[{"data":1,"prerenderedAt":572},["ShallowReactive",2],{"uc-clinical-and-regulatory-document-drafting":3,"uc-regulations":364},{"useCase":4,"evidence":201,"blitsAiDeployments":264,"benchmarks":265,"indicative":277,"related":280,"indexability":362,"includeUnpublished":207},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"problem":31,"problemStats":32,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":85,"feasibility":86,"implementation":99,"risk":142,"blitsAi":181,"faq":183,"related":193,"datePublished":196,"dateModified":196,"lastVerified":196,"changelog":197,"slug":200},"AI drafting of clinical study reports and regulatory documents","Clinical and regulatory document drafting","AI clinical study report and regulatory writing","Generative AI drafts clinical study reports from trial tables for medical writers to review. Merck halved draft errors; Novo Nordisk cut CSR writing time by 90%.","published","Generative AI that drafts clinical study reports and other regulated documents, such as protocols, patient materials and submission modules, from the trial's statistical tables, listings and figures and from approved template text, for medical writers to verify, edit and approve before anything is submitted to a regulator.",[12,13,14,15,16],"AI medical writing","clinical study report automation","CSR generation with generative AI","regulatory submission drafting","AI authoring of regulatory documents",[18],"pharma-and-life-sciences",[20,21],"regulatory-compliance","operations",[23,24,25],"content-generation","rag-knowledge-assistant","document-processing",[27],"internal-tools","employee-facing","copilot","early-adopters","A clinical study report describes the design, conduct and results of a trial in the structure\nregulators expect, and can run to 300 pages. Medical\nwriters assemble it from thousands of pages of tables, listings and figures, reconcile conflicting\nnumbers, and write narrative in precise regulatory language, followed by rounds of review. The\nsame pattern repeats for protocols, investigator brochures, safety narratives, patient materials\nand the modules of a marketing application.\n\nThe work sits on the critical path to approval, so weeks spent writing are weeks a medicine is not\nwith patients, and each report ties up experienced writers for a long time. Manual drafting is also\nprone to errors, and every error has to be found and corrected in review. Generative AI fits the task because much of the text restates structured\nresults in standard language, but the output must be exactly right: a transposed number or an\ninvented statement in a regulatory document is a serious quality failure.",[33],{"statement":34,"sourceTitle":35,"sourceUrl":36,"year":37},"Anthropic's case study reports that Novo Nordisk's staff writers averaged only 2.3 clinical study reports per year.","Novo Nordisk accelerates clinical documentation and drug development with Claude","https://claude.com/customers/novo-nordisk",2025,"1. **Ingest the study outputs.** Statistical tables, listings and figures, the protocol and the\n   statistical analysis plan are loaded and preprocessed, so each table can be read reliably.\n2. **Map content to the template.** Each section of the report template is linked to the tables and\n   approved boilerplate it needs, following the house structure and the ICH format.\n3. **Draft section by section.** A language model writes each section from its mapped sources, with\n   every number traced back to the table it came from and approved text reused where possible.\n4. **Check automatically.** Rules compare numbers in the text with the source tables, flag missing\n   sections and check terminology and style before a person sees the draft.\n5. **Review and approve.** Medical writers and study experts review, edit and interpret the results;\n   the document follows the normal quality control and approval process before submission.",[40,41,42,43],"speed","employee-productivity","compliance","cost-to-serve",[45,46,47,48,49],"handling-time-reduction","processing-time-reduction","error-reduction","hours-saved","productivity-gain",{"referenceOrg":51,"inputs":52,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A drug developer that writes 20 clinical study reports a year",[53,59,66,73],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"reports","Clinical study reports per year",20,"reports per year","The reference developer.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"hoursPerDraft","Hours to a fully human reviewed first draft today",120,180,"hours per report","The high end is Merck's reported average of 180 hours before its platform. The low end is an editorial assumption, replace both with your own time data.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"timeSaved","Share of drafting time saved",0.3,0.55,"fraction of drafting hours","The high end is Merck's achieved result: Merck went from an average of 180 to 80 hours for a fully human reviewed first draft (about 55% less). The low end is an editorial assumption for a less mature platform. Novo Nordisk reports 90% less writing time, which excludes review and approval, so it is not used as the high end.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerHour","Fully loaded cost per medical writer hour",80,150,"USD per hour","Editorial assumption covering internal writers and agency rates. Replace with your own.","reports * hoursPerDraft * timeSaved * costPerHour","USD","per year","Medical writing effort released on first drafts","Drafting effort only. It leaves out the value of earlier submissions, which for a marketed medicine can far exceed the writing cost, review and quality control time that remains, the cost of building and validating the platform, and other document types.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"high","The hard parts are reliable extraction from complex statistical tables, traceability from every sentence to its source, validation of the system under quality management rules, and changing the writing process. Merck reports a team of more than 80 people across data science, AI and medical expertise; Novo Nordisk spent months integrating legacy systems for device protocols.",[90,91,92,93],"Statistical tables, listings and figures in a consistent, machine readable format","Approved templates and boilerplate text per document type, with owners","Past reports and reviewer comments to test against","A style guide and terminology list",[95,96,97,98],"Statistical computing environment that produces the study outputs","Document management and regulatory information management systems","Quality management system for review, approval and electronic signatures","Submission publishing tools",{"steps":100,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[101,104,107,110,113],{"title":102,"detail":103},"Start with one document type and its most formulaic sections","Clinical study report results sections that restate tables are the usual starting point; leave interpretation, discussion and conclusions to writers at first.",{"title":105,"detail":106},"Make every number traceable","Store the link between each sentence and the table cell it uses, and check numbers automatically, so reviewers verify instead of recalculating.",{"title":108,"detail":109},"Reuse approved text","Put expert approved boilerplate and templates in a governed library with versions, and generate only what changes per study.",{"title":111,"detail":112},"Validate under your quality system","Treat the platform as a computerized system used in a regulated process: define its intended use, validate it on real studies, and control changes to prompts, models and templates.",{"title":114,"detail":115},"Redesign the review, then scale","Train writers to review generated drafts, measure hours, error rates and review cycles per report, and extend to further document types once quality is stable.",[117,118,119,120,121],"Every generated number checked automatically against its source table before human review","No document submitted without review and approval by qualified medical writers and study experts","Interpretation of results and conclusions written or approved by named experts","Unpublished trial data processed only in approved, access controlled environments","Change control and revalidation for prompts, models and templates","Medical writers own every document: they review each section against its sources, edit the narrative and interpretation, and take the draft through the normal quality control and approval workflow. Study clinicians and statisticians approve the interpretation of results.",[124,125,126,127,128],"Writer hours to a human reviewed first draft, per document type","Errors found in quality control per draft, by category (data, terminology, citations)","Review cycles and elapsed days from database lock to final report","Share of generated text kept after review","Regulator questions attributable to document quality",[130,133,136,139],{"title":131,"detail":132},"Numbers that do not match the tables","A model transposes, rounds or invents a figure. Check every number against its source automatically and block drafts that fail.",{"title":134,"detail":135},"Plausible but wrong interpretation","The draft states a conclusion the data do not support. Keep interpretation and discussion with experts and mark generated interpretive text clearly.",{"title":137,"detail":138},"Faster drafts, same timeline","Drafting gets quicker but review and approval stay slow. Train reviewers to work with generated drafts, and measure review cycles and elapsed days, not only drafting hours. Merck reports that it revamped its operations and trained teams in the skills needed to oversee its platform.",{"title":140,"detail":141},"Unvalidated changes","A model or prompt update changes output quality unnoticed. Regression test on reference studies before every change.",{"euAiAct":143,"regulations":146,"guidance":151,"controls":174,"incidents":180},{"tier":144,"basis":145},"context-dependent","Drafting regulated documents for expert review is not listed in Annex III and is not a practice prohibited by Article 5, so the tier turns on the sponsor's role under Article 50. A sponsor that deploys a third party drafting tool has no specific AI Act obligations beyond AI literacy: the Article 50(4) disclosure duty covers AI generated text published to inform the public on matters of public interest, which clinical study reports and regulatory submissions are not. For that sponsor the tier is minimal. A sponsor that builds its own generating system, as Merck (a proprietary platform) and Novo Nordisk (NovoScribe) did, is its provider under Article 50(2) and must mark the synthetic text in a machine readable format, unless the exemption for an assistive function for standard editing applies; drafting whole report sections goes beyond that exemption, so for that sponsor the tier is limited. Quality expectations come from medicines regulation and EMA guidance: the EMA reflection paper expects close human supervision and quality review when AI drafts medicinal product information documents, and makes the clinical trial sponsor, marketing authorisation applicant or holder, or manufacturer responsible for ensuring that models and data pipelines are fit for purpose and meet GxP standards and EMA guidelines.",[147,148,149,150],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001",[152,158,164,170],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"ICH E3, Structure and Content of Clinical Study Reports","International Council for Harmonisation","global","https://database.ich.org/sites/default/files/E3_Guideline.pdf","Sets the structure and content that a clinical study report follows for submission to regulators, the format the drafting tool's templates map against.",{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Reflection paper on the use of artificial intelligence (AI) in the medicinal product lifecycle","European Medicines Agency","europe","https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf","States that AI used for drafting, compiling or reviewing medicinal product information documents should be used under close human supervision, with quality review so that all generated text is factually and syntactically correct before submission.",{"title":165,"issuer":166,"region":167,"url":168,"note":169},"Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products (draft guidance)","US Food and Drug Administration","north-america","https://www.fda.gov/media/184830/download","The January 2025 draft, still marked as draft guidance on the FDA site in September 2026, does not address AI used for operational efficiencies such as drafting or writing a regulatory submission when it does not affect patient safety, drug quality or the reliability of study results; its credibility framework applies when AI produces data that support regulatory decisions.",{"title":171,"issuer":166,"region":167,"url":172,"note":173},"Part 11, Electronic Records; Electronic Signatures, Scope and Application","https://www.fda.gov/regulatory-information/search-fda-guidance-documents/part-11-electronic-records-electronic-signatures-scope-and-application","States that FDA interprets the scope of Part 11 narrowly, covering electronic records and signatures required by predicate rules or submitted to FDA, and announces enforcement discretion for the validation, audit trail, record retention and record copying requirements, and for all Part 11 requirements on systems operational before 20 August 1997 (legacy systems). Predicate rules stay fully enforced regardless.",[175,176,177,178,179],"Documented intended use, validation report and change control for the drafting platform","Traceability from generated text to source tables and approved templates","Automated numeric consistency checks with results kept in the document history","Access controls and logging for unpublished trial data","Quality control sampling of generated sections by experienced writers",[],{"howToBuild":182},"On Blits.ai this is an **agentic workflow** run per document. Study outputs are registered as a\n**SQL knowledge base** or ingested as documents, approved templates and boilerplate live in a\n**knowledge base** with version control, and **hybrid retrieval** pulls the right approved text for\neach section. An **AI agent** with **structured output** drafts each section together with the\ntable references it used, and **custom functions** run the numeric checks against the source data\nand write the draft to the document system.\n\n**Human in the loop approval** keeps the release of every document with a medical writer, the\n**run history and audit trail** show what was generated from which inputs, and **test suites**\ncompare drafts with approved reference reports before any prompt or model change. The platform is\nmodel agnostic, so the sponsor can choose or switch models per section type.",[184,187,190],{"question":185,"answer":186},"How much faster does AI make clinical study reports?","Merck reports that first drafts now take three to four days instead of two to three weeks, and that the time to a fully human reviewed first draft fell from an average of 180 to 80 hours. Novo Nordisk says writing times on clinical study reports fell by 90%. Review and approval still take time, so measure the whole cycle, not only drafting.",{"question":188,"answer":189},"What do regulators expect for AI drafted submissions?","Responsibility stays with the company that uses the AI: the EMA reflection paper makes the clinical trial sponsor, marketing authorisation applicant or holder, or manufacturer responsible for ensuring that models and data pipelines are fit for purpose and meet GxP standards and EMA guidelines. For medicinal product information documents, the EMA asks for close human supervision and quality review so that all generated text is factually and syntactically correct. The FDA's January 2025 draft guidance on AI for regulatory decision making does not address AI used to draft a submission when it does not affect patient safety, drug quality or the reliability of study results.",{"question":191,"answer":192},"Does AI reduce errors or add them?","Both are possible. Merck reports 50% fewer errors in drafts across data, messaging, citations, terminology and typography, but language models can also produce plausible wrong numbers, which is why automatic checks against the source tables matter.",[194,195],"adverse-event-case-intake","clinical-trial-patient-matching","2026-09-27",[198],{"date":196,"note":199},"First published","clinical-and-regulatory-document-drafting",[202,236],{"title":203,"useCases":204,"organization":205,"vendors":209,"summary":212,"stage":213,"year":37,"channels":214,"languages":215,"metrics":217,"outcomeDisclosed":226,"sources":227,"verification":231,"grade":233,"id":234,"organizationSlug":235},"Merck: generative AI platform for first drafts of clinical study reports",[200],{"name":206,"anonymized":207,"country":208,"region":167,"industry":18},"Merck & Co.",false,"US",[210],{"name":206,"role":211},"in-house","Merck built an internal generative AI platform that combines table preprocessing with large language model authoring to produce first drafts of clinical study reports, under the oversight of qualified medical writers. Merck reports that first drafts now take three to four days instead of two to three weeks, that the time to a fully human reviewed first draft fell from an average of 180 hours to 80 hours, and that draft errors halved. The first live reports built on the platform were submitted in 2025, and Merck said it was scaling the platform across its late phase pipeline.","production",[27],[216],"en",[218],{"kpi":47,"value":219,"unit":220,"qualifier":221,"period":222,"claimant":223,"quote":224,"sourceUrl":225},50,"percent","exact","CSR first drafts, across multiple studies","organization","Increased the quality of CSR drafts – as measured by reducing the number of errors by 50% – in categories such as data, messaging, citations, terminology and typography.","https://www.merck.com/news/merck-expands-innovative-internal-generative-ai-solutions-helping-to-deliver-medicines-to-patients-faster/",true,[228],{"url":225,"title":229,"publisher":206,"date":230},"Merck Expands Innovative Internal Generative AI Solutions Helping to Deliver Medicines to Patients Faster","2025-06-25",{"level":232,"checkedAt":196},"source-verified","B","merck-clinical-study-report-generation",null,{"title":237,"useCases":238,"organization":239,"vendors":242,"summary":251,"stage":213,"year":37,"channels":252,"languages":253,"metrics":254,"outcomeDisclosed":226,"sources":259,"verification":261,"grade":262,"id":263,"organizationSlug":235},"Novo Nordisk: NovoScribe for clinical study reports and regulatory documentation",[200],{"name":240,"anonymized":207,"country":241,"region":161,"industry":18},"Novo Nordisk","DK",[243,246,249],{"name":244,"role":245},"Anthropic","model-provider",{"name":247,"role":248},"Amazon Web Services","platform",{"name":250,"role":248},"MongoDB","Novo Nordisk built NovoScribe, a documentation platform that combines retrieval augmented generation over expert approved text with case specific variables to draft clinical study reports. It runs on Amazon Bedrock and MongoDB Atlas with Claude models, and has been extended to device verification protocols and patient materials. Anthropic's case study quotes Novo Nordisk saying writing times on clinical study reports fell by 90%, with drafts going to people for review and approval; the company aims to extend it to full Common Technical Documents.",[27],[216],[255],{"kpi":45,"value":256,"unit":220,"qualifier":221,"period":257,"claimant":223,"quote":258,"sourceUrl":36},90,"writing time per clinical study report","“Claude has helped us cut writing times on CSRs by 90% so we can get documentation directly into human hands for review and approval,” said Waheed Jowiya, Digitalization Strategy Director at Novo Nordisk.",[260],{"url":36,"title":35,"publisher":244},{"level":232,"checkedAt":196},"C","novo-nordisk-novoscribe-clinical-documentation",0,[266,272],{"kpi":47,"label":267,"unit":220,"aggregate":226,"higherIsBetter":226,"n":268,"nUpTo":264,"median":219,"min":219,"max":219,"byClaimant":269,"vendorOnly":207,"points":270},"Error reduction",1,{"organization":268,"vendor":264,"regulator":264,"independent":264},[271],{"evidenceId":234,"organization":206,"value":219,"qualifier":221,"claimant":223,"grade":233,"pooled":226},{"kpi":45,"label":273,"unit":220,"aggregate":226,"higherIsBetter":226,"n":268,"nUpTo":264,"median":256,"min":256,"max":256,"byClaimant":274,"vendorOnly":207,"points":275},"Handling time reduction",{"organization":268,"vendor":264,"regulator":264,"independent":264},[276],{"evidenceId":263,"organization":240,"value":256,"qualifier":221,"claimant":223,"grade":262,"pooled":226},{"low":278,"high":279},57600,297000.00000000006,[281,300,320,348],{"slug":194,"title":282,"shortTitle":283,"definition":284,"status":9,"industries":285,"functions":287,"patterns":289,"audience":292,"autonomy":293,"adoptionStage":30,"evidenceCount":294,"publicEvidenceCount":294,"organizations":295,"bestGrade":233,"headline":235,"lastVerified":196,"indexable":226},"AI for pharmacovigilance adverse event case intake","Adverse event case intake","AI that takes in adverse event reports about medicines, vaccines and devices from calls, emails, forms, literature and partner files, decides whether each is a valid case, flags seriousness, extracts and codes the case data into the safety database format, and routes it to drug safety professionals, who review medical content and regulatory reporting.",[18,286],"government",[20,288,21],"case-management",[25,290,291],"classification-and-routing","conversational-agent","back-office","supervised-agent",4,[296,297,298,299],"Bayer","U.S. Food and Drug Administration, Center for Drug Evaluation and Research","U.S. Food and Drug Administration","Pfizer",{"slug":195,"title":301,"shortTitle":302,"definition":303,"status":9,"industries":304,"functions":306,"patterns":308,"audience":28,"autonomy":309,"adoptionStage":30,"evidenceCount":310,"publicEvidenceCount":310,"organizations":311,"bestGrade":233,"headline":315,"lastVerified":196,"indexable":226},"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.",[305,18],"healthcare",[21,307],"analytics-and-reporting",[25,290],"assist",3,[312,313,314],"Cleveland Clinic","Mount Sinai Health System","Yale Cancer Center",{"kpi":316,"label":317,"unit":220,"n":268,"nUpTo":264,"kind":318,"value":319,"qualifier":221,"claimant":223,"organization":312,"vendorReported":207},"accuracy","Accuracy","reported",100,{"slug":321,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":330,"patterns":334,"audience":28,"autonomy":29,"adoptionStage":30,"segment":292,"evidenceCount":336,"publicEvidenceCount":336,"organizations":337,"bestGrade":233,"headline":342,"lastVerified":347,"indexable":226},"outbound-notice-drafting","AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[326,327,328,286,305,329],"cross-industry","banking","insurance","wealth-and-asset-management",[21,331,332,20,333],"customer-service","collections-and-recovery","claims",[23,24,335],"translation",5,[338,339,340,341],"Acentra Health","Hiscox","Health Resources and Services Administration","SS&C Technologies",{"kpi":46,"label":343,"unit":220,"n":268,"nUpTo":264,"kind":318,"value":344,"qualifier":221,"claimant":345,"organization":346,"vendorReported":226},"Cycle time reduction",25,"vendor","SS&C GIDS and RS","2026-09-26",{"slug":349,"title":350,"shortTitle":351,"definition":352,"status":9,"industries":353,"functions":354,"patterns":357,"audience":28,"autonomy":29,"adoptionStage":358,"segment":359,"evidenceCount":268,"publicEvidenceCount":268,"organizations":360,"bestGrade":233,"headline":235,"lastVerified":347,"indexable":226},"shariah-compliance-screening","AI assistant for Shariah compliance screening and review","Shariah compliance screening","An AI assistant that screens Islamic financing contracts, deal structures and investments for Shariah compliance risks such as riba, gharar and exposure to prohibited activities, retrieves the relevant standards and fatwas, drafts the Shariah review documentation and flags issues for the Shariah board, which keeps sole authority over any ruling.",[327,329,328],[20,355,356],"legal","product-and-pricing",[24,25,290,23],"emerging","specialized-businesses",[361],"Zoya",{"indexable":226,"reasons":363},[],[365,371,375,381,387,393,400,407,415,421,427,433,440,447,453,458,465,471,477,483,489,495,501,506,511,518,525,530,536,543,549,555,561,566],{"id":147,"label":366,"issuer":367,"region":161,"url":368,"description":369,"useCases":370,"indexable":226},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":148,"label":372,"issuer":367,"region":161,"url":373,"description":374,"useCases":63,"indexable":226},"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.",{"id":150,"label":376,"issuer":377,"region":155,"url":378,"description":379,"useCases":380,"indexable":226},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":149,"label":382,"issuer":383,"region":167,"url":384,"description":385,"useCases":386,"indexable":226},"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":388,"label":389,"issuer":367,"region":161,"url":390,"description":391,"useCases":392,"indexable":226},"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":394,"label":395,"issuer":396,"region":161,"url":397,"description":398,"useCases":399,"indexable":226},"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":401,"label":402,"issuer":403,"region":161,"url":404,"description":405,"useCases":406,"indexable":226},"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":408,"label":409,"issuer":410,"region":411,"url":412,"description":413,"useCases":414,"indexable":226},"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":416,"label":417,"issuer":418,"region":411,"url":419,"description":420,"useCases":344,"indexable":226},"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.",{"id":422,"label":423,"issuer":424,"region":155,"url":425,"description":426,"useCases":56,"indexable":226},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":428,"label":429,"issuer":430,"region":167,"url":431,"description":432,"useCases":56,"indexable":226},"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":434,"label":435,"issuer":436,"region":161,"url":437,"description":438,"useCases":439,"indexable":226},"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":441,"label":442,"issuer":443,"region":155,"url":444,"description":445,"useCases":446,"indexable":226},"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":448,"label":449,"issuer":367,"region":161,"url":450,"description":451,"useCases":452,"indexable":226},"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":454,"label":455,"issuer":367,"region":161,"url":456,"description":457,"useCases":452,"indexable":226},"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":459,"label":460,"issuer":461,"region":167,"url":462,"description":463,"useCases":464,"indexable":226},"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":466,"label":467,"issuer":367,"region":161,"url":468,"description":469,"useCases":470,"indexable":226},"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":472,"label":473,"issuer":474,"region":167,"url":475,"description":476,"useCases":470,"indexable":226},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":478,"label":479,"issuer":480,"region":155,"url":481,"description":482,"useCases":470,"indexable":226},"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":484,"label":485,"issuer":367,"region":161,"url":486,"description":487,"useCases":488,"indexable":226},"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":490,"label":491,"issuer":492,"region":167,"url":493,"description":494,"useCases":488,"indexable":226},"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":496,"label":497,"issuer":410,"region":411,"url":498,"description":499,"useCases":500,"indexable":226},"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":502,"label":503,"issuer":367,"region":161,"url":504,"description":505,"useCases":500,"indexable":226},"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":507,"label":508,"issuer":367,"region":161,"url":509,"description":510,"useCases":500,"indexable":226},"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":512,"label":513,"issuer":514,"region":161,"url":515,"description":516,"useCases":517,"indexable":226},"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":519,"label":520,"issuer":521,"region":167,"url":522,"description":523,"useCases":524,"indexable":226},"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":526,"label":527,"issuer":367,"region":161,"url":528,"description":529,"useCases":524,"indexable":226},"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":531,"label":532,"issuer":367,"region":161,"url":533,"description":534,"useCases":535,"indexable":226},"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":537,"label":538,"issuer":539,"region":540,"url":541,"description":542,"useCases":336,"indexable":226},"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":544,"label":545,"issuer":546,"region":161,"url":547,"description":548,"useCases":294,"indexable":226},"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":550,"label":551,"issuer":552,"region":161,"url":553,"description":554,"useCases":294,"indexable":226},"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":556,"label":557,"issuer":558,"region":411,"url":559,"description":560,"useCases":310,"indexable":226},"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":562,"label":563,"issuer":367,"region":161,"url":564,"description":565,"useCases":310,"indexable":226},"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":567,"label":568,"issuer":569,"region":167,"url":570,"description":571,"useCases":310,"indexable":226},"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.",1790598298738]