[{"data":1,"prerenderedAt":601},["ShallowReactive",2],{"uc-freedom-of-information-request-processing":3,"uc-regulations":392},{"useCase":4,"evidence":194,"blitsAiDeployments":287,"benchmarks":288,"indicative":289,"related":292,"indexability":390,"includeUnpublished":199},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":26,"audience":29,"autonomy":30,"adoptionStage":31,"problem":32,"problemStats":33,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":50,"macroEstimates":86,"feasibility":87,"implementation":99,"risk":139,"blitsAi":170,"faq":172,"related":182,"datePublished":188,"dateModified":188,"lastVerified":189,"changelog":190,"slug":193},"AI for freedom of information request processing","Freedom of information requests","AI for FOI and FOIA request processing","AI groups similar FOI requests, deduplicates records and proposes redactions for officers to review, as at the US FDA, the Interior Department and North Holland.","published","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.",[12,13,14,15],"FOIA processing AI","AI redaction for FOI requests","open government request handling","public records request automation",[17],"government",[19,20,21],"citizen-services","legal","case-management",[23,24,25],"document-processing","classification-and-routing","content-generation",[27,28],"internal-tools","email","employee-facing","copilot","early-adopters","Freedom of information laws give everyone the right to ask for government records, and request\nvolumes keep rising. Each request means finding every relevant record across email, file shares and\ncase systems, removing duplicates, reading everything, redacting personal data and exempt material,\nand explaining the decision within a statutory deadline. Large requests can involve very large\ndocument sets, and similar requests can reach several offices of the same government body.\n\nThe work is mostly manual and legal in nature, so backlogs grow and deadlines are missed, which\nundermines the transparency the law is meant to deliver. Errors cut both ways: over redaction\nwithholds information the public is entitled to, and under redaction leaks personal data.",[34],{"statement":35,"sourceTitle":36,"sourceUrl":37,"year":38},"US federal agencies received a record 1,707,197 FOIA requests in fiscal year 2025, 13.7% more than the year before, and ended the year with 339,671 backlogged requests, a 27% increase.","2025 Annual FOIA Report Summary","https://www.justice.gov/oip/media/1450791/dl?inline",2026,"1. **Log and clarify.** Incoming requests are read, logged with key fields and compared with open and\n   past requests, so similar requests are grouped and answered consistently. Unclear requests get a\n   drafted clarification question.\n2. **Collect and cull.** Records gathered under the search plan are made searchable (including text\n   recognition for scans), deduplicated and grouped by topic so reviewers see what is relevant first.\n3. **Propose redactions.** Named entity recognition and trained models mark personal data and other\n   candidate redactions, each with the proposed exemption code.\n4. **Review and decide.** FOI officers and lawyers accept, change or reject every proposed\n   redaction and decide what is released, with a second reviewer for doubtful cases.\n5. **Draft the response.** The response letter, including the exemptions relied on and appeal\n   rights, is drafted from the decision record for the officer to finalise.",[41,42,43,44],"employee-productivity","speed","compliance","inclusion-and-access",[46,47,48,49],"processing-time-reduction","hours-saved","cycle-time-days","accuracy",{"referenceOrg":51,"inputs":52,"formula":81,"currency":82,"period":83,"resultLabel":84,"caveat":85},"A government department that receives 5,000 information requests a year",[53,60,67,74],{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"requests","Requests per year",3000,7000,"requests per year","Editorial assumption. Replace with your own request log.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"hoursPerRequest","Staff hours per request today",5,12,"hours per request","Editorial assumption covering search, review, redaction and response. Large requests take far longer.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"savedShare","Share of those hours saved",0.15,0.3,"fraction of hours","Editorial assumption. No agency on this page publishes a measured saving; legal review remains human work.",{"key":75,"label":76,"low":77,"high":78,"unit":79,"note":80},"hourlyCost","Fully loaded cost of an FOI officer's hour",40,60,"EUR per hour","Editorial assumption. Replace with your own staff cost.","requests * hoursPerRequest * savedShare * hourlyCost","EUR","per year","FOI officer time released","Time released, not cash saved. It leaves out licence and assurance costs, the value of meeting statutory deadlines, and the cost of a redaction error, which can be far larger than the saving.",[],{"complexity":88,"complexityNote":89,"dataPrerequisites":90,"integrations":94},"medium","Document review and redaction platforms are established products (the Province of North Holland has used one since 2021); the effort is in connecting to the places records live, tuning redaction to the agency's exemptions and building a review workflow that lawyers trust.",[91,92,93],"Request log with past requests, decisions and exemptions applied","Access to the record stores searched for requests (email, file shares, case systems)","Written redaction rules per exemption, with examples",[95,96,97,98],"FOI case management or request tracking system","eDiscovery or document review platform","Email and records management systems","Public disclosure log or reading room for published responses",{"steps":100,"guardrails":113,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[101,104,107,110],{"title":102,"detail":103},"Start with deduplication and grouping","Grouping similar requests and exact duplicate documents is a lower risk place to start, because no redaction or release decision is automated. Check near duplicate removal with care, since versions that differ can both be responsive. The Department of the Interior's Office of the Solicitor has used request similarity and clustering tools since 2023 to coordinate answers to similar requests.",{"title":105,"detail":106},"Introduce proposed redactions with full review","Let the tool propose redactions with the redaction code for each, as FDA's FRED tool does, while officers still review and approve every proposal. Measure how often proposals are changed.",{"title":108,"detail":109},"Tune rules to your exemptions","Configure generic patterns (phone numbers, national identifiers) and individual rules for names, as the Province of North Holland does.",{"title":111,"detail":112},"Draft the response letter last","Once decisions are recorded per document, generate the response letter from them, so the letter matches what was actually decided.",[114,115,116,117,118],"Every redaction and release decision is taken by an FOI officer, never by the tool","Second review for documents where the officer is in doubt","Redaction burned into the released file, with the original kept securely","Personal data in request logs and prompts masked and retained only as the law allows","Search plan documented, so the scope of records is defensible","FOI officers and lawyers own the search plan, review every proposed redaction and decide what is released. A second reviewer or team lead checks doubtful documents. Requesters keep their complaint and appeal rights.",[121,122,123,124,125],"Median days from request to response and share within the statutory deadline","Backlog of open requests","Share of proposed redactions changed by reviewers","Redaction errors found after release","Appeals upheld against over redaction",[127,130,133,136],{"title":128,"detail":129},"Missed personal data","A name in an image, a signature or an unusual format is not recognised and is released. Reviewers must check every page, and scanned material needs extra care.",{"title":131,"detail":132},"Over redaction by default","Accepting every proposal withholds information that should be public. Track changed proposals and appeals.",{"title":134,"detail":135},"Incomplete search","AI speeds up review but does not fix a search that missed a record store. Keep the search plan explicit.",{"title":137,"detail":138},"Redaction that can be undone","Visual boxes over text that can still be copied from the file. Use tools that remove the underlying text.",{"euAiAct":140,"regulations":143,"guidance":150,"controls":163,"incidents":169},{"tier":141,"basis":142},"minimal","Tools that support staff in searching, deduplicating and proposing redactions are not listed in Annex III (point 5(a) covers eligibility for public assistance benefits and services, not access to documents), and every release decision stays with an officer. A public facing request assistant that talks to requesters would carry the Article 50(1) transparency duty.",[144,145,146,147,148,149],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[151,157],{"title":152,"issuer":153,"region":154,"url":155,"note":156},"Algoritmeregister van de Nederlandse overheid","Government of the Netherlands","europe","https://algoritmes.overheid.nl/nl","Dutch public bodies register the tools they use to support open government requests, including how staff check proposed redactions.",{"title":158,"issuer":159,"region":160,"url":161,"note":162},"US Department of Justice, Office of Information Policy: annual FOIA report summaries","U.S. Department of Justice","north-america","https://www.justice.gov/oip/reports-1","Government wide request volumes, processing times and backlogs, the baseline against which improvements should be measured.",[164,165,166,167,168],"Documented redaction rules per exemption and a review workflow with sign off","Audit trail of proposed and final redactions per document","Quality sampling of released documents for missed personal data","Access controls on the record sets gathered for each request","Register or inventory entry for each AI tool used",[],{"howToBuild":171},"Redaction itself is best done in a specialist document review platform, called through **custom\nfunctions**. Blits.ai adds the request handling around it: an **agentic workflow** that reads a new\nrequest from the **email channel**, logs it, compares it with past requests stored in a **SQL\nknowledge base**, drafts a clarification question where needed and, once officers have recorded\ntheir decisions, drafts the response letter, with **human in the loop approval** at each step.\n\nA **knowledge base** with hybrid retrieval over the FOI law, exemption guidance and past decision\nletters helps officers apply exemptions consistently. **PII masking** at the gateway keeps\nrequester data out of prompts and logs, every run keeps a full audit trail, and EU and UAE data\nresidency keeps records in region.",[173,176,179],{"question":174,"answer":175},"Can AI redact documents for FOI requests?","It can propose redactions. FDA's FRED tool marks the text it recommends redacting with a redaction code, and the Province of North Holland uses general rules that recognise phone and citizen service numbers, while names need their own individual rules. In both cases staff review the proposals and decide; accountability stays with the officer.",{"question":177,"answer":178},"Where does AI help most in FOI work?","In the volume steps: grouping similar requests, deduplicating and sorting records, and proposing redactions of personal data. Judgment on exemptions and the public interest remains human work.",{"question":180,"answer":181},"Is AI for FOI processing high risk under the EU AI Act?","No. Handling requests for access to documents is not listed in Annex III, and good practice keeps every release decision with an officer. If you add a chatbot that talks to requesters, it must tell them they are dealing with AI (Article 50).",[183,184,185,186,187],"correspondence-triage-and-routing","civil-servant-drafting-copilot","court-and-case-file-summarization","enterprise-knowledge-search","intelligent-document-processing","2026-09-27","2026-09-26",[191],{"date":188,"note":192},"First published","freedom-of-information-request-processing",[195,229,249,266],{"title":196,"useCases":197,"organization":198,"vendors":201,"summary":212,"stage":213,"year":214,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":199,"sources":219,"verification":224,"grade":226,"id":227,"organizationSlug":228},"US Department of Justice: AI features in FOIA production tools for classification, redaction and deduplication",[193],{"name":159,"anonymized":199,"country":200,"region":160,"industry":17},false,"US",[202,205,208,210],{"name":203,"role":204},"FOIAXpress","platform",{"name":206,"role":207},"Forum One","integrator",{"name":209,"role":204},"Adobe",{"name":211,"role":207},"Polydelta","The Department of Justice reports a department wide entry for FOIA production tools, deployed in January 2025, that add AI to its FOIAXpress request processing: classifying documents, identifying sensitive or confidential information for redaction and removing duplicate documents. The outputs are classifications, recommendations and predictions, and the department rates the use as not high impact because it is not the principal basis for decisions with legal or significant effect. The expected benefits are faster processing, fewer human errors and more accurate, compliant processing; no measured outcome is published.","production",2025,[27],[217],"en",[],[220],{"url":221,"title":222,"publisher":223},"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":225,"checkedAt":189},"source-verified","B","doj-foia-production-tools","u-s-department-of-justice",{"title":230,"useCases":231,"organization":232,"vendors":234,"summary":240,"stage":213,"year":214,"channels":241,"languages":242,"metrics":243,"outcomeDisclosed":199,"sources":244,"verification":246,"grade":226,"id":247,"organizationSlug":248},"US Food and Drug Administration: FRED tool that proposes FOIA redactions",[193],{"name":233,"anonymized":199,"country":200,"region":160,"industry":17},"U.S. Food and Drug Administration, Center for Drug Evaluation and Research",[235,238],{"name":236,"role":237},"U.S. Food and Drug Administration","in-house",{"name":239,"role":207},"Contractor teams (not named)","FDA's Center for Drug Evaluation and Research uses the FOIA Redaction (FRED) tool, a generative AI system built by FDA and contractor teams, to help FOIA staff redact records more efficiently and consistently, because redaction is time consuming and FOIA backlogs build up. The tool returns a PDF with boxes around the text it recommends redacting, each with a comment giving the redaction code. Its data are completed FDA Form 483 inspection records in their original and staff redacted versions, and every output needs human review and approval. Listed as deployed since May 2025; no outcome figures are published.",[27],[217],[],[245],{"url":221,"title":222,"publisher":223},{"level":225,"checkedAt":189},"fda-foia-redaction-tool",null,{"title":250,"useCases":251,"organization":252,"vendors":254,"summary":257,"stage":213,"year":258,"channels":259,"languages":260,"metrics":261,"outcomeDisclosed":199,"sources":262,"verification":264,"grade":226,"id":265,"organizationSlug":248},"US Department of the Interior: clustering and similarity tools for Freedom of Information Act requests",[193],{"name":253,"anonymized":199,"country":200,"region":160,"industry":17},"U.S. Department of the Interior, Office of the Solicitor",[255],{"name":256,"role":237},"U.S. Department of the Interior","The Department of the Interior's Office of the Solicitor lists four deployed tools, developed in house, that group incoming FOIA requests: two clustering tools (one embedding based, one density based on term frequency, run in its document review platform), a semantic similarity score and a lexical similarity tool. They identify requests that ask for the same or similar records, including similar requests sent to several offices, so that the work can be coordinated and responses kept uniform instead of duplicated. The inventory gives operational dates of August and November 2023; no outcome figures are published.",2023,[27],[217],[],[263],{"url":221,"title":222,"publisher":223},{"level":225,"checkedAt":189},"us-department-of-the-interior-foia-request-similarity-tools",{"title":267,"useCases":268,"organization":269,"vendors":272,"summary":275,"stage":213,"year":276,"channels":277,"languages":278,"metrics":280,"outcomeDisclosed":199,"sources":281,"verification":285,"grade":226,"id":286,"organizationSlug":248},"Province of North Holland: document analysis and redaction support for open government requests",[193],{"name":270,"anonymized":199,"country":271,"region":154,"industry":17},"Provincie Noord-Holland","NL",[273],{"name":274,"role":204},"ZyLAB (Reveal)","The Province of North Holland uses ZyLAB to handle large requests under the Dutch Open Government Act (Woo). After staff draw up a search plan and collect the potentially relevant documents, the platform makes the set searchable, including text recognition for scanned documents, helps staff judge relevance and, on instruction, produces a trial redacted version: generic rules recognise items such as phone and citizen service numbers, while names need their own individual rules, and staff switch the rules on themselves. Staff check every document to avoid too much or too little redaction, and what is released is agreed between the responsible staff and lawyers. In use since October 2021.",2021,[27],[279],"nl",[],[282],{"url":283,"title":284,"publisher":152},"https://algoritmes.overheid.nl/nl/algoritme/pv27/86188119/ondersteuning-openbaarmakingsverzoeken","Ondersteuning Openbaarmakingsverzoeken, Algoritmeregister",{"level":225,"checkedAt":188},"provincie-noord-holland-woo-request-support",0,[],{"low":290,"high":291},90000,1512000,[293,326,342,353,369],{"slug":183,"title":294,"shortTitle":295,"definition":296,"status":9,"industries":297,"functions":301,"patterns":304,"audience":306,"autonomy":307,"adoptionStage":308,"segment":306,"evidenceCount":309,"publicEvidenceCount":309,"organizations":310,"bestGrade":226,"headline":317,"lastVerified":188,"indexable":325},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[298,299,300,17],"cross-industry","banking","insurance",[302,303,21],"operations","customer-service",[24,23,305],"summarization","back-office","supervised-agent","mainstream",6,[311,312,313,314,315,316],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":49,"label":318,"unit":319,"n":320,"nUpTo":287,"kind":321,"value":322,"qualifier":323,"claimant":324,"organization":315,"vendorReported":325},"Accuracy","percent",1,"reported",91,"exact","vendor",true,{"slug":184,"title":327,"shortTitle":328,"definition":329,"status":9,"industries":330,"functions":331,"patterns":333,"audience":29,"autonomy":30,"adoptionStage":335,"evidenceCount":63,"publicEvidenceCount":63,"organizations":336,"bestGrade":226,"headline":248,"lastVerified":188,"indexable":325},"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],[19,332,21],"knowledge-management",[25,334,305],"rag-knowledge-assistant","emerging",[337,338,339,340,341],"Cabinet Office (Government Communication Service)","Crown Prosecution Service","Department for Education","Department for Science, Innovation and Technology (Incubator for Artificial Intelligence)","Government Digital Service",{"slug":185,"title":343,"shortTitle":344,"definition":345,"status":9,"industries":346,"functions":347,"patterns":348,"audience":29,"autonomy":30,"adoptionStage":31,"evidenceCount":349,"publicEvidenceCount":349,"organizations":350,"bestGrade":226,"headline":248,"lastVerified":188,"indexable":325},"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.",[17],[20,21],[305,23,334],4,[338,159,351,352],"Gemeente Amsterdam","Supremo Tribunal Federal",{"slug":186,"title":354,"shortTitle":355,"definition":356,"status":9,"industries":357,"functions":360,"patterns":361,"audience":29,"autonomy":363,"adoptionStage":308,"evidenceCount":349,"publicEvidenceCount":349,"organizations":364,"bestGrade":226,"headline":248,"lastVerified":188,"indexable":325},"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.",[298,299,358,300,17,359],"wealth-and-asset-management","professional-services",[332,302,303],[334,362,305],"conversational-agent","assist",[365,366,367,368],"Bank of America","Morgan Stanley","SIGNAL IDUNA","Wells Fargo",{"slug":187,"title":370,"shortTitle":371,"definition":372,"status":9,"industries":373,"functions":376,"patterns":378,"audience":306,"autonomy":307,"adoptionStage":308,"evidenceCount":380,"publicEvidenceCount":63,"organizations":381,"bestGrade":226,"headline":387,"lastVerified":188,"indexable":325},"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.",[298,17,374,375],"automotive","manufacturing",[302,21,377],"finance-and-accounting",[23,379,24],"computer-vision",7,[382,383,384,385,386],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":49,"label":318,"unit":319,"n":320,"nUpTo":287,"kind":321,"value":388,"qualifier":389,"claimant":324,"organization":382,"vendorReported":325},90,"at-least",{"indexable":325,"reasons":391},[],[393,399,404,411,417,423,429,436,444,451,458,464,470,477,483,488,495,500,506,512,518,524,530,535,540,547,554,559,564,571,577,583,590,595],{"id":144,"label":394,"issuer":395,"region":154,"url":396,"description":397,"useCases":398,"indexable":325},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":145,"label":400,"issuer":395,"region":154,"url":401,"description":402,"useCases":403,"indexable":325},"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":147,"label":405,"issuer":406,"region":407,"url":408,"description":409,"useCases":410,"indexable":325},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":146,"label":412,"issuer":413,"region":160,"url":414,"description":415,"useCases":416,"indexable":325},"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":418,"label":419,"issuer":395,"region":154,"url":420,"description":421,"useCases":422,"indexable":325},"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":148,"label":424,"issuer":425,"region":154,"url":426,"description":427,"useCases":428,"indexable":325},"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":430,"label":431,"issuer":432,"region":154,"url":433,"description":434,"useCases":435,"indexable":325},"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":437,"label":438,"issuer":439,"region":440,"url":441,"description":442,"useCases":443,"indexable":325},"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":445,"label":446,"issuer":447,"region":440,"url":448,"description":449,"useCases":450,"indexable":325},"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":452,"label":453,"issuer":454,"region":407,"url":455,"description":456,"useCases":457,"indexable":325},"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":459,"label":460,"issuer":461,"region":160,"url":462,"description":463,"useCases":457,"indexable":325},"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":149,"label":465,"issuer":466,"region":154,"url":467,"description":468,"useCases":469,"indexable":325},"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":471,"label":472,"issuer":473,"region":407,"url":474,"description":475,"useCases":476,"indexable":325},"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":478,"label":479,"issuer":395,"region":154,"url":480,"description":481,"useCases":482,"indexable":325},"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":484,"label":485,"issuer":395,"region":154,"url":486,"description":487,"useCases":482,"indexable":325},"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":489,"label":490,"issuer":491,"region":160,"url":492,"description":493,"useCases":494,"indexable":325},"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":496,"label":497,"issuer":395,"region":154,"url":498,"description":499,"useCases":64,"indexable":325},"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":501,"label":502,"issuer":503,"region":160,"url":504,"description":505,"useCases":64,"indexable":325},"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":507,"label":508,"issuer":509,"region":407,"url":510,"description":511,"useCases":64,"indexable":325},"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":513,"label":514,"issuer":395,"region":154,"url":515,"description":516,"useCases":517,"indexable":325},"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":519,"label":520,"issuer":521,"region":160,"url":522,"description":523,"useCases":517,"indexable":325},"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":525,"label":526,"issuer":439,"region":440,"url":527,"description":528,"useCases":529,"indexable":325},"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":531,"label":532,"issuer":395,"region":154,"url":533,"description":534,"useCases":529,"indexable":325},"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":536,"label":537,"issuer":395,"region":154,"url":538,"description":539,"useCases":529,"indexable":325},"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":541,"label":542,"issuer":543,"region":154,"url":544,"description":545,"useCases":546,"indexable":325},"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":548,"label":549,"issuer":550,"region":160,"url":551,"description":552,"useCases":553,"indexable":325},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":555,"label":556,"issuer":395,"region":154,"url":557,"description":558,"useCases":553,"indexable":325},"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":560,"label":561,"issuer":395,"region":154,"url":562,"description":563,"useCases":309,"indexable":325},"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":565,"label":566,"issuer":567,"region":568,"url":569,"description":570,"useCases":63,"indexable":325},"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":572,"label":573,"issuer":574,"region":154,"url":575,"description":576,"useCases":349,"indexable":325},"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":578,"label":579,"issuer":580,"region":154,"url":581,"description":582,"useCases":349,"indexable":325},"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":584,"label":585,"issuer":586,"region":440,"url":587,"description":588,"useCases":589,"indexable":325},"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":591,"label":592,"issuer":395,"region":154,"url":593,"description":594,"useCases":589,"indexable":325},"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":596,"label":597,"issuer":598,"region":160,"url":599,"description":600,"useCases":589,"indexable":325},"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.",1790598299998]