audit high value codes.",{"title":129,"detail":130},"Accuracy measured on the wrong sample","Vendor and customer accuracy figures may cover only the encounters that were automated, or a sample chosen by the vendor; the Your Health figures on this page do not say how accuracy was measured. Audit a random sample of automated encounters yourself.",{"title":132,"detail":133},"Stale rules","Annual code set and payer rule changes are not reflected in time. Assign owners for updates and test before each effective date.",{"title":135,"detail":136},"Coders lose the skill to audit","If coders only handle exceptions, the organization may lose the expertise to check the machine. Keep audit and training time in coder roles.",{"euAiAct":138,"regulations":141,"guidance":146,"controls":162,"incidents":168},{"tier":139,"basis":140},"minimal","Assigning billing and statistical codes from clinical documentation is not listed in Annex III and does not decide on a person's access to care, so no specific AI Act obligations apply beyond AI literacy. Health data processing falls under GDPR Article 9, and in the United States under HIPAA and the payment integrity rules of public payers. Minimal under the AI Act does not mean low stakes: the Veterans Health Administration classifies its computer assisted coding deployment on this page as high impact in the 2025 US federal AI use case inventory, even though coders select every code.",[142,143,144,145],"eu-ai-act","gdpr","hipaa","nist-ai-rmf",[147,153,158],{"title":148,"issuer":149,"region":150,"url":151,"note":152},"General Compliance Program Guidance","Office of Inspector General, US Department of Health and Human Services","north-america","https://oig.hhs.gov/compliance/general-compliance-program-guidance/","Voluntary OIG guidance on the federal fraud and abuse laws and the seven elements of a compliance program, the frame against which US providers audit and monitor billing and coding, whether done by people or software.",{"title":154,"issuer":155,"region":150,"url":156,"note":157},"Medicare Advantage Risk Adjustment Data Validation Program","Centers for Medicare & Medicaid Services","https://www.cms.gov/data-research/monitoring-programs/medicare-risk-adjustment-data-validation-program","Explains how CMS audits whether diagnoses that Medicare Advantage organizations submit for risk adjustment are supported by medical records. Providers are not the audited party, but the plans they code for are, so unsupported diagnoses from automated coding flow into this exposure.",{"title":159,"issuer":155,"region":150,"url":160,"note":161},"ICD-10","https://www.cms.gov/medicare/coding-billing/icd-10-codes","CMS page for the ICD-10 code sets. It publishes the annual ICD-10-PCS procedure code files and relays the ICD-10-CM diagnosis code updates that CDC develops and announces; an automated coding engine must track both.",[163,164,165,166,167],"Written release rules per encounter type and code family, approved by compliance","Evidence link from every code to the supporting documentation, kept with the claim","Weekly audit of automated encounters and trend monitoring of code distributions","Change control for model, rule and code set updates","Access controls and logging for clinical documentation used by the engine",[],{"howToBuild":170},"On Blits.ai each closed encounter triggers an **agentic workflow** through the **REST API**. An\n**AI agent** with **structured output** proposes codes with the supporting text and a confidence\nlevel, using a **knowledge base** that holds the coding guidelines and the organization's policies,\nretrieved with hybrid search. **Custom functions** read the documentation from the record system,\napply payer rules and write the result to the billing system.\n\nEvery encounter below the agreed threshold, and all excluded code families, is handed to a coder\nfor approval through **human in the loop approval**. The **audit trail** records each run, **test suites** compare the\nagent's codes with coded reference encounters before any change, **monitors** alert on failures,\nand **PII masking** limits the patient data in prompts. The platform is model agnostic.",[172,175,178],{"question":173,"answer":174},"What share of encounters can AI code without a coder?","Published figures are few and define automation differently. Your Health reports that it codes 95.5% of encounters automatically with Fathom across all service lines, with accuracy rising from 96.3% to 98.3%. A Mass General Brigham executive cited \"a 70% reduction in manual labor\" with CodaMetrix, without stating scope or period. Our editorial advice is to treat these self reported figures as upper bounds and measure on your own audit sample.",{"question":176,"answer":177},"Does automated coding raise compliance risk?","It can, because errors repeat at scale. The main exposure is unsupported diagnoses or higher service levels that increase payments, which public payers audit. Keep evidence for every code, audit automated encounters and route risk adjustment diagnoses to coders.",{"question":179,"answer":180},"Is computer assisted coding the same as autonomous coding?","No. Computer assisted coding suggests codes that a coder confirms, as in the Veterans Health Administration's deployment; autonomous coding releases confident encounters to billing without a coder and sends the rest to a worklist.",[182,183],"ambient-clinical-documentation","health-prior-authorization-and-claims-adjudication","2026-09-27",[186],{"date":184,"note":187},"First published","medical-coding-automation",[190,218,262],{"title":191,"useCases":192,"organization":193,"vendors":197,"summary":201,"stage":202,"year":203,"channels":204,"languages":205,"metrics":207,"outcomeDisclosed":195,"sources":208,"verification":213,"grade":215,"id":216,"organizationSlug":217},"Veterans Health Administration: computer assisted coding with automatic code suggestions",[188],{"name":194,"anonymized":195,"country":196,"region":150,"industry":17},"US Department of Veterans Affairs, Veterans Health Administration",false,"US",[198],{"name":199,"role":200},"Solventum (formerly 3M)","platform","The Veterans Health Administration reports in the 2025 federal AI use case inventory that it uses the Solventum (formerly 3M) 360 Encompass computer assisted coding system to suggest ICD-10-CM, CPT and HCPCS codes from the clinical documentation of each encounter. Medical coders review, validate and select the codes, so the AI speeds up the coder rather than coding on its own. The inventory lists the use as deployed, classifies it as high impact and publishes no outcome figures.","production",2025,[25],[206],"en",[],[209],{"url":210,"title":211,"publisher":212},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 federal agency AI use case inventory, individually reported use cases (raw data)","Office of Management and Budget (GitHub)",{"level":214,"checkedAt":184},"source-verified","B","veterans-health-administration-computer-assisted-coding",null,{"title":219,"useCases":220,"organization":221,"vendors":223,"summary":226,"stage":227,"year":203,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":243,"sources":244,"verification":260,"grade":215,"id":261,"organizationSlug":217},"Your Health: autonomous medical coding across all service lines",[188],{"name":222,"anonymized":195,"country":196,"region":150,"industry":17},"Your Health",[224],{"name":225,"role":200},"Fathom","Your Health, a senior focused primary and specialty care group in South Carolina and Georgia with about one million patient visits a year, went live with Fathom's autonomous coding in October 2025 across every service line and place of service, integrated with its athenahealth record system. In its own newsroom Your Health reports a 95.5% encounter level automation rate and coding accuracy up from 96.3% to 98.3% since implementation; Fathom's release adds more complete diagnosis capture that raised average risk adjustment scores. Neither source says how accuracy was measured or how the remaining encounters are handled.","scaled",[26],[206],[231,239],{"kpi":40,"value":232,"unit":233,"qualifier":234,"period":235,"claimant":236,"quote":237,"sourceUrl":238},95.5,"percent","exact","encounter level, since implementation in October 2025","organization","Since implementation in October 2025, we have achieved a 95.5% encounter-level automation rate and increased coding accuracy from 96.3% to 98.3%, reflecting meaningful gains in efficiency, precision, and scalability across the organization.","https://www.yourhealth.org/company-news-1/your-health-reaches-new-milestone-in-coding-accuracy-and-automation",{"kpi":41,"value":240,"unit":233,"qualifier":234,"period":241,"baseline":242,"claimant":236,"quote":237,"sourceUrl":238},98.3,"since implementation in October 2025","96.3% under the prior coding approach",true,[245,248,252,256],{"url":238,"title":246,"publisher":222,"date":247},"Your Health Reaches New Milestone in Coding Accuracy and Automation","2026-03-23",{"url":249,"title":250,"publisher":225,"date":251},"https://fathomhealth.com/insights/your-health-deploys-fathom-autonomous-medical-coding-to-achieve-95-5-automation-rate-at-98-3-accuracy-rate-across-all-service-lines","Your Health deploys Fathom autonomous medical coding to achieve 95.5% automation rate at 98.3% accuracy rate across all service lines","2026-03-19",{"url":253,"title":254,"publisher":255,"date":251},"https://finance.yahoo.com/sectors/healthcare/articles/health-deploys-fathom-autonomous-medical-140000178.html","Your Health Deploys Fathom Autonomous Medical Coding to Achieve 95.5% Automation Rate at 98.3% Accuracy Rate Across All Service Lines","Business Wire (via Yahoo Finance)",{"url":257,"title":258,"publisher":259,"date":251},"https://www.businesswire.com/news/home/20260319818217/en/Your-Health-Deploys-Fathom-Autonomous-Medical-Coding-to-Achieve-95.5-Automation-Rate-at-98.3-Accuracy-Rate-Across-All-Service-Lines","Your Health Deploys Fathom Autonomous Medical Coding to Achieve 95.5 Automation Rate at 98.3 Accuracy Rate Across All Service Lines","Business Wire",{"level":214,"checkedAt":184},"your-health-autonomous-medical-coding",{"title":263,"useCases":264,"organization":265,"vendors":267,"summary":270,"stage":202,"year":271,"channels":272,"languages":273,"metrics":274,"outcomeDisclosed":243,"sources":283,"verification":288,"grade":289,"id":290,"organizationSlug":217},"Mass General Brigham: CodaMetrix coding automation for professional services",[188],{"name":266,"anonymized":195,"country":196,"region":150,"industry":17},"Mass General Brigham",[268],{"name":269,"role":200},"CodaMetrix","CodaMetrix, a company spun out of Mass General Brigham, uses machine learning and natural language processing on the clinical record to translate clinical notes into procedure and diagnosis codes automatically and reduce the workload of human coders. In CodaMetrix's February 2023 funding release, Mass General Brigham's vice president of physician revenue cycle services cited \"a 70% reduction in manual labor\" and a 59% reduction in denials due to coding as \"our outcomes\", without saying which work, period or sites the figures cover. Mass General Brigham physician organizations also invested in the company.",2023,[26],[206],[275,280],{"kpi":43,"value":276,"unit":233,"qualifier":234,"period":277,"claimant":236,"quote":278,"sourceUrl":279},70,"manual labor, scope and period not stated","Our outcomes — a 70% reduction in manual labor — 59% reduction in denials due to coding, and a significant increase in cost savings — is the proof.\" said Michael Mercurio, Vice President of Physician Revenue Cycle Services at Mass General Brigham.","https://www.prnewswire.com/news-releases/codametrix-closes-55m-series-a-to-autonomously-power-medical-coding-boost-health-system-revenue-cycles-301756940.html",{"kpi":42,"value":281,"unit":233,"qualifier":234,"period":282,"claimant":236,"quote":278,"sourceUrl":279},59,"claim denials due to coding",[284],{"url":279,"title":285,"publisher":286,"date":287},"CodaMetrix Closes $55M Series A to Autonomously Power Medical Coding, Boost Health System Revenue Cycles","CodaMetrix (PR Newswire)","2023-02-27",{"level":214,"checkedAt":184},"C","mass-general-brigham-codametrix-coding-automation",0,[293,299,304,309],{"kpi":41,"label":294,"unit":233,"aggregate":243,"higherIsBetter":243,"n":295,"nUpTo":291,"median":240,"min":240,"max":240,"byClaimant":296,"vendorOnly":195,"points":297},"Accuracy",1,{"organization":295,"vendor":291,"regulator":291,"independent":291},[298],{"evidenceId":261,"organization":222,"value":240,"qualifier":234,"claimant":236,"grade":215,"pooled":243},{"kpi":40,"label":300,"unit":233,"aggregate":243,"higherIsBetter":243,"n":295,"nUpTo":291,"median":232,"min":232,"max":232,"byClaimant":301,"vendorOnly":195,"points":302},"Automation rate",{"organization":295,"vendor":291,"regulator":291,"independent":291},[303],{"evidenceId":261,"organization":222,"value":232,"qualifier":234,"claimant":236,"grade":215,"pooled":243},{"kpi":42,"label":305,"unit":233,"aggregate":243,"higherIsBetter":243,"n":295,"nUpTo":291,"median":281,"min":281,"max":281,"byClaimant":306,"vendorOnly":195,"points":307},"Error reduction",{"organization":295,"vendor":291,"regulator":291,"independent":291},[308],{"evidenceId":290,"organization":266,"value":281,"qualifier":234,"claimant":236,"grade":289,"pooled":243},{"kpi":43,"label":310,"unit":233,"aggregate":243,"higherIsBetter":243,"n":295,"nUpTo":291,"median":276,"min":276,"max":276,"byClaimant":311,"vendorOnly":195,"points":312},"Productivity gain",{"organization":295,"vendor":291,"regulator":291,"independent":291},[313],{"evidenceId":290,"organization":266,"value":276,"qualifier":234,"claimant":236,"grade":289,"pooled":243},{"low":315,"high":316},583333.3333333334,4250000,[318,341,362,385],{"slug":182,"title":319,"shortTitle":320,"definition":321,"status":9,"industries":322,"functions":323,"patterns":325,"audience":329,"autonomy":330,"adoptionStage":331,"evidenceCount":332,"publicEvidenceCount":332,"organizations":333,"bestGrade":215,"headline":336,"lastVerified":184,"indexable":243},"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.",[17],[20,324],"knowledge-management",[326,327,328],"speech-analytics","summarization","content-generation","employee-facing","copilot","mainstream",3,[334,335,194],"Great Ormond Street Hospital for Children NHS Foundation Trust","Kaiser Permanente",{"kpi":337,"label":338,"unit":233,"n":295,"nUpTo":291,"kind":339,"value":340,"qualifier":234,"claimant":236,"organization":334,"vendorReported":195},"handling-time-reduction","Handling time reduction","reported",8.2,{"slug":183,"title":342,"shortTitle":343,"definition":344,"status":9,"industries":345,"functions":347,"patterns":350,"audience":329,"autonomy":330,"adoptionStage":29,"segment":348,"evidenceCount":66,"publicEvidenceCount":66,"organizations":352,"bestGrade":215,"headline":358,"lastVerified":184,"indexable":243},"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.",[346,17],"insurance",[348,349,20],"claims","case-management",[23,327,351,22,328],"rag-knowledge-assistant",[353,354,355,356,357],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","ICICI Lombard","Manulife",{"kpi":337,"label":338,"unit":233,"n":65,"nUpTo":291,"kind":339,"value":359,"qualifier":360,"claimant":361,"organization":353,"vendorReported":243},50,"approximately","vendor",{"slug":363,"title":364,"shortTitle":365,"definition":366,"status":9,"industries":367,"functions":372,"patterns":373,"audience":27,"autonomy":28,"adoptionStage":331,"evidenceCount":375,"publicEvidenceCount":66,"organizations":376,"bestGrade":215,"headline":382,"lastVerified":184,"indexable":243},"intelligent-document-processing","AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[368,369,370,371],"cross-industry","government","automotive","manufacturing",[20,349,19],[23,374,22],"computer-vision",7,[377,378,379,380,381],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":41,"label":294,"unit":233,"n":295,"nUpTo":291,"kind":339,"value":383,"qualifier":384,"claimant":361,"organization":377,"vendorReported":243},90,"at-least",{"slug":386,"title":387,"shortTitle":388,"definition":389,"status":9,"industries":390,"functions":392,"patterns":394,"audience":329,"autonomy":395,"adoptionStage":29,"evidenceCount":332,"publicEvidenceCount":332,"organizations":396,"bestGrade":215,"headline":400,"lastVerified":184,"indexable":243},"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.",[17,391],"pharma-and-life-sciences",[20,393],"analytics-and-reporting",[23,22],"assist",[397,398,399],"Cleveland Clinic","Mount Sinai Health System","Yale Cancer Center",{"kpi":41,"label":294,"unit":233,"n":295,"nUpTo":291,"kind":339,"value":401,"qualifier":234,"claimant":236,"organization":397,"vendorReported":195},100,{"indexable":243,"reasons":403},[],[405,412,417,425,431,437,444,451,459,466,473,479,486,493,499,504,511,517,522,528,534,540,546,551,556,563,570,575,581,588,595,601,607,612],{"id":142,"label":406,"issuer":407,"region":408,"url":409,"description":410,"useCases":411,"indexable":243},"EU AI Act","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":143,"label":413,"issuer":407,"region":408,"url":414,"description":415,"useCases":416,"indexable":243},"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":418,"label":419,"issuer":420,"region":421,"url":422,"description":423,"useCases":424,"indexable":243},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":145,"label":426,"issuer":427,"region":150,"url":428,"description":429,"useCases":430,"indexable":243},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":432,"label":433,"issuer":407,"region":408,"url":434,"description":435,"useCases":436,"indexable":243},"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":438,"label":439,"issuer":440,"region":408,"url":441,"description":442,"useCases":443,"indexable":243},"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":445,"label":446,"issuer":447,"region":408,"url":448,"description":449,"useCases":450,"indexable":243},"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":452,"label":453,"issuer":454,"region":455,"url":456,"description":457,"useCases":458,"indexable":243},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":460,"label":461,"issuer":462,"region":455,"url":463,"description":464,"useCases":465,"indexable":243},"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":467,"label":468,"issuer":469,"region":421,"url":470,"description":471,"useCases":472,"indexable":243},"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":474,"label":475,"issuer":476,"region":150,"url":477,"description":478,"useCases":472,"indexable":243},"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":480,"label":481,"issuer":482,"region":408,"url":483,"description":484,"useCases":485,"indexable":243},"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":487,"label":488,"issuer":489,"region":421,"url":490,"description":491,"useCases":492,"indexable":243},"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":494,"label":495,"issuer":407,"region":408,"url":496,"description":497,"useCases":498,"indexable":243},"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":500,"label":501,"issuer":407,"region":408,"url":502,"description":503,"useCases":498,"indexable":243},"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":505,"label":506,"issuer":507,"region":150,"url":508,"description":509,"useCases":510,"indexable":243},"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":512,"label":513,"issuer":407,"region":408,"url":514,"description":515,"useCases":516,"indexable":243},"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":144,"label":518,"issuer":519,"region":150,"url":520,"description":521,"useCases":516,"indexable":243},"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":523,"label":524,"issuer":525,"region":421,"url":526,"description":527,"useCases":516,"indexable":243},"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":529,"label":530,"issuer":407,"region":408,"url":531,"description":532,"useCases":533,"indexable":243},"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":535,"label":536,"issuer":537,"region":150,"url":538,"description":539,"useCases":533,"indexable":243},"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":541,"label":542,"issuer":454,"region":455,"url":543,"description":544,"useCases":545,"indexable":243},"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":547,"label":548,"issuer":407,"region":408,"url":549,"description":550,"useCases":545,"indexable":243},"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":552,"label":553,"issuer":407,"region":408,"url":554,"description":555,"useCases":545,"indexable":243},"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":557,"label":558,"issuer":559,"region":408,"url":560,"description":561,"useCases":562,"indexable":243},"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":564,"label":565,"issuer":566,"region":150,"url":567,"description":568,"useCases":569,"indexable":243},"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":571,"label":572,"issuer":407,"region":408,"url":573,"description":574,"useCases":569,"indexable":243},"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":576,"label":577,"issuer":407,"region":408,"url":578,"description":579,"useCases":580,"indexable":243},"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":582,"label":583,"issuer":584,"region":585,"url":586,"description":587,"useCases":66,"indexable":243},"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":589,"label":590,"issuer":591,"region":408,"url":592,"description":593,"useCases":594,"indexable":243},"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":596,"label":597,"issuer":598,"region":408,"url":599,"description":600,"useCases":594,"indexable":243},"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":602,"label":603,"issuer":604,"region":455,"url":605,"description":606,"useCases":332,"indexable":243},"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":608,"label":609,"issuer":407,"region":408,"url":610,"description":611,"useCases":332,"indexable":243},"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":613,"label":614,"issuer":615,"region":150,"url":616,"description":617,"useCases":332,"indexable":243},"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.",1790598302653]