[{"data":1,"prerenderedAt":626},["ShallowReactive",2],{"uc-ai-assisted-financial-statement-audit":3,"uc-regulations":400},{"useCase":4,"evidence":174,"blitsAiDeployments":273,"benchmarks":274,"indicative":275,"related":278,"indexability":398,"includeUnpublished":180},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":18,"patterns":21,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"indicativeValue":42,"macroEstimates":76,"feasibility":77,"implementation":89,"risk":130,"blitsAi":153,"faq":155,"related":165,"datePublished":169,"dateModified":169,"lastVerified":169,"changelog":170,"slug":173},"AI assisted financial statement audit","AI in external audit","AI in external audit: agents and document review","How external auditors use AI to vouch expenses, read evidence and check disclosures. EY, KPMG and Deloitte have put AI agents into their audit platforms.","published","AI assisted financial statement audit is the use of machine learning and generative AI by an external auditor, an audit firm or a public audit office independent of the audited body, to carry out evidence procedures on that body's financial records: vouching expenses to their support, searching for unrecorded liabilities, reading and reconciling the documents it submits and completing disclosure checklists. The auditor reviews every output and owns the conclusion. Drafting plans and reports for an organization's own internal audit function is a separate page (internal audit copilot).",[6,12,13,14],"audit agents","AI audit platform","generative AI for statutory audit",[16,17],"professional-services","government",[19,20],"finance-and-accounting","regulatory-compliance",[22,23],"document-processing","content-generation",[25],"internal-tools","employee-facing","copilot","early-adopters","back-office","An external audit rests on evidence: invoices, contracts, bank confirmations, ledgers and the notes to the\naccounts. Most of it is checked by people, in samples, under a fixed timetable. The volume grows every year,\nand the checks are repetitive: match an expense to its invoice, look for liabilities that were not recorded,\ntick a disclosure checklist against the notes.\n\nSampling means most transactions are never looked at. Audit regulators also press on quality. The UK\nFinancial Reporting Council published its first guidance on AI in audit in June 2025 and a review of how\nthe six largest firms certify automated tools, because AI tools are, in its words, moving beyond\nexperimentation to becoming a reality in certain audit scenarios. The same pressure applies to public audit\noffices: Google Cloud's customer story says the Auditoría Superior de la Federación in Mexico received\nfolders and boxes of documents from audited entities and worked mainly on paper until it adopted Google\nCloud tools.",[],"1. **Load the evidence.** The platform ingests the ledger, journal entries and the documents the client\n   provides, and an AI model reads unstructured documents such as invoices, contracts and board minutes.\n2. **Run a procedure.** The AI carries out a defined step, for example comparing selected expenses with\n   their support or reading payments after year end for liabilities that were not recorded, and records\n   what it checked.\n3. **Flag exceptions.** Items that do not match their support go to the auditor with the evidence the AI\n   used. A disclosure analyzer proposes answers to the checklist from the draft financial statements, and\n   the AI drafts documentation and preliminary conclusions for the auditor.\n4. **The auditor decides.** A qualified auditor reviews the output, applies professional skepticism,\n   follows up the exceptions and signs off the working paper. The firm certifies the tool before use and\n   monitors it afterwards.",[34,35,36],"employee-productivity","risk-reduction","compliance",[38,39,40,41],"processing-time-reduction","productivity-gain","error-reduction","accuracy",{"referenceOrg":43,"inputs":44,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A mid size audit firm with 40 audit engagements for listed companies",[45,50,57,64],{"key":46,"label":47,"low":48,"high":48,"unit":46,"note":49},"engagements","Audit engagements",40,"The reference firm.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"hoursPerEngagement","Staff hours spent on routine procedures per engagement",300,600,"hours per engagement","Editorial assumption for vouching, reconciliations and checklist work. Replace with your own time records.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"share","Share of those hours the AI takes over",0.1,0.3,"fraction of hours","Editorial assumption. None of the firms on this page has published an audit time saving, so this range is deliberately modest.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"rate","Cost per staff hour",60,120,"USD per hour","Editorial assumption for a blended fully loaded rate, replace with your own.","engagements * hoursPerEngagement * share * rate","USD","per year","Staff time cost redeployed to judgment work","Gross figure. It leaves out the cost of building and certifying the tools, review time spent on AI output and any change in audit fees.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"high","A firm needs the audit platform and client data feeds, a document extraction layer, a tool certification and documentation process that satisfies the audit regulator, and training for auditors. Public audit offices also need secure handling of the documents that audited bodies submit.",[81,82,83],"Digital ledgers, journal entries and supporting documents from the audited entity","A library of audit procedures written as steps an agent can follow","Reviewed examples of past working papers and exceptions for testing",[85,86,87,88],"Audit platform and working paper system","Client data transfer and secure document intake","Document extraction and OCR","Firm quality management and AI inventory",{"steps":90,"guardrails":106,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":117},[91,94,97,100,103],{"title":92,"detail":93},"Start with one procedure","Pick a procedure with clear inputs and a clear answer, such as expense vouching or a disclosure checklist, and write it as numbered steps with the evidence required at each step.",{"title":95,"detail":96},"Certify the tool before use","Document what the tool does, its inputs, its limits and how it was tested, to the standard your audit regulator expects. The FRC guidance on AI in audit describes the documentation it looks for.",{"title":98,"detail":99},"Test against past engagements","Run the agent on closed engagements where the exceptions are known, and measure how many it finds and misses before it touches a live audit.",{"title":101,"detail":102},"Keep the auditor in the loop","Every output is a proposal. The auditor reviews exceptions and a sample of items the agent passed, and the working paper shows who concluded what.",{"title":104,"detail":105},"Monitor quality, not only usage","Track errors found in review, rework and regulator inspection findings, as well as licenses and active users.",[107,108,109,110],"The agent proposes and the auditor concludes, and the sign off is always human","Client data stays in the firm's controlled environment and is not used to train shared models","Tools are certified and documented before use on a live audit","Every agent step is logged so a reviewer can reproduce it","Auditors review all exceptions and a sample of the items the agent passed, and a partner signs the audit opinion. The firm's quality function reviews tool performance and changes.",[113,114,115,116],"Exceptions found by the agent versus found later by a reviewer","Staff hours per procedure before and after","Share of engagements and procedures using the tool","Review findings and regulator inspection findings that involve the tool",[118,121,124,127],{"title":119,"detail":120},"Usage measured, quality not","A firm counts teams and engagements that use the tool but never measures what it misses. Add quality measures, such as exceptions found later in review, from the start.",{"title":122,"detail":123},"Over reliance on the output","Auditors accept a clean result without testing it. Sample passed items and keep professional skepticism in the methodology.",{"title":125,"detail":126},"Documents misread","Poor scans and unusual contract wording produce wrong extractions. Route low confidence documents to a person.",{"title":128,"detail":129},"Client confidentiality","Evidence leaves the controlled environment. Keep processing inside approved environments and log access.",{"euAiAct":131,"regulations":134,"guidance":140,"controls":147,"incidents":152},{"tier":132,"basis":133},"minimal","A tool that helps auditors check an entity's financial records and documents is not an Annex III use, and a qualified auditor reviews every output. It would become high risk only if the same tool were used to evaluate or monitor the audit staff themselves (Annex III point 4). Audit regulators such as the FRC supervise audit quality and the documentation of the tool rather than classify it.",[135,136,137,138,139],"eu-ai-act","gdpr","uk-gdpr","iso-42001","nist-ai-rmf",[141],{"title":142,"issuer":143,"region":144,"url":145,"note":146},"FRC publishes landmark guidance providing clarity to audit profession on the uses of AI","Financial Reporting Council","europe","https:\u002F\u002Fwww.frc.org.uk\u002Fnews-and-events\u002Fnews\u002F2025\u002F06\u002Ffrc-publishes-landmark-guidance-providing-clarity-to-audit-profession-on-the-uses-of-ai\u002F","First UK guidance on AI in audit, with a thematic review of how the six largest firms certify automated tools.",[148,149,150,151],"Tool inventory with owner, purpose and certification date","Documented testing and limits per tool","Logs of every agent step and every auditor decision","Client confidentiality controls for all evidence",[],{"howToBuild":154},"Blits.ai does not replace the audit platform, the working paper system or the firm's tool certification.\nWhat it can build around them is an **AI agent** with **custom functions** that read from the platform's\nAPIs and a **knowledge base** with **hybrid retrieval** over methodology, guidance and prior working\npapers, so an auditor can ask which procedure applies and get an answer with the source.\n\nFor procedures such as comparing an expense to its supporting document, an **agentic workflow** runs the\nsteps and pauses for **human in the loop approval** before anything is written to a working paper.\n**Guardrails** and **PII masking** protect client data, **test suites** replay closed engagements on every\nchange, and the workflow **run history** keeps a full audit trail of every step the agent took. The platform is model agnostic,\nso the firm chooses the model per agent. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.",[156,159,162],{"question":157,"answer":158},"Which audit firms use AI agents in their audit platforms?","EY announced in April 2026 a multi agent framework in EY Canvas for 130,000 Assurance professionals, KPMG announced AI agents in KPMG Clara in April 2025 for expense vouching and unrecorded liabilities searches, and Deloitte announced in June 2026 a network of AI agents in Deloitte Omnia that executes preliminary procedures for professionals to review. None of the three has published a measured audit time or quality result.",{"question":160,"answer":161},"What do audit regulators expect when AI is used in an audit?","The UK Financial Reporting Council published guidance in June 2025 on documenting AI tools used in audit, and a thematic review of how the six largest firms certify automated tools. The auditor remains accountable for the conclusion, so firms certify tools, document them and review their output.",{"question":163,"answer":164},"Do public audit offices use AI too?","Yes. Google Cloud reports that Mexico's Auditoría Superior de la Federación uses ScriptoMind, built on Vertex AI, Gemini and Document AI, to read and reconcile documents from audited entities, and says audit processes that took about 10 months now take hours, or even minutes. That figure is the vendor's account.",[166,167,168],"internal-audit-copilot","continuous-controls-testing","intelligent-document-processing","2026-11-12",[171],{"date":169,"note":172},"First published","ai-assisted-financial-statement-audit",[175,203,225,246],{"title":176,"useCases":177,"organization":178,"vendors":183,"summary":186,"stage":187,"year":188,"channels":189,"languages":190,"metrics":191,"outcomeDisclosed":180,"sources":192,"verification":198,"grade":200,"id":201,"organizationSlug":202},"Deloitte: network of AI agents inside the Deloitte Omnia audit platform",[173],{"name":179,"anonymized":180,"region":181,"industry":16,"wikidata":182},"Deloitte",false,"global","Q491748",[184],{"name":179,"role":185},"in-house","Deloitte announced on 24 June 2026 a unified agentic intelligence network inside Deloitte Omnia, its global audit and assurance platform. The network brings new and existing AI agents under one framework so they can coordinate whole workflows for nearly 85,000 Audit and Assurance professionals. It was built by Deloitte's own engineering teams under its Trustworthy AI framework. Among the examples, the agents execute preliminary procedures, including data extraction, evidence analysis, drafting documentation and preliminary conclusions for professionals to review. The release gives no outcome number.","production",2026,[25],[],[],[193],{"url":194,"title":195,"publisher":196,"date":197},"https:\u002F\u002Fwww.deloitte.com\u002Fus\u002Fen\u002Fabout\u002Fpress-room\u002Fdeloitte-unveils-connected-agentic-intelligence-in-omnia.html","Deloitte unveils connected agentic intelligence in Deloitte Omnia to advance audit and assurance","Deloitte US","2026-06-24",{"level":199,"checkedAt":169},"source-verified","B","deloitte-omnia-agentic-intelligence-audit",null,{"title":204,"useCases":205,"organization":206,"vendors":209,"summary":214,"stage":187,"year":188,"channels":215,"languages":216,"metrics":217,"outcomeDisclosed":180,"sources":218,"verification":223,"grade":200,"id":224,"organizationSlug":202},"EY: agentic AI embedded in EY Canvas for 160,000 audit engagements",[173],{"name":207,"anonymized":180,"region":181,"industry":16,"wikidata":208},"EY","Q489097",[210,213],{"name":211,"role":212},"Microsoft","platform",{"name":207,"role":185},"The EY organization announced on 7 April 2026 the global roll out of a multi agent framework inside EY Canvas, its Assurance platform, covering the daily workflows of 130,000 Assurance professionals across 160,000 audit engagements. The framework runs on Microsoft Azure, Microsoft Foundry and Microsoft Fabric and is meant to help audit teams orchestrate complex tasks. EY says the roll out follows a period of testing and piloting. EY says it expects the capabilities to support all end to end audit activities by 2028 and that auditors keep professional judgment and skepticism. EY does not publish an audit quality or time outcome in the release.",[25],[],[],[219],{"url":220,"title":221,"publisher":207,"date":222},"https:\u002F\u002Fwww.ey.com\u002Fen_gl\u002Fnewsroom\u002F2026\u002F04\u002Fey-launches-enterprise-scale-agentic-ai-to-redefine-the-audit-experience-for-the-ai-era","EY launches enterprise scale agentic AI to redefine the audit experience for the AI era","2026-04-07",{"level":199,"checkedAt":169},"ey-canvas-agentic-ai-audit",{"title":226,"useCases":227,"organization":228,"vendors":231,"summary":233,"stage":187,"year":234,"channels":235,"languages":236,"metrics":237,"outcomeDisclosed":180,"sources":238,"verification":244,"grade":200,"id":245,"organizationSlug":202},"KPMG: AI agents and Financial Report Analyzer in the KPMG Clara audit platform",[173],{"name":229,"anonymized":180,"region":181,"industry":16,"wikidata":230},"KPMG","Q493751",[232],{"name":229,"role":185},"KPMG International and its member firms announced on 22 April 2025 the deployment of AI agents into KPMG Clara, the global smart audit platform, aimed at more than 95,000 auditors. The first agents support substantive procedures such as expense vouching and the search for unrecorded liabilities and accrued expenses, and a Financial Report Analyzer AI engine produces output that auditors use to complete disclosure checklists. KPMG describes the approach as AI enabled and people powered, with a human in the loop. The release gives no outcome number.",2025,[25],[],[],[239],{"url":240,"title":241,"publisher":242,"date":243},"https:\u002F\u002Fkpmg.com\u002Fxx\u002Fen\u002Fmedia\u002Fpress-releases\u002F2025\u002F04\u002Fkpmg-advances-ai-integration-in-kpmg-clara-smart-audit-platform.html","KPMG advances AI integration in KPMG Clara smart audit platform","KPMG International","2025-04-22",{"level":199,"checkedAt":169},"kpmg-clara-ai-agents-audit",{"title":247,"useCases":248,"organization":249,"vendors":254,"summary":260,"stage":187,"year":234,"channels":261,"languages":262,"metrics":264,"outcomeDisclosed":265,"sources":266,"verification":270,"grade":271,"id":272,"organizationSlug":202},"Auditoría Superior de la Federación (Mexico): ScriptoMind AI document review for federal spending audits",[173],{"name":250,"anonymized":180,"country":251,"region":252,"industry":17,"wikidata":253},"Auditoría Superior de la Federación","MX","latin-america","Q65162748",[255,257],{"name":256,"role":212},"Google Cloud",{"name":258,"role":259},"Nuuptech","integrator","The Auditoría Superior de la Federación, Mexico's federal audit office, deployed ScriptoMind in early 2025 in the unit that audits federalized spending, which reviews more than 40% of the federal resources transferred to the 32 states and more than 600 municipalities. Google Cloud's customer story says it uses Vertex AI, the Gemini API, Document AI and Vision AI to read, analyze and reconcile the documents that audited entities submit. The story reports that audit processes that took about 10 months now take hours, or even minutes. The figure is Google Cloud's account and is not recorded as a metric because it gives no comparable before and after unit.",[25],[263],"es",[],true,[267],{"url":268,"title":269,"publisher":256},"https:\u002F\u002Fcloud.google.com\u002Fcustomers\u002Fintl\u002Fes-419\u002Fasf","La Auditoría Superior de la Federación confió en IA de Google Cloud para transformar sus procesos",{"level":199,"checkedAt":169},"C","asf-mexico-scriptomind-financial-audit",0,[],{"low":276,"high":277},72000,864000,[279,311,329,372],{"slug":166,"title":280,"shortTitle":281,"definition":282,"status":9,"industries":283,"functions":289,"patterns":291,"audience":26,"autonomy":27,"adoptionStage":28,"evidenceCount":295,"publicEvidenceCount":295,"organizations":296,"bestGrade":200,"headline":301,"lastVerified":310,"indexable":265},"Generative AI copilot for internal audit","Internal audit copilot","An internal audit copilot is AI that helps internal auditors plan and write up audits, from drafting planning materials to summarizing evidence and drafting findings. It drafts planning memos and document request lists from prior audits, summarizes large evidence sets, builds risk and control matrices from policies and process documents, and drafts findings and reports, with every statement traceable to evidence and a qualified auditor accountable for every conclusion.",[284,285,286,17,287,288],"cross-industry","banking","insurance","capital-markets","wealth-and-asset-management",[290,20,19],"risk-management",[292,293,23,22,294],"rag-knowledge-assistant","summarization","anomaly-detection",4,[297,298,299,300],"Banco Bradesco","BBVA","British Columbia Investment Management Corporation","XP Inc.",{"kpi":302,"label":303,"unit":304,"n":305,"nUpTo":273,"kind":306,"value":307,"qualifier":308,"claimant":309,"organization":297,"vendorReported":265},"handling-time-reduction","Handling time reduction","percent",2,"reported",55,"exact","vendor","2026-09-27",{"slug":167,"title":312,"shortTitle":313,"definition":314,"status":9,"industries":315,"functions":316,"patterns":318,"audience":29,"autonomy":321,"adoptionStage":322,"segment":323,"evidenceCount":295,"publicEvidenceCount":295,"organizations":324,"bestGrade":200,"headline":202,"lastVerified":310,"indexable":265},"AI for continuous controls testing and control self assessment","Continuous controls testing","AI continuous controls testing checks every transaction or record against a control's rule instead of a periodic sample, giving continuous, full population assurance. It collects evidence from source systems, maps each artifact to the control it supports, flags exceptions for a human to judge, and prepares the risk and control self assessment from incident and loss data for the business to review.",[284,285,17],[290,20,317],"operations",[319,22,294,320],"agentic-workflow","classification-and-routing","supervised-agent","emerging","second-line",[325,326,327,328],"Federal Deposit Insurance Corporation","Ginnie Mae","U.S. Department of the Interior","Pension Benefit Guaranty Corporation",{"slug":168,"title":330,"shortTitle":331,"definition":332,"status":9,"industries":333,"functions":336,"patterns":338,"audience":29,"autonomy":321,"adoptionStage":340,"evidenceCount":341,"publicEvidenceCount":342,"organizations":343,"bestGrade":200,"headline":366,"lastVerified":310,"indexable":265},"AI document intelligence for unstructured forms and documents","Intelligent document processing","Intelligent document processing is AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, and splits, classifies and extracts the required fields. Each field gets a confidence score, is validated against business rules and source systems, and only the uncertain cases go to a person before the data enters the downstream process.",[284,17,285,286,334,335],"automotive","manufacturing",[317,337,19],"case-management",[22,339,320],"computer-vision","mainstream",24,22,[344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365],"Absa Group","Ancine","Banco do Brasil","Banorte","CI Financial","Daman (The National Health Insurance Company)","Emirates NBD","Encova Insurance","Food and Drug Administration","Hirschbach Motor Lines","HM Revenue and Customs","Kela","Loadsure","The Master Trust Bank of Japan","Ministry of Human Resources and Emiratisation (MoHRE)","Pupuk Indonesia","RAKBANK","Tokio Marine Insurance UAE","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","U.S. Department of Veterans Affairs","Volvo Group",{"kpi":38,"label":367,"unit":304,"n":368,"nUpTo":369,"kind":370,"value":371,"qualifier":308,"claimant":202,"organization":202,"vendorReported":180},"Cycle time reduction",7,1,"median",75,{"slug":373,"title":374,"shortTitle":375,"definition":376,"status":9,"industries":377,"functions":379,"patterns":382,"audience":26,"autonomy":27,"adoptionStage":28,"evidenceCount":383,"publicEvidenceCount":384,"organizations":385,"bestGrade":200,"headline":395,"lastVerified":310,"indexable":265},"procurement-contract-review","AI assistant for procurement and supplier contract review","Procurement and contract review","Procurement and contract review is an assistant that reads supplier contracts and proposals, for procurement and vendor management teams. It extracts the key terms, flags deviations from the organization's standard positions, drafts requests for proposal and evaluation matrices, and prepares negotiation positions, with a procurement or legal owner approving every conclusion.",[284,285,17,378,335],"retail-and-ecommerce",[380,381,19],"procurement","legal",[22,292,23],12,11,[386,387,388,389,390,391,328,392,393,394],"Bridgewater Associates","CompSource Mutual Insurance Company","U.S. General Services Administration","Administration for Children and Families","Internal Revenue Service","Authority for Public Projects and Tenders and Local Content","Pacific Northwest National Laboratory","Technology Common Services Center","Western Union",{"kpi":38,"label":367,"unit":304,"n":305,"nUpTo":273,"kind":306,"value":396,"qualifier":397,"claimant":309,"organization":387,"vendorReported":265},95,"approximately",{"indexable":265,"reasons":399},[],[401,407,412,418,425,431,437,444,452,459,465,471,478,484,491,498,504,510,515,521,527,532,538,543,548,555,562,567,572,578,584,590,596,603,608,614,620],{"id":135,"label":402,"issuer":403,"region":144,"url":404,"description":405,"useCases":406,"indexable":265},"EU AI Act","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",306,{"id":136,"label":408,"issuer":403,"region":144,"url":409,"description":410,"useCases":411,"indexable":265},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",276,{"id":138,"label":413,"issuer":414,"region":181,"url":415,"description":416,"useCases":417,"indexable":265},"ISO\u002FIEC 42001","ISO and IEC","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",149,{"id":139,"label":419,"issuer":420,"region":421,"url":422,"description":423,"useCases":424,"indexable":265},"NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",114,{"id":137,"label":426,"issuer":427,"region":144,"url":428,"description":429,"useCases":430,"indexable":265},"UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",87,{"id":432,"label":433,"issuer":403,"region":144,"url":434,"description":435,"useCases":436,"indexable":265},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":438,"label":439,"issuer":440,"region":144,"url":441,"description":442,"useCases":443,"indexable":265},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":445,"label":446,"issuer":447,"region":448,"url":449,"description":450,"useCases":451,"indexable":265},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":453,"label":454,"issuer":455,"region":448,"url":456,"description":457,"useCases":458,"indexable":265},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":460,"label":461,"issuer":403,"region":144,"url":462,"description":463,"useCases":464,"indexable":265},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":466,"label":467,"issuer":468,"region":181,"url":469,"description":470,"useCases":464,"indexable":265},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":472,"label":473,"issuer":474,"region":421,"url":475,"description":476,"useCases":477,"indexable":265},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",21,{"id":479,"label":480,"issuer":481,"region":421,"url":482,"description":483,"useCases":477,"indexable":265},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":485,"label":486,"issuer":487,"region":144,"url":488,"description":489,"useCases":490,"indexable":265},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":492,"label":493,"issuer":494,"region":421,"url":495,"description":496,"useCases":497,"indexable":265},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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.",17,{"id":499,"label":500,"issuer":403,"region":144,"url":501,"description":502,"useCases":503,"indexable":265},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",14,{"id":505,"label":506,"issuer":507,"region":181,"url":508,"description":509,"useCases":503,"indexable":265},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":511,"label":512,"issuer":403,"region":144,"url":513,"description":514,"useCases":503,"indexable":265},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":516,"label":517,"issuer":518,"region":181,"url":519,"description":520,"useCases":503,"indexable":265},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":522,"label":523,"issuer":403,"region":144,"url":524,"description":525,"useCases":526,"indexable":265},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",13,{"id":528,"label":529,"issuer":403,"region":144,"url":530,"description":531,"useCases":526,"indexable":265},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":533,"label":534,"issuer":535,"region":421,"url":536,"description":537,"useCases":383,"indexable":265},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",{"id":539,"label":540,"issuer":403,"region":144,"url":541,"description":542,"useCases":383,"indexable":265},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":544,"label":545,"issuer":403,"region":144,"url":546,"description":547,"useCases":384,"indexable":265},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":549,"label":550,"issuer":551,"region":421,"url":552,"description":553,"useCases":554,"indexable":265},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",10,{"id":556,"label":557,"issuer":558,"region":144,"url":559,"description":560,"useCases":561,"indexable":265},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":563,"label":564,"issuer":403,"region":144,"url":565,"description":566,"useCases":561,"indexable":265},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":568,"label":569,"issuer":447,"region":448,"url":570,"description":571,"useCases":561,"indexable":265},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":573,"label":574,"issuer":575,"region":421,"url":576,"description":577,"useCases":368,"indexable":265},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":579,"label":580,"issuer":403,"region":144,"url":581,"description":582,"useCases":583,"indexable":265},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",5,{"id":585,"label":586,"issuer":587,"region":144,"url":588,"description":589,"useCases":583,"indexable":265},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":591,"label":592,"issuer":593,"region":448,"url":594,"description":595,"useCases":295,"indexable":265},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":597,"label":598,"issuer":599,"region":600,"url":601,"description":602,"useCases":295,"indexable":265},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":604,"label":605,"issuer":447,"region":448,"url":606,"description":607,"useCases":295,"indexable":265},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":609,"label":610,"issuer":611,"region":144,"url":612,"description":613,"useCases":295,"indexable":265},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":615,"label":616,"issuer":403,"region":144,"url":617,"description":618,"useCases":619,"indexable":265},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",3,{"id":621,"label":622,"issuer":623,"region":421,"url":624,"description":625,"useCases":619,"indexable":265},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1794572838692]