[{"data":1,"prerenderedAt":586},["ShallowReactive",2],{"uc-case-admissibility-and-selection-triage":3,"uc-regulations":368},{"useCase":4,"evidence":187,"blitsAiDeployments":265,"benchmarks":266,"indicative":273,"related":276,"indexability":366,"includeUnpublished":193},{"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":33,"valueDrivers":34,"kpis":37,"indicativeValue":41,"macroEstimates":79,"feasibility":80,"implementation":91,"risk":129,"blitsAi":158,"faq":160,"related":173,"datePublished":175,"dateModified":176,"lastVerified":176,"changelog":177,"slug":186},"AI triage for case admissibility and selection at supreme and constitutional courts","Case admissibility and selection triage","AI case admissibility triage for courts","AI sorts appeals by legal theme so top courts triage cases faster. Brazil's STF reported 85% test accuracy; Colombia's court gets 620,000 tutela filings a year.","published","AI that reads an incoming appeal or petition at a supreme or constitutional court, classifies it by legal theme and suggests whether it raises a question the court has already grouped for combined resolution or should be selected for full review, so that staff and justices spend their time on the legal judgment itself instead of sorting the docket by hand, while a person confirms every classification before it affects a case.",[12,13,14,15,16],"appeal admissibility classifier","general repercussion classification AI","tutela selection AI","repetitive case triage for courts","supreme court docket triage AI",[18],"government",[20,21],"legal","case-management",[23,24,25],"classification-and-routing","prediction-and-scoring","document-processing",[27],"internal-tools","employee-facing","assist","emerging","Supreme and constitutional courts sit at the top of a system that can send them enormous numbers of\nfilings that repeat the same legal question. Brazil's Federal Supreme Court decides extraordinary\nappeals only when they raise a question with \"general repercussion\", a question of economic,\npolitical, social or legal relevance, often shared by thousands of similar cases in other courts;\nevery appeal received is analysed by the court's own Secretaria de Gestão de Precedentes and, on classification,\ndecided by the court president. The court describes an AI classifier's suggested theme as an\nindication that the justices always validate or confirm when they assess the case, not a\nclassification that stands on its own. Colombia's Constitutional Court receives far more tutela\nactions, Colombia's fast track constitutional complaint for a violation of fundamental rights, than it can\nindividually review, and has to select which ones raise an issue important enough for the court\nitself to decide.\n\nSorting filings at this scale by hand is slow and adds staff time with every filing received. It is also legally\nsensitive: which theme a filing is placed under can decide whether it is resolved on its own merits or\nbundled with thousands of others under a single leading case, so an error in classification is not a\nminor administrative slip.",[],"1. **Digitise and structure the filing.** Scanned appeals and petitions are converted to text and\n   structured so a classifier can read them; Brazil's Victor system includes an image to text\n   conversion step for this reason.\n2. **Classify against the court's own themes.** A model trained on years of the court's own decided\n   cases suggests which recognised legal theme, or which selection criteria, the new filing matches.\n3. **Surface the suggestion for staff to check.** The suggested theme or selection flag appears to\n   court staff, alongside the filing, for them to confirm, correct or escalate; the model output is a\n   recommendation, not a ruling.\n4. **Route by outcome.** Confirmed filings that match an existing theme are grouped for combined\n   resolution; filings flagged as raising a new or important issue go to the selection or admission\n   process a justice ultimately decides.\n5. **Retrain as the law moves.** New leading cases and legal themes are added to the training set so\n   the classifier keeps pace with what the court has decided since, rather than freezing its\n   understanding of the law at deployment time.",[35,36],"employee-productivity","speed",[38,39,40],"accuracy","time-saved-per-task","hours-saved",{"referenceOrg":42,"inputs":43,"formula":74,"currency":75,"period":76,"resultLabel":77,"caveat":78},"A supreme or constitutional court that receives 600,000 filings a year",[44,52,60,67],{"key":45,"label":46,"low":47,"high":48,"unit":49,"note":50,"sourceUrl":51},"filings","Filings received per year",200000,620000,"filings per year","The high end, rounded down from 620,242, is Colombia's Constitutional Court's own figure for the tutela filings it receives a year on average, given by court president Alberto Rojas Ríos at the tool's launch. The low end, 200,000, is an editorial assumption for a smaller court, replace with your own.","https://www.eltiempo.com/justicia/cortes/pretoria-nueva-herramienta-para-mejorar-seleccion-de-tutelas-en-corte-constitucional-522688",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58,"sourceUrl":59},"minutesSavedPerFiling","Staff minutes saved per filing on theme classification",40,60,"minutes per filing","Brazil's Federal Supreme Court president was quoted in 2018, while Victor's classifier was still in testing rather than production, saying the classification work that would cost a court employee 40 minutes to an hour by hand is done by Victor in five seconds; the five seconds is not subtracted here because it is small next to the range. This overstates the time actually released where the court still has justices validate or confirm every suggestion: treat the low end of this range, not the high end, as the more realistic saving until you measure your own review time.","https://noticias.stf.jus.br/postsnoticias/inteligencia-artificial-trabalho-judicial-de-40-minutos-pode-ser-feito-em-5-segundos/",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"shareAutomatable","Share of filings routine enough for automated theme classification",0.2,0.5,"fraction of filings","Editorial assumption, replace with your own. Not every filing fits an existing theme; a meaningful share will always need a person's full read from the start.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"hourlyCost","Fully loaded cost of a court staff member's hour",25,50,"EUR per hour","Editorial assumption. Replace with your own staff cost.","filings * shareAutomatable * (minutesSavedPerFiling / 60) * hourlyCost","EUR","per year","Staff triage time released","Time released, not cash saved, unless the court changes staffing. It leaves out the cost of building and validating the classifier against years of decided cases, the ongoing review every suggestion still needs, the cost of a wrong classification that reaches a person late, and the very different filing volumes and manual triage times across courts and jurisdictions.",[],{"complexity":81,"complexityNote":82,"dataPrerequisites":83,"integrations":87},"high","The classifier itself is a mature text classification problem, but building one a court can trust is slow: Brazil's Federal Supreme Court began its multi year partnership with the University of Brasília in late 2017. Its theme classifier showed its first lab results in 2018, covering 27 themes, and was reported at 85% accuracy in tests; by the court's own account, the optical character recognition step of Victor has been in production only since December 2020, with the document splitter and the document classifier still being readied for production and no date set for either, and Victor as a whole was still not definitively in operation. The hard parts are a large, clean set of the court's own decided cases to train on, a legal theme taxonomy the court agrees to use, and a validation and revalidation process the judiciary accepts.",[84,85,86],"A large set of the court's own historical filings, each labelled with the theme or selection decision a person made","An owned, current taxonomy of the court's recognised legal themes or selection criteria","Reliable text extraction for scanned and handwritten filings",[88,89,90],"The court's own case management or digital docket system","Document ingestion and optical character recognition for scanned filings","The court's repository of leading cases and precedent, so the taxonomy stays current",{"steps":92,"guardrails":108,"humanInTheLoop":113,"kpisToInstrument":114,"failureModes":119},[93,96,99,102,105],{"title":94,"detail":95},"Start with a suggestion, not a decision","Build the tool to propose a theme or selection flag that a justice or the reviewing judge validates, the design both Victor and PretorIA use, rather than one that classifies filings without review.",{"title":97,"detail":98},"Digitise and structure the intake first","Convert scanned and paper filings to clean, structured text before classification runs; Victor's own pipeline treats this as a distinct step because a classifier is only as good as the text it reads.",{"title":100,"detail":101},"Train on the court's own decided cases","Use years of the court's historical filings, each already labelled by a person's classification or selection decision, as the training set, so the model reflects how this court actually classifies, not a generic legal taxonomy.",{"title":103,"detail":104},"Pilot on one filing type, then widen","Run the classifier alongside the manual process on one appeal type or a limited volume first, as Colombia's Constitutional Court planned to make PretorIA's categorisation and statistics available initially only for health related rulings, and compare its suggestions against what staff decided before relying on it more broadly.",{"title":106,"detail":107},"Keep the taxonomy and the model current","Assign an owner to add new legal themes and leading cases as the court decides them, and retrain or revalidate the classifier on a schedule, not only when accuracy visibly drops.",[109,110,111,112],"Every classification is a suggestion; a court official or justice confirms the theme or selection before it affects how a filing is routed or resolved","No new precedent, deadline or substantive right is decided by the model; it sorts and prioritises only","A record links each suggested classification to the human decision that followed, for audit and for measuring accuracy over time","The theme and selection taxonomy is owned by a legal team, not inferred by the model on its own","A justice or panel makes the actual admissibility, selection or merits decision in both deployments. PretorIA's own design keeps the reviewing judge as the sole decision maker. At Brazil's STF, every appeal received is analysed by the Secretaria de Gestão de Precedentes, and Victor's suggested theme is an indication that the justices always validate or confirm when they actually assess the case.",[115,116,117,118],"Accuracy of suggested classifications against a sample staff reviewed independently","Staff minutes per filing on classification, before and after","Share of suggestions accepted unchanged versus corrected or escalated","Backlog of filings awaiting classification or selection",[120,123,126],{"title":121,"detail":122},"A confidently wrong classification on a genuinely new question","A filing that raises a real new legal question gets sorted into an existing theme because it resembles one on the surface, and its novelty is missed. Sample newly classified filings for signs of a new issue and give staff an easy way to flag \"does not fit any current theme.\"",{"title":124,"detail":125},"Bad text in, bad classification out","A poorly scanned or handwritten filing produces garbled text that the classifier reads confidently but wrongly. Check extraction quality as its own step, the way Victor's image to text conversion is separated from theme classification, before trusting the classification.",{"title":127,"detail":128},"An \"uncertain\" queue nobody owns","Filings the model is not confident about pile up without a service level target because no team is explicitly responsible for them. Give the uncertain queue an owner and a target time to clear, not just a lower confidence score.",{"euAiAct":130,"regulations":132,"guidance":137,"controls":152,"incidents":157},{"tier":81,"basis":131},"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. Classifying a filing by legal theme and flagging it for selection or combined resolution goes to which law applies and how, not only to organising the file, so the narrow procedural task exception in Article 6(3) is unlikely to apply here, unlike a tool that only summarises or indexes a file for a person to read.",[133,134,135,136],"eu-ai-act","gdpr","iso-42001","nist-ai-rmf",[138,144,148],{"title":139,"issuer":140,"region":141,"url":142,"note":143},"EU AI Act Annex III, high risk AI systems","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 8(a) covers AI used by or for judicial authorities to research and interpret facts and law and apply the law to a concrete set of facts, which case theme classification and selection for review fall under.",{"title":145,"issuer":140,"region":141,"url":146,"note":147},"Article 6, classification rules for high risk AI systems","https://artificialintelligenceact.eu/article/6/","Sets out the narrow exception for AI that performs a narrow procedural task, improves the result of a previously completed human activity, detects decision making patterns without replacing the prior human assessment, or performs a preparatory task to an Annex III assessment, plus the Article 6(4) duty to document that assessment when a provider relies on it.",{"title":149,"issuer":140,"region":141,"url":150,"note":151},"EU AI Act Recital 61, purely ancillary administrative activities","https://artificialintelligenceact.eu/recital/61/","Excludes AI for purely ancillary administrative activities that do not affect the actual administration of justice in individual cases, such as anonymisation or communication between personnel; theme classification and case selection affect the individual case, so this exclusion does not cover them.",[153,154,155,156],"Validation of the classifier's suggestions against an independently reviewed sample before launch and on a recurring schedule after","A named legal owner for the theme or selection taxonomy, who approves every addition or change","Full record of every suggested classification and the human decision that followed","A working route for staff to flag a filing that does not fit any current theme, reviewed regularly for signs the taxonomy needs to grow",[],{"howToBuild":159},"Blits.ai is not a legal text classification research platform, so the trained theme or selection\nclassifier itself is built and validated outside it, the way Brazil's Federal Supreme Court built\nVictor with a university partner. What runs on Blits.ai is the workflow around that classifier: an\n**agentic workflow** receives the extracted filing text through the **REST API**, calls the\nclassifier as an external tool through a **custom function**, and asks an **AI agent** with\n**structured output** to turn the raw classification into a clear suggested theme and a short\nrationale for staff, citing which parts of the filing matched.\n\nThe suggestion, never a final classification, is written to the court's case system through another\n**custom function** only after **human in the loop approval**, and a full **audit trail** records\nevery suggestion and the decision that followed it, which is what lets a court measure accuracy over\ntime. A **knowledge base** of the court's current theme taxonomy and recent leading cases, retrieved\nwith **hybrid search**, keeps the agent's rationale grounded in what the court has actually decided\nrather than a static list. **Role based access control** limits who can see filings and change the\ntaxonomy, **test suites** replay a set of already classified filings before any prompt or model\nchange goes live, and the platform is model agnostic with EU and UAE data residency for\njurisdictions that require it.",[161,164,167,170],{"question":162,"answer":163},"Does AI decide which cases a supreme or constitutional court hears?","Not in either deployment on this page. Independent research describes Colombia's PretorIA as functioning like a trained search engine that helps staff identify cases, with the reviewing judge kept as the sole decision maker. Brazil's Victor suggests the legal theme of an appeal that court staff at the Secretaria de Gestão de Precedentes analyse, and the justices always validate or confirm that suggestion when they actually assess the case.",{"question":165,"answer":166},"How accurate is this kind of classification?","The CNJ news service, republished on Brazil's Federal Supreme Court's own news site, quoted then court president Dias Toffoli reporting Victor identified extraordinary appeal cases with 85% accuracy in tests, a figure from the court's own account rather than an independent audit, and from testing rather than production use. Accuracy will vary by court, filing type and how well the taxonomy fits the caseload, so measure it on your own filings before relying on it.",{"question":168,"answer":169},"How is this different from AI that summarises case files for a judge?","Case file summarisation condenses a filing, evidence or earlier decisions into a structured summary for a person to read faster. This use case classifies a filing against the court's own legal themes or selection criteria to decide how it should be routed, a narrower and more legally consequential task that this page's own EU AI Act analysis treats as high risk, because the narrow procedural task exception in Article 6(3) is unlikely to apply to it.",{"question":171,"answer":172},"Is this high risk under the EU AI Act?","Yes, as designed here. Annex III point 8(a) covers AI that assists a judicial authority in applying the law to a concrete set of facts, which legal theme classification and case selection for review both do, so the requirements for high risk systems, including risk management, logging and human oversight, apply.",[174],"court-and-case-file-summarization","2026-09-29","2026-09-30",[178,180,182,184],{"date":176,"note":179},"Published after review by an automated review workflow (independent skeptic review).",{"date":176,"note":181},"Fixed adversarial review blockers: removed the unsupported claim that PretorIA's categorisation widened beyond health rulings (El Tiempo only says it was planned to launch there); dropped the implied full production replacement of Victor and stated instead that the STF always validates or confirms the suggested theme when a case is assessed; corrected general repercussion from a 'recurring' issue to a question of relevance shared by many similar cases, matching the STF's own wording; removed the contested 4,500 cases a day figure from metaDescription and replaced it with the court's own tutela filing volume. Also restored 'always validates' in implementation steps, humanInTheLoop, FAQ 1 and the Victor evidence summary; changed the classification step to say a justice or the reviewing judge validates, not staff; dropped 'about' from the five seconds quote; attributed the 85% article to the CNJ news service (republished by STF); tightened the Article 6(3) guidance wording away from 'purely preparatory or procedural'; noted the 40 to 60 minute saving comes from a 2018 test stage remark; and softened the unsourced 'does not scale' claim.",{"date":176,"note":183},"Unpublished by an automated review workflow (independent skeptic review).",{"date":175,"note":185},"First published","case-admissibility-and-selection-triage",[188,228],{"title":189,"useCases":190,"organization":191,"vendors":196,"summary":200,"stage":201,"year":202,"channels":203,"languages":204,"metrics":206,"outcomeDisclosed":214,"sources":215,"verification":223,"grade":225,"id":226,"organizationSlug":227},"Brazil's Federal Supreme Court: Victor appeal admissibility and theme classifier",[186],{"name":192,"anonymized":193,"country":194,"region":195,"industry":18},"Supremo Tribunal Federal",false,"BR","latin-america",[197],{"name":198,"role":199},"Universidade de Brasília","integrator","Victor is an artificial intelligence system Brazil's Federal Supreme Court (STF) began building with the University of Brasília in late 2017. Of its four planned steps (converting scanned filings to text, splitting a filing into its component documents, classifying those documents, and suggesting the theme of general repercussion a filing belongs to), only the text conversion step has been in production, since the end of December 2020: as of May 2021 it had processed over 10 million pages the same day they arrived. The theme classifier's own first lab results came in 2018, covering 27 themes of general repercussion. As of mid 2021, the court's own account says the document splitter and the document classifier (\"classificador de peças\") were still being readied for production with no date set, and that Victor as a whole is \"mesmo ainda não definitivamente em funcionamento\" (still not definitively in operation). Every appeal is analysed by the court's Secretaria de Gestão de Precedentes and decided by the court president; a suggested theme is an indication that the justices always validate or confirm when they actually assess the case.","pilot",2018,[27],[205],"pt",[207],{"kpi":38,"value":208,"unit":209,"qualifier":210,"period":211,"claimant":212,"quote":213,"sourceUrl":59},85,"percent","exact","in tests, not yet in production","organization","Já temos feito testes no Projeto Victor, de inteligência artificial, que identifica os casos de recursos extraordinários ou de agravo em recursos extraordinários com acuidade de 85%",true,[216,219],{"url":59,"title":217,"publisher":218},"Inteligência artificial: Trabalho judicial de 40 minutos pode ser feito em 5 segundos","Supremo Tribunal Federal (STF)",{"url":220,"title":221,"publisher":218,"archivedUrl":222},"https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/","Projeto Victor avança em pesquisa e desenvolvimento para identificação dos temas de repercussão geral","https://web.archive.org/web/20251105220818/https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/",{"level":224,"checkedAt":175},"source-verified","B","supremo-tribunal-federal-victor-appeal-triage",null,{"title":229,"useCases":230,"organization":231,"vendors":234,"summary":239,"stage":240,"year":241,"channels":242,"languages":243,"metrics":245,"outcomeDisclosed":214,"sources":255,"verification":262,"grade":263,"id":264,"organizationSlug":227},"Colombia's Constitutional Court: PretorIA tutela case selection",[186],{"name":232,"anonymized":193,"country":233,"region":195,"industry":18},"Corte Constitucional de Colombia","CO",[235,237],{"name":236,"role":199},"Universidad de Buenos Aires",{"name":238,"role":199},"Universidad El Rosario","PretorIA is an AI system the Colombian Constitutional Court implemented in 2020, inspired by Argentina's Prometea, to help manage the tutela action (Colombia's fast track constitutional complaint for a violation of fundamental rights), of which the court receives over 600,000 a year. It searches, categorises and produces statistics on incoming tutela rulings so staff can identify the cases that raise an important or recurring issue and should be selected for the court's own review; categorisation and statistics were planned to be available initially only for health related rulings. Independent research describes it as functioning like a trained search engine: it does not decide which cases are selected, and every case the tool surfaces is still reviewed and chosen by a person. The court built PretorIA through a public private alliance with Universidad de Buenos Aires and Universidad El Rosario, alongside other supporting institutions.","production",2020,[27],[244],"es",[246],{"kpi":247,"value":248,"unit":249,"qualifier":250,"period":251,"claimant":252,"quote":253,"sourceUrl":254},"interactions-handled",4500,"count","approximately","cases processed per day","independent","PretorIA aids this procedure by processing approximately 4,500 cases daily, helping the Court select which decisions should be reviewed.","https://www.techandjustice.bsg.ox.ac.uk/research/colombia",[256,259],{"url":254,"title":257,"publisher":258},"Colombia: increasing AI use in law enforcement, prosecution, and courts","Oxford Institute of Technology and Justice, University of Oxford",{"url":51,"title":260,"publisher":261},"Pretoria: nueva herramienta para mejorar selección de tutelas en Corte Constitucional","El Tiempo",{"level":224,"checkedAt":175},"C","colombia-constitutional-court-pretoria-tutela-selection",0,[267],{"kpi":38,"label":268,"unit":209,"aggregate":214,"higherIsBetter":214,"n":269,"nUpTo":265,"median":208,"min":208,"max":208,"byClaimant":270,"vendorOnly":193,"points":271},"Accuracy",1,{"organization":269,"vendor":265,"regulator":265,"independent":265},[272],{"evidenceId":226,"organization":192,"value":208,"qualifier":210,"claimant":212,"grade":225,"pooled":214},{"low":274,"high":275},666666.6666666666,15500000,[277,296,318,335],{"slug":174,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":282,"patterns":283,"audience":28,"autonomy":286,"adoptionStage":287,"evidenceCount":288,"publicEvidenceCount":288,"organizations":289,"bestGrade":225,"headline":227,"lastVerified":295,"indexable":214},"AI for court and case file summarization","Case file summarization","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.",[18],[20,21],[284,25,285],"summarization","rag-knowledge-assistant","copilot","early-adopters",6,[290,291,292,293,294,192],"Crown Prosecution Service","U.S. Department of Justice, Civil Division","U.S. Department of Justice","Gemeente Amsterdam","Ministerio Público Fiscal de la Ciudad Autónoma de Buenos Aires","2026-09-27",{"slug":297,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":304,"patterns":305,"audience":28,"autonomy":286,"adoptionStage":306,"evidenceCount":307,"publicEvidenceCount":307,"organizations":308,"bestGrade":225,"headline":313,"lastVerified":295,"indexable":214},"ediscovery-and-disclosure-document-review","AI for eDiscovery and disclosure document review","eDiscovery document review","AI that sorts, prioritises and codes large collections of emails, chats and files for relevance, issues and legal privilege in litigation, investigations and regulatory requests, so that lawyers review the documents most likely to matter and can show the court how the rest were handled.",[302,303,18],"cross-industry","professional-services",[20,21],[23,25,284],"mainstream",5,[292,309,310,311,312],"Federal Trade Commission","Microsoft","Purpose Legal","Serious Fraud Office",{"kpi":314,"label":315,"unit":209,"n":269,"nUpTo":265,"kind":316,"value":208,"qualifier":210,"claimant":317,"organization":311,"vendorReported":214},"processing-time-reduction","Cycle time reduction","reported","vendor",{"slug":319,"title":320,"shortTitle":321,"definition":322,"status":9,"industries":323,"functions":324,"patterns":326,"audience":28,"autonomy":286,"adoptionStage":287,"evidenceCount":288,"publicEvidenceCount":288,"organizations":328,"bestGrade":225,"headline":227,"lastVerified":334,"indexable":214},"freedom-of-information-request-processing","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.",[18],[325,20,21],"citizen-services",[25,23,327],"content-generation",[292,329,330,331,332,333],"U.S. Food and Drug Administration, Center for Drug Evaluation and Research","National Transportation Safety Board","Provincie Noord-Holland","Securities and Exchange Commission","U.S. Department of the Interior","2026-09-26",{"slug":336,"title":337,"shortTitle":338,"definition":339,"status":9,"industries":340,"functions":343,"patterns":346,"audience":348,"autonomy":349,"adoptionStage":306,"evidenceCount":350,"publicEvidenceCount":351,"organizations":352,"bestGrade":225,"headline":363,"lastVerified":295,"indexable":214},"intelligent-document-processing","AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[302,18,341,342],"automotive","manufacturing",[344,21,345],"operations","finance-and-accounting",[25,347,23],"computer-vision","back-office","supervised-agent",12,10,[353,354,355,356,357,358,359,360,361,362],"Ancine","Banorte","CI Financial","Daman (The National Health Insurance Company)","Hirschbach Motor Lines","HM Revenue and Customs","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":314,"label":315,"unit":209,"n":364,"nUpTo":265,"kind":365,"value":56,"qualifier":210,"claimant":317,"organization":227,"vendorReported":214},4,"median",{"indexable":214,"reasons":367},[],[369,374,379,386,393,400,406,412,420,426,433,439,445,451,458,465,471,478,483,489,495,502,507,512,517,523,528,533,539,544,551,557,563,570,575,580],{"id":133,"label":370,"issuer":140,"region":141,"url":371,"description":372,"useCases":373,"indexable":214},"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.",250,{"id":134,"label":375,"issuer":140,"region":141,"url":376,"description":377,"useCases":378,"indexable":214},"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.",223,{"id":135,"label":380,"issuer":381,"region":382,"url":383,"description":384,"useCases":385,"indexable":214},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":136,"label":387,"issuer":388,"region":389,"url":390,"description":391,"useCases":392,"indexable":214},"NIST AI Risk Management Framework","NIST","north-america","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.",95,{"id":394,"label":395,"issuer":396,"region":141,"url":397,"description":398,"useCases":399,"indexable":214},"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.",73,{"id":401,"label":402,"issuer":140,"region":141,"url":403,"description":404,"useCases":405,"indexable":214},"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.",67,{"id":407,"label":408,"issuer":409,"region":141,"url":410,"description":411,"useCases":71,"indexable":214},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",{"id":413,"label":414,"issuer":415,"region":416,"url":417,"description":418,"useCases":419,"indexable":214},"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.",37,{"id":421,"label":422,"issuer":423,"region":416,"url":424,"description":425,"useCases":70,"indexable":214},"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":427,"label":428,"issuer":429,"region":382,"url":430,"description":431,"useCases":432,"indexable":214},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":434,"label":435,"issuer":436,"region":389,"url":437,"description":438,"useCases":432,"indexable":214},"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":440,"label":441,"issuer":140,"region":141,"url":442,"description":443,"useCases":444,"indexable":214},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":446,"label":447,"issuer":448,"region":141,"url":449,"description":450,"useCases":444,"indexable":214},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":452,"label":453,"issuer":454,"region":389,"url":455,"description":456,"useCases":457,"indexable":214},"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.",16,{"id":459,"label":460,"issuer":461,"region":382,"url":462,"description":463,"useCases":464,"indexable":214},"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":466,"label":467,"issuer":140,"region":141,"url":468,"description":469,"useCases":470,"indexable":214},"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":472,"label":473,"issuer":474,"region":389,"url":475,"description":476,"useCases":477,"indexable":214},"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":479,"label":480,"issuer":140,"region":141,"url":481,"description":482,"useCases":477,"indexable":214},"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.",{"id":484,"label":485,"issuer":486,"region":389,"url":487,"description":488,"useCases":477,"indexable":214},"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":490,"label":491,"issuer":492,"region":382,"url":493,"description":494,"useCases":350,"indexable":214},"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":496,"label":497,"issuer":498,"region":389,"url":499,"description":500,"useCases":501,"indexable":214},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":503,"label":504,"issuer":140,"region":141,"url":505,"description":506,"useCases":501,"indexable":214},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":508,"label":509,"issuer":140,"region":141,"url":510,"description":511,"useCases":501,"indexable":214},"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":513,"label":514,"issuer":140,"region":141,"url":515,"description":516,"useCases":501,"indexable":214},"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":518,"label":519,"issuer":520,"region":141,"url":521,"description":522,"useCases":351,"indexable":214},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",{"id":524,"label":525,"issuer":415,"region":416,"url":526,"description":527,"useCases":351,"indexable":214},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":529,"label":530,"issuer":140,"region":141,"url":531,"description":532,"useCases":351,"indexable":214},"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":534,"label":535,"issuer":309,"region":389,"url":536,"description":537,"useCases":538,"indexable":214},"us-fcra","Fair Credit Reporting Act","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":540,"label":541,"issuer":140,"region":141,"url":542,"description":543,"useCases":538,"indexable":214},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":545,"label":546,"issuer":547,"region":548,"url":549,"description":550,"useCases":307,"indexable":214},"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":552,"label":553,"issuer":554,"region":141,"url":555,"description":556,"useCases":364,"indexable":214},"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":558,"label":559,"issuer":560,"region":141,"url":561,"description":562,"useCases":364,"indexable":214},"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":564,"label":565,"issuer":566,"region":416,"url":567,"description":568,"useCases":569,"indexable":214},"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":571,"label":572,"issuer":140,"region":141,"url":573,"description":574,"useCases":569,"indexable":214},"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":576,"label":577,"issuer":140,"region":141,"url":578,"description":579,"useCases":569,"indexable":214},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":581,"label":582,"issuer":583,"region":389,"url":584,"description":585,"useCases":569,"indexable":214},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790783087222]