[{"data":1,"prerenderedAt":589},["ShallowReactive",2],{"uc-court-and-case-file-summarization":3,"uc-regulations":380},{"useCase":4,"evidence":183,"blitsAiDeployments":285,"benchmarks":286,"indicative":287,"related":290,"indexability":378,"includeUnpublished":189},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":36,"indicativeValue":41,"macroEstimates":71,"feasibility":72,"implementation":83,"risk":126,"blitsAi":160,"faq":162,"related":172,"datePublished":178,"dateModified":178,"lastVerified":178,"changelog":179,"slug":182},"AI for court and case file summarization","Case file summarization","AI court and case file summarization","AI summarizes court filings, case files and recorded evidence for staff to check. See how the UK CPS and Brazil's Supreme Court use it, and the EU AI Act rules.","published","AI that condenses court filings, case files, evidence recordings and earlier decisions into structured summaries, chronologies and draft case reports with references to the source pages, so that judges, prosecutors, tribunal staff and government lawyers find what matters faster, while the person responsible reads the underlying material and makes every legal judgment.",[12,13,14,15],"case file summarisation AI","court filing summaries","judicial case report drafting","legal case bundle summarization",[17],"government",[19,20],"legal","case-management",[22,23,24],"summarization","document-processing","rag-knowledge-assistant",[26],"internal-tools","employee-facing","copilot","early-adopters","Courts, tribunals, prosecutors and government legal teams work through very large files. A single\nimmigration filing can run to hundreds of pages that the parties have not organised; a prosecution\nmay rest on long recorded interviews that prosecutors have to watch while taking notes manually;\nBrazil's Federal Supreme Court drafts case reports and headnotes for the appeals it decides. Finding,\nordering and summarising this material is slow, manual work that comes before the legal analysis starts.\n\nSummaries are an obvious help and an obvious risk. A summary that omits a key fact, misattributes a\nstatement or invents a citation can distort a decision about someone's liberty, residence or\nrights, and the High Court of England and Wales has already dealt with fictitious case citations,\nsuspected to come from generative AI, put before it.\nThe design has to keep every summary traceable to the record and every judgment with a person.",[],"1. **Ingest the file.** Filings, bundles, evidence recordings and earlier decisions are uploaded in\n   the secure environment; scans are converted to text and recordings are transcribed with time\n   stamps.\n2. **Organise.** Documents are classified by type (application, evidence, submission, decision),\n   tabbed and deduplicated, so the record has a navigable structure.\n3. **Summarise with references.** The model produces a summary in an agreed template (parties, key\n   facts, chronology, issues, relief sought) where every statement points to the page or time stamp\n   it came from.\n4. **Draft routine documents.** For high volume work it drafts standard documents such as case\n   reports or headnotes for a staff member to review and adapt.\n5. **Verify and decide.** The judge, prosecutor or lawyer checks the summary against the record and\n   makes every decision; feedback on errors improves the templates.",[34,35],"employee-productivity","speed",[37,38,39,40],"time-saved-per-task","processing-time-reduction","accuracy","hours-saved",{"referenceOrg":42,"inputs":43,"formula":66,"currency":67,"period":68,"resultLabel":69,"caveat":70},"A tribunal or prosecution service that reviews 20,000 case files a year",[44,52,59],{"key":45,"label":46,"low":47,"high":48,"unit":49,"note":50,"sourceUrl":51},"files","Case files reviewed per year",10000,30000,"case files per year","Editorial assumption. For scale, the Crown Prosecution Service expects to use its video evidence tool on about 11,000 cases a year.","https://www.gov.uk/algorithmic-transparency-records/the-crown-prosecution-service-beam-notes",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"hoursSaved","Staff hours saved per file after checking",0.5,1.5,"hours per file","Editorial assumption. No organization on this page publishes a measured saving; the person must still read the underlying material.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"hourlyCost","Fully loaded cost of a lawyer's or case officer's hour",50,90,"EUR per hour","Editorial assumption. Replace with your own staff cost.","files * hoursSaved * hourlyCost","EUR","per year","Legal staff time released","Time released, not cash saved. It leaves out the value of shorter backlogs and faster decisions for the people involved, the cost of secure infrastructure and assurance, and the cost of an error that reaches a decision.",[],{"complexity":73,"complexityNote":74,"dataPrerequisites":75,"integrations":79},"medium","Summarisation itself is mature. The work is in secure hosting for highly sensitive material, templates agreed with the judiciary or legal profession, reliable source references, and a culture where summaries support rather than replace reading the record.",[76,77,78],"Digital case files, or scans that can be converted to text reliably","Templates for the summaries and standard documents, agreed with users","A set of files with expert summaries to evaluate against",[80,81,82],"Case management system of the court, tribunal or prosecution service","Document and evidence stores, including audio and video evidence","Identity and access management with need to know permissions per case",{"steps":84,"guardrails":100,"humanInTheLoop":106,"kpisToInstrument":107,"failureModes":113},[85,88,91,94,97],{"title":86,"detail":87},"Start with staff facing preparation, not decisions","Begin with summaries that help staff navigate a file, such as the Crown Prosecution Service's summaries of video interviews, or the summaries of earlier objection advice that Amsterdam has registered for its lawyers' internal case library.",{"title":89,"detail":90},"Agree templates with the users","Co design the summary structure with judges, prosecutors or lawyers, as the CPS did with its supplier, so the output fits the way they work.",{"title":92,"detail":93},"Require references for every statement","Make each summary point link to the page or time stamp in the record, so checking is quick and omissions show.",{"title":95,"detail":96},"Evaluate against expert summaries","Measure omissions and factual errors on a sample of files summarised by experienced staff before scaling.",{"title":98,"detail":99},"Train users on limits","Mandate training before access and make clear that the summary never replaces reading or watching the evidence, as the CPS requires.",[101,102,103,104,105],"The person responsible reads or watches the underlying material; the summary is an aid","Every summary statement is referenced to the record","No generation of legal authorities; case law is retrieved from trusted databases and verified","Hosting and retention appropriate to the sensitivity of the case material","AI generated content labelled as such in the case file","Judges, prosecutors and lawyers review every summary against the record and make every decision. Summaries are never placed in the case record or shared with parties without review, and users can flag and correct errors.",[108,109,110,111,112],"Time to prepare a case for review, before and after","Omission and error rate on a sampled set of summaries","Share of summaries rated usable without major edits","Backlog and time to decision","Errors reported by users, and time to fix templates",[114,117,120,123],{"title":115,"detail":116},"Omitted facts","A summary that leaves out a key fact or a contradiction in the evidence can steer the reader. Require references and sample for omissions.",{"title":118,"detail":119},"Invented authorities","Generative models can produce plausible but fictitious case law. Only cite from trusted legal databases and verify every authority.",{"title":121,"detail":122},"Over reliance","Under time pressure staff read the summary instead of the record. Make reading the source part of the process and audit it.",{"title":124,"detail":125},"Transcription errors in names and details","Speech recognition misspells names or mishears details in recorded evidence. Let users correct transcripts and check key details against the recording.",{"euAiAct":127,"regulations":130,"guidance":137,"controls":149,"incidents":155},{"tier":128,"basis":129},"context-dependent","Annex III point 8(a) makes AI high risk when it is intended to assist a judicial authority in researching and interpreting facts and the law and in applying the law to a concrete set of facts. Tools for prosecutors fall under point 6(c) if they evaluate the reliability of evidence, and tools that assist the examination of asylum, visa or residence applications fall under point 7(c). Under Article 6(3) a system that only performs a narrow procedural task or a preparatory task, such as organising a file or transcribing and summarising it for the person who decides, may not be high risk, but the provider must document that assessment (Article 6(4)). Summaries of internal legal advice for government lawyers, as Amsterdam plans, are generally outside Annex III.",[131,132,133,134,135,136],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[138,144],{"title":139,"issuer":140,"region":141,"url":142,"note":143},"Artificial Intelligence (AI) judicial guidance (October 2025)","Courts and Tribunals Judiciary of England and Wales","europe","https://www.judiciary.uk/guidance-and-resources/artificial-intelligence-ai-judicial-guidance-october-2025/","Guidance for judicial office holders on confidentiality, hallucinations and bias; it lists summarising large bodies of text as a potentially useful task, provided the summary is checked for accuracy.",{"title":145,"issuer":146,"region":141,"url":147,"note":148},"EU AI Act Annex III, high risk AI systems","European Union","https://artificialintelligenceact.eu/annex/3/","Point 8(a) covers AI used by or for judicial authorities to research and interpret facts and law; points 6(c) and 7(c) cover evaluating evidence in criminal cases and examining asylum, visa and residence applications.",[150,151,152,153,154],"Documented Article 6(3) assessment or high risk conformity work for court facing tools","Transparency record and user guidance for every tool","Mandatory user training before access","Sampled quality review of summaries against the record","Retention and access controls matched to case sensitivity",[156],{"title":157,"url":158,"note":159},"High Court of England and Wales: Ayinde v London Borough of Haringey and Al-Haroun v Qatar National Bank [2025] EWHC 1383 (Admin)","https://www.judiciary.uk/judgments/ayinde-v-london-borough-of-haringey-and-al-haroun-v-qatar-national-bank/","The Divisional Court dealt with two cases in which fictitious or inaccurate case citations, suspected to come from generative AI, were put before the court, and warned the legal profession about its duty to verify authorities.",{"howToBuild":161},"On Blits.ai case files are ingested into a secure **knowledge base** (PDF, DOCX, images and Outlook\nemail files), and **self hosted transcription with speaker diarization** turns recorded interviews into\ntime stamped transcripts on Blits.ai infrastructure. An **agentic workflow** classifies the\ndocuments, produces a summary in the agreed template with references to pages and time stamps, and\nwaits for **human in the loop approval** before anything is saved to the case system through\n**custom functions**.\n\nAn **output guardrail** with an admin authored policy blocks responses that present case law as\nauthority, and **role based access control** with per bot roles limits who can use the tool and\nview its logs. **PII masking** at the gateway can mask configured patterns, such as emails, phone\nnumbers and IBANs, in user messages, and **test suites** grade summaries against expert summaries\nbefore each change goes live. The platform is **model agnostic** and offers EU and UAE data\nresidency for sensitive case material, with a model choice that fits the material's sensitivity.",[163,166,169],{"question":164,"answer":165},"Do courts use AI to summarise case files?","Some do, with staff in control. Brazil's Federal Supreme Court uses its Maria platform to draft case reports and headnotes for staff to review, the UK Crown Prosecution Service summarises video interviews with Beam Notes, and the US immigration courts have listed filing summaries as a planned use.",{"question":167,"answer":168},"Is AI summarisation for judges high risk under the EU AI Act?","It can be. Annex III point 8(a) covers AI that assists judicial authorities in researching and interpreting facts and the law. A purely preparatory tool, such as one that organises and summarises a file, may fall under the Article 6(3) exception, but the provider has to document that assessment.",{"question":170,"answer":171},"What is the biggest risk?","That a summary or a generated citation is trusted without checking. English courts have already dealt with fictitious authorities put before them. Keep summaries referenced to the record and verify every authority.",[173,174,175,176,177],"freedom-of-information-request-processing","civil-servant-drafting-copilot","enterprise-knowledge-search","public-service-translation","meeting-summarization-and-action-items","2026-09-27",[180],{"date":178,"note":181},"First published","court-and-case-file-summarization",[184,218,244,265],{"title":185,"useCases":186,"organization":187,"vendors":191,"summary":200,"stage":201,"year":202,"channels":203,"languages":204,"metrics":206,"outcomeDisclosed":189,"sources":207,"verification":212,"grade":215,"id":216,"organizationSlug":217},"Crown Prosecution Service: Beam Notes transcripts and structured summaries of video evidence",[182],{"name":188,"anonymized":189,"country":190,"region":141,"industry":17},"Crown Prosecution Service",false,"GB",[192,195,198],{"name":193,"role":194},"Beam","platform",{"name":196,"role":197},"OpenAI","model-provider",{"name":199,"role":197},"ElevenLabs","Prosecutors in England and Wales review recorded video interviews as part of case preparation. Beam Notes lets them upload a recording and receive a time stamped transcript and a structured summary (key details such as names and dates, and a chronology of events) built on templates that the CPS co designed with the supplier. Prosecutors must still watch the original video, every summary is reviewed before anything is moved into case systems, users rate summaries in the app, and the tool gives no advice on charging or case outcomes. The transparency record says it will be used across all 14 CPS areas on about 11,000 cases a year, with data kept by the supplier for up to 30 days.","production",2026,[26],[205],"en",[],[208],{"url":51,"title":209,"publisher":210,"date":211},"The Crown Prosecution Service: Beam Notes (algorithmic transparency record)","GOV.UK","2026-09-09",{"level":213,"checkedAt":214},"source-verified","2026-09-26","B","crown-prosecution-service-beam-notes-video-evidence-summaries",null,{"title":219,"useCases":220,"organization":221,"vendors":224,"summary":230,"stage":231,"year":202,"channels":232,"languages":233,"metrics":235,"outcomeDisclosed":189,"sources":236,"verification":242,"grade":215,"id":243,"organizationSlug":217},"City of Amsterdam: planned generative AI summaries of legal advice on objections for the city's lawyers",[182],{"name":222,"anonymized":189,"country":223,"region":141,"industry":17},"Gemeente Amsterdam","NL",[225,228],{"name":226,"role":227},"In house (Gemeente Amsterdam)","in-house",{"name":229,"role":197},"OpenAI (used via Microsoft Azure)","The City of Amsterdam's legal department keeps an internal case library of its advice on objections (bezwaren) against municipal decisions. The city has registered a tool, built in house, that would use an OpenAI GPT model (gpt-3.5-turbo or gpt-4) through Azure to write a summary of the core of each existing advice, shown first in the library, so lawyers handling a new objection can judge more quickly whether an earlier advice is relevant. Users can report errors in a summary, which are then corrected; the register says summaries will be marked as made with generative AI and checked by sampling. The register entry is marked \"In gebruik\" (in use) with a start date of July 2022, but its method section says the algorithm still has to be developed and that the prompting approach is yet to be decided, so the tool is recorded here as announced. No outcome figures are published.","announced",[26],[234],"nl",[],[237],{"url":238,"title":239,"publisher":240,"date":241},"https://algoritmes.overheid.nl/nl/algoritme/gm0363/47842380/samenvatten-van-juridische-bezwaaradviezen","Samenvatten van juridische bezwaaradviezen, Algoritmeregister","Algoritmeregister van de Nederlandse overheid","2026-07-13",{"level":213,"checkedAt":178},"gemeente-amsterdam-objection-advice-summaries",{"title":245,"useCases":246,"organization":247,"vendors":251,"summary":252,"stage":201,"year":253,"channels":254,"languages":255,"metrics":257,"outcomeDisclosed":189,"sources":258,"verification":263,"grade":215,"id":264,"organizationSlug":217},"Supremo Tribunal Federal (Brazil): Maria generative AI platform for case reports and headnotes",[182],{"name":248,"anonymized":189,"country":249,"region":250,"industry":17},"Supremo Tribunal Federal","BR","latin-america",[],"Brazil's Federal Supreme Court runs Maria, a generative AI platform inside its STF Digital environment that supports court staff in analysing cases and producing documents. It started by generating headnotes (ementas) in the National Council of Justice's standard format, case reports (relatórios) for extraordinary appeals and questionnaires for initial petitions in constitutional complaints (reclamações), and has been extended to more case classes, grammatical review and a unified search of related precedents. The court stresses that Maria never acts autonomously: staff review, adapt and decide whether to use each report or headnote. It builds on earlier tools, Victor (2018) for triaging extraordinary appeals and VitorIA (2023) for grouping similar cases. The court is deploying open source language models on servers that will soon be available in its own data center. No outcome figures are given in the article.",2025,[26],[256],"pt",[],[259],{"url":260,"title":261,"publisher":248,"date":262},"https://noticias.stf.jus.br/postsnoticias/stf-amplia-uso-de-inteligencia-artificial-em-apoio-a-atividade-jurisdicional/","STF amplia uso de inteligência artificial em apoio à atividade jurisdicional","2025-09-25",{"level":213,"checkedAt":214},"supremo-tribunal-federal-maria-case-reports",{"title":266,"useCases":267,"organization":268,"vendors":272,"summary":273,"stage":231,"year":253,"channels":274,"languages":275,"metrics":276,"outcomeDisclosed":189,"sources":277,"verification":282,"grade":215,"id":283,"organizationSlug":284},"US Executive Office for Immigration Review: planned summaries of immigration court filings",[182],{"name":269,"anonymized":189,"country":270,"region":271,"industry":17},"U.S. Department of Justice, Executive Office for Immigration Review","US","north-america",[],"The Executive Office for Immigration Review, which runs the US immigration courts, lists a pre deployment use case for summarising court filings. Filings can run to hundreds of pages and are often poorly organised; the planned tool would summarise their contents with references to the source of the information, tab and label submission types, point adjudicators and legal support staff to where relevant content sits in the record, and summarise case law for training material. The stated aim is to let adjudicators spend their time on legal analysis and conclusions.",[26],[205],[],[278],{"url":279,"title":280,"publisher":281},"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":213,"checkedAt":178},"doj-eoir-immigration-filing-summaries","u-s-department-of-justice",0,[],{"low":288,"high":289},250000,4050000,[291,308,323,345,365],{"slug":173,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":296,"patterns":298,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":301,"publicEvidenceCount":301,"organizations":302,"bestGrade":215,"headline":217,"lastVerified":214,"indexable":307},"AI for freedom of information request processing","Freedom of information requests","AI that helps a public body handle freedom of information and open government requests: logging and clarifying requests, spotting duplicates, searching and deduplicating the records in scope, proposing redactions with the exemption that applies, and drafting the response letter, with an FOI officer deciding what is released.",[17],[297,19,20],"citizen-services",[23,299,300],"classification-and-routing","content-generation",4,[303,304,305,306],"U.S. Department of Justice","U.S. Food and Drug Administration, Center for Drug Evaluation and Research","Provincie Noord-Holland","U.S. Department of the Interior",true,{"slug":174,"title":309,"shortTitle":310,"definition":311,"status":9,"industries":312,"functions":313,"patterns":315,"audience":27,"autonomy":28,"adoptionStage":316,"evidenceCount":317,"publicEvidenceCount":317,"organizations":318,"bestGrade":215,"headline":217,"lastVerified":178,"indexable":307},"AI drafting copilot for civil servants for correspondence, briefings and ministerial replies","Civil servant drafting copilot","A generative AI assistant that drafts replies to correspondence from the public and elected representatives, briefings, submissions and summaries for civil servants, grounded in the department's approved lines, policy documents and case data, with the official editing and approving every word before it is sent or cleared.",[17],[297,314,20],"knowledge-management",[300,24,22],"emerging",5,[319,188,320,321,322],"Cabinet Office (Government Communication Service)","Department for Education","Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)","Government Digital Service",{"slug":175,"title":324,"shortTitle":325,"definition":326,"status":9,"industries":327,"functions":333,"patterns":336,"audience":27,"autonomy":338,"adoptionStage":339,"evidenceCount":301,"publicEvidenceCount":301,"organizations":340,"bestGrade":215,"headline":217,"lastVerified":178,"indexable":307},"AI enterprise knowledge search for employees","Enterprise knowledge search","An assistant that lets any employee ask a question in plain language and get a synthesized answer from the organization's own policies, procedures, product manuals and research, with citations to the source documents and only from documents the employee is allowed to see.",[328,329,330,331,17,332],"cross-industry","banking","wealth-and-asset-management","insurance","professional-services",[314,334,335],"operations","customer-service",[24,337,22],"conversational-agent","assist","mainstream",[341,342,343,344],"Bank of America","Morgan Stanley","SIGNAL IDUNA","Wells Fargo",{"slug":176,"title":346,"shortTitle":347,"definition":348,"status":9,"industries":349,"functions":350,"patterns":351,"audience":354,"autonomy":28,"adoptionStage":29,"evidenceCount":355,"publicEvidenceCount":355,"organizations":356,"bestGrade":215,"headline":217,"lastVerified":214,"indexable":307},"AI translation and interpretation for multilingual public services","Public service translation","AI that translates government content, documents and conversations between officials and the public, in writing and in real time speech, so people can use public services in their own language, with human translators and interpreters reviewing what carries legal or safety weight.",[17],[297,335,334],[352,337,353,23],"translation","speech-analytics","customer-facing",8,[357,358,359,360,361,362,363,364],"Baltimore City 911 (Emergency Communications)","Delaware County","European Commission","Federal Emergency Management Agency","Internal Revenue Service","Madrid Destino","Montgomery County Government","U.S. Department of State (Bureau of Consular Affairs)",{"slug":177,"title":366,"shortTitle":367,"definition":368,"status":9,"industries":369,"functions":371,"patterns":372,"audience":27,"autonomy":28,"adoptionStage":339,"evidenceCount":317,"publicEvidenceCount":317,"organizations":373,"bestGrade":215,"headline":217,"lastVerified":178,"indexable":307},"AI meeting summarization and action items","Meeting summaries and action items","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[328,17,370,332],"technology",[314,334],[22,353],[374,375,376,377,322],"U.S. Department of Labor","Ministry of Justice","Softcat","Trace3",{"indexable":307,"reasons":379},[],[381,386,391,398,404,410,416,423,431,438,445,451,457,464,470,475,482,488,494,500,506,512,518,523,528,535,541,546,552,559,565,571,578,583],{"id":131,"label":382,"issuer":146,"region":141,"url":383,"description":384,"useCases":385,"indexable":307},"EU AI Act","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":132,"label":387,"issuer":146,"region":141,"url":388,"description":389,"useCases":390,"indexable":307},"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":134,"label":392,"issuer":393,"region":394,"url":395,"description":396,"useCases":397,"indexable":307},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":133,"label":399,"issuer":400,"region":271,"url":401,"description":402,"useCases":403,"indexable":307},"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":405,"label":406,"issuer":146,"region":141,"url":407,"description":408,"useCases":409,"indexable":307},"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":135,"label":411,"issuer":412,"region":141,"url":413,"description":414,"useCases":415,"indexable":307},"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":417,"label":418,"issuer":419,"region":141,"url":420,"description":421,"useCases":422,"indexable":307},"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":424,"label":425,"issuer":426,"region":427,"url":428,"description":429,"useCases":430,"indexable":307},"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":432,"label":433,"issuer":434,"region":427,"url":435,"description":436,"useCases":437,"indexable":307},"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":439,"label":440,"issuer":441,"region":394,"url":442,"description":443,"useCases":444,"indexable":307},"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":446,"label":447,"issuer":448,"region":271,"url":449,"description":450,"useCases":444,"indexable":307},"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":136,"label":452,"issuer":453,"region":141,"url":454,"description":455,"useCases":456,"indexable":307},"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":458,"label":459,"issuer":460,"region":394,"url":461,"description":462,"useCases":463,"indexable":307},"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":465,"label":466,"issuer":146,"region":141,"url":467,"description":468,"useCases":469,"indexable":307},"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":471,"label":472,"issuer":146,"region":141,"url":473,"description":474,"useCases":469,"indexable":307},"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":476,"label":477,"issuer":478,"region":271,"url":479,"description":480,"useCases":481,"indexable":307},"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":483,"label":484,"issuer":146,"region":141,"url":485,"description":486,"useCases":487,"indexable":307},"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":489,"label":490,"issuer":491,"region":271,"url":492,"description":493,"useCases":487,"indexable":307},"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":495,"label":496,"issuer":497,"region":394,"url":498,"description":499,"useCases":487,"indexable":307},"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":501,"label":502,"issuer":146,"region":141,"url":503,"description":504,"useCases":505,"indexable":307},"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":507,"label":508,"issuer":509,"region":271,"url":510,"description":511,"useCases":505,"indexable":307},"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":513,"label":514,"issuer":426,"region":427,"url":515,"description":516,"useCases":517,"indexable":307},"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":519,"label":520,"issuer":146,"region":141,"url":521,"description":522,"useCases":517,"indexable":307},"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":524,"label":525,"issuer":146,"region":141,"url":526,"description":527,"useCases":517,"indexable":307},"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":529,"label":530,"issuer":531,"region":141,"url":532,"description":533,"useCases":534,"indexable":307},"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":536,"label":537,"issuer":538,"region":271,"url":539,"description":540,"useCases":355,"indexable":307},"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.",{"id":542,"label":543,"issuer":146,"region":141,"url":544,"description":545,"useCases":355,"indexable":307},"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":547,"label":548,"issuer":146,"region":141,"url":549,"description":550,"useCases":551,"indexable":307},"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":553,"label":554,"issuer":555,"region":556,"url":557,"description":558,"useCases":317,"indexable":307},"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":560,"label":561,"issuer":562,"region":141,"url":563,"description":564,"useCases":301,"indexable":307},"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":566,"label":567,"issuer":568,"region":141,"url":569,"description":570,"useCases":301,"indexable":307},"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":572,"label":573,"issuer":574,"region":427,"url":575,"description":576,"useCases":577,"indexable":307},"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.",3,{"id":579,"label":580,"issuer":146,"region":141,"url":581,"description":582,"useCases":577,"indexable":307},"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":584,"label":585,"issuer":586,"region":271,"url":587,"description":588,"useCases":577,"indexable":307},"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.",1790598299654]