[{"data":1,"prerenderedAt":581},["ShallowReactive",2],{"uc-police-incident-report-drafting":3,"uc-regulations":357},{"useCase":4,"evidence":187,"blitsAiDeployments":248,"benchmarks":249,"indicative":261,"related":264,"indexability":355,"includeUnpublished":193},{"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":37,"valueDrivers":38,"kpis":41,"indicativeValue":46,"macroEstimates":85,"feasibility":86,"implementation":97,"risk":132,"blitsAi":164,"faq":166,"related":179,"datePublished":182,"dateModified":182,"lastVerified":182,"changelog":183,"slug":186},"AI for police incident report drafting from body worn camera audio","Police report drafting","Police report drafting AI from bodycam audio","AI drafts police reports from body camera audio for an officer to edit. Fort Collins police reported an 82% drop in report writing time using Axon Draft One.","published","An AI tool that turns the transcript of body worn camera audio into a first draft of a police incident report narrative, which the officer who was on the call must review, correct and sign before it becomes part of the official record.",[12,13,14,15],"AI police report writing","body camera report drafting","police narrative generation","automated incident report drafting",[17],"government",[19,20],"case-management","operations",[22,23,24],"speech-analytics","content-generation","summarization",[26],"internal-tools","employee-facing","copilot","early-adopters","Writing the report narrative after a call for service is one of the most time consuming parts of\npatrol work, and it happens after nearly every incident, not just the serious ones. Axon, whose body\nworn cameras and records systems are used by police departments across the US, reports that officers\ncan spend a large share of their working week on this kind of paperwork. Every hour spent typing a\nnarrative is an hour not spent on patrol, an investigation, or simply talking with the community, and\ndepartments already short of officers feel the cost most.\n\nAn AI tool that drafts the narrative from the audio the camera already recorded promises to give that\ntime back, but it raises a real question: who is accountable for what the report says once a machine\nwrote the first version. That promise does not always hold: a peer reviewed study of the Manchester,\nNew Hampshire police department found no time saved once officers' editing time was included, and\nManchester and the Anchorage, Alaska police department both stopped using the tool. The two examples on\nthis page show departments using the tool in practice, and independent reporting on the same product\nshows why the review step has to be genuine, not a formality.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"Axon reports that officers in the United States can spend up to 40% of their time, or 15 hours per week, on paperwork it describes as essentially data entry.","Axon reimagines report writing with Draft One, a first-of-its-kind AI-powered force multiplier for public safety","https://www.prnewswire.com/news-releases/axon-reimagines-report-writing-with-draft-one-a-first-of-its-kind-ai-powered-force-multiplier-for-public-safety-302124489.html",2024,"1. **Capture.** The officer's body worn camera records audio during the call for service or incident,\n   as it already would without the tool.\n2. **Transcribe.** The audio is automatically transcribed once the officer ends or uploads the\n   recording, usually within minutes.\n3. **Draft.** A language model turns the transcript into a narrative in the department's own report\n   format, using only what the audio contains and leaving a visible placeholder wherever information\n   is missing.\n4. **Review against the recording.** The officer reads the draft while checking it against the\n   recording itself, corrects anything wrong, and completes every placeholder.\n5. **Sign and submit.** The officer certifies that the report is accurate before it enters the records\n   management system; only that signed version is the official record.",[39,40],"employee-productivity","speed",[42,43,44,45],"handling-time-reduction","hours-saved","time-saved-per-task","quality-score-uplift",{"referenceOrg":47,"inputs":48,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A police department with 150 sworn officers who write reports",[49,54,61,67,73],{"key":50,"label":51,"low":52,"high":52,"unit":50,"note":53},"officers","Sworn officers who write reports",150,"The reference department, close in size to Rochester Police Department's approximately 160 sworn officers.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"reportsPerOfficerPerWeek","Reports written per officer per week",3,5,"reports per officer per week","Editorial assumption for a mid sized US municipal department. Replace with your own reporting volume.",{"key":62,"label":63,"low":58,"high":64,"unit":65,"note":66},"minutesSavedPerReport","Minutes saved per report",25,"minutes per report","Axon's customer story on Rochester Police Department states that officers save roughly 20 to 25 minutes per report with Draft One, and Fort Collins Police Services (Colorado) separately reported an 82% reduction in report writing time during its trial. Set the low end well under that: a 2024 peer reviewed study of the Manchester, New Hampshire police department found no improvement in report filing time because officers spent significant time editing the drafts, and Manchester and the Anchorage Police Department in Alaska both stopped using Draft One, Anchorage citing zero time savings. Treat this as an uncertain range and validate it against your own department's experience.",{"key":68,"label":69,"low":70,"high":70,"unit":71,"note":72},"weeksPerYear","Working weeks per year",48,"weeks","Standard allowance for leave.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costPerOfficerHour","Fully loaded cost per officer hour",45,75,"USD per hour","Editorial assumption for a US municipal police officer. Replace with your own fully loaded cost.","officers * reportsPerOfficerPerWeek * (minutesSavedPerReport / 60) * weeksPerYear * costPerOfficerHour","USD","per year","Officer time cost avoided on report writing","Gross time value only. It leaves out the cost of the AI service itself, the officer time still needed to review every draft against the recording, and any rework cost from an error that is not caught before the report is signed. It also assumes time is actually saved: at least one department (Manchester, New Hampshire) found none in a peer reviewed study and discontinued the tool, so a department should measure its own result before counting on this range.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":93},"medium","Transcribing audio and drafting a narrative is the easy part. The real work is restricting the tool to a safe set of incident types and charge levels, integrating it with the department's records management system, and building a review process that catches errors before a report is signed, not after.",[90,91,92],"Body worn camera audio with reliable automatic transcription","The department's own report narrative template and writing conventions","A written policy defining which incident types and charge levels the tool may draft for",[94,95,96],"Body worn camera and digital evidence platform","Records management system that holds the official report","Case and charge management system, so charge levels stay linked to what the tool may draft",{"steps":98,"guardrails":114,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":125},[99,102,105,108,111],{"title":100,"detail":101},"Start with minor, low risk incident types","Axon's own default configuration excludes arrests and felony charges from Draft One at launch. Start the same way and expand only once officers and supervisors have reviewed enough drafts to know the pattern of errors the tool makes.",{"title":103,"detail":104},"Keep the model tied to the recording","Draft only from the transcript of the audio actually captured, with no invented detail, and leave a visible placeholder wherever the audio does not cover something the report needs.",{"title":106,"detail":107},"Require a genuine edit, not a rubber stamp","Make officers open, read and complete every placeholder before they can sign, and keep the AI drafted version and the officer's edited version as separate records, so the department can show what was actually reviewed if a report is challenged in court.",{"title":109,"detail":110},"Run your own quality comparison before rollout","Axon compared Draft One narratives with officer only narratives across several quality dimensions using independent reviewers before making claims about quality. Run a similar comparison on your own reports before and after rollout instead of relying only on a vendor's study.",{"title":112,"detail":113},"Train supervisors to sample, not just approve","Have a supervisor review a random sample of signed reports each week against the underlying recording, specifically checking for facts the AI added or missed that the officer did not catch.",[115,116,117,118],"Restricted to a defined list of low risk incident types and charge levels at launch","Officer sign off required before a report enters the records management system","No draft text generated from anything other than the audio transcript","The AI drafted version is kept separate from the officer's edited version, for discovery and audit","The officer who was on the call reviews, completes and signs the report; the tool never submits a report on its own, and the department's written policy states which incident types are eligible for it.",[121,122,123,124],"Minutes saved per report compared with the same report type before the tool","Rate of factual corrections found in supervisor sampling","Share of reports where a placeholder was left unedited before signing, as a warning sign","Prosecutor and defense feedback on report clarity and completeness",[126,129],{"title":127,"detail":128},"A review that does not really review","Reporting on Draft One has found that the product does not store the original AI draft at all, by design, which makes it hard to show what was genuinely reviewed once the officer's edited version is the only copy left. Keep the original AI draft and the edited version as separate, retained records.",{"title":130,"detail":131},"A draft that still needs real correction, not a rubber stamp","A 2024 peer reviewed study of the Manchester, New Hampshire police department found officers had to remove irrelevant information, fix inaccuracies and add facts the AI missed on Draft One narratives, and King County prosecutor Daniel Clark has said he has seen AI written reports get the names of witnesses and officers wrong or place an officer at a scene they only heard over the radio. Require the officer to check the draft against the recording itself, not just read the text for tone and grammar.",{"euAiAct":133,"regulations":136,"guidance":140,"controls":147,"incidents":152},{"tier":134,"basis":135},"limited","A tool that only drafts a narrative for a named officer to review, correct and sign is not listed in Annex III: it does not evaluate the reliability of evidence or a person's risk of offending or reoffending, the law enforcement uses Annex III point 6 covers. Article 50(4)'s disclosure duty for AI generated text does not apply either way, because that duty covers only text published to inform the public on matters of public interest, and an internal police report is not published for that purpose; officer review is not what exempts it. Article 50(2) is the provision that does apply: a provider whose system generates synthetic text from a transcript, rather than only lightly editing text the deployer already supplied, must mark its output in a machine readable, detectable format. That places the tier at limited, not minimal. It would move toward Annex III point 6, and a high tier, if the same kind of system were extended to judge witness credibility or predict reoffending rather than to draft a narrative.",[137,138,139],"eu-ai-act","nist-ai-rmf","iso-42001",[141],{"title":142,"issuer":143,"region":144,"url":145,"note":146},"S.B. 180, Law Enforcement Usage of Artificial Intelligence (Utah, enrolled)","Utah State Legislature","north-america","https://le.utah.gov/Session/2025/bills/enrolled/SB0180.pdf","Requires a law enforcement agency to keep a written policy on generative AI, add a disclaimer to any report created wholly or partly with it, and have the author certify the report was reviewed for accuracy.",[148,149,150,151],"Written agency policy on which report types and charge levels the tool may draft","Disclaimer kept with every report noting it was drafted with AI assistance","Audit trail that keeps the AI draft separate from the officer's edits","Supervisor sampling of signed reports against the underlying recording",[153,157,161],{"title":154,"url":155,"note":156},"Axon's Draft One Is Designed to Defy Transparency","https://www.eff.org/deeplinks/2025/07/axons-draft-one-designed-defy-transparency","The Electronic Frontier Foundation reports that Draft One does not store the original AI draft at all: the officer copies it into the report and it disappears as soon as the browser window closes, by design, which makes it hard for defense lawyers, prosecutors and the public to audit an AI drafted report.",{"title":158,"url":159,"note":160},"Axon Says AI Police Reports Save Time. Public Records Show They Get Facts Wrong","https://www.forbes.com/sites/thomasbrewster/2026/07/22/axon-says-ai-police-reports-save-time-public-records-show-they-get-facts-wrong/","Forbes reported public records showing errors in Axon's separate Form One product, which fills in names, plates and ID details, at the Lafayette, Indiana police department. On Draft One narratives specifically, Forbes cites a 2024 peer reviewed study of the Manchester, New Hampshire police department that found officers had to remove irrelevant information, fix inaccuracies and add missed facts, and quotes King County prosecutor Daniel Clark saying he has seen AI written reports get witness and officer names wrong, which is why a genuine officer check against the recording matters more than the tool itself. forbes.com blocks automated fetches (403); this was checked against the Wayback Machine copy at https://web.archive.org/web/2026/https://www.forbes.com/sites/thomasbrewster/2026/07/22/axon-says-ai-police-reports-save-time-public-records-show-they-get-facts-wrong/.",{"title":162,"url":155,"note":163},"Guardrail bug disclosed to Frederick PD and King County's refusal to accept AI drafted reports","The Electronic Frontier Foundation reports that Axon disclosed to the Frederick Police Department in Colorado that engineers had found a bug that let officers circumvent Draft One's review guardrails on at least three occasions, and that the King County Prosecuting Attorney's Office in Washington has told police \"our office has made the decision not to accept any police narratives that were produced with the assistance of AI.\"",{"howToBuild":165},"On Blits.ai this runs as an agentic workflow: a custom function retrieves the call or incident\ntranscript, a flow checks the incident type and charge level against an allow list before anything is\ndrafted, and an AI agent turns the transcript into a narrative, grounded on the department's own\nreport template loaded into a knowledge base. An output guardrail runs an LLM judged check for\nlanguage the transcript does not support, which reduces, but cannot fully guarantee against, the\nagent adding a detail that is not there.\n\nThe workflow only writes to the records management system through a human in the loop approval step,\nonce a named officer has reviewed and signed the draft, and every run keeps a full audit trail of what\nthe agent produced. PII masking protects victim and witness details in logs, and the platform's model\nagnostic routing lets a department change the underlying model without rebuilding the workflow around\nit. Tracking what an officer changed between the AI draft and the signed report, and instrumenting\nminutes saved per report, both depend on the department's records management system, outside\nBlits.ai.",[167,170,173,176],{"question":168,"answer":169},"Does an AI write the final police report?","No. Every example on this page requires the officer who was on the call to review, correct and sign the draft before it becomes the official report. Axon built Draft One so that every report must be reviewed and approved by a human officer. Axon's customer story on Rochester Police Department describes Draft One as generating \"a first draft from officer interviews/inputs\", and Axon's own product design is built around the officer completing that draft, not a finished report.",{"question":171,"answer":172},"How much time does this actually save?","Public figures vary sharply by department. Fort Collins Police Services (Colorado) reported an 82% decrease in time spent writing reports during its trial, and Rochester Police Department in Minnesota reported saving about 750 officer hours after writing 1,800 reports over two months with Draft One. Neither figure is an independently audited, company wide result: the Fort Collins number is from Axon's own product launch press release and the Rochester number from an Axon customer story. Independent evidence points the other way at some departments: a 2024 peer reviewed study found no improvement in report filing time at the Manchester, New Hampshire police department because officers spent significant time editing the drafts, and Manchester and the Anchorage Police Department in Alaska both stopped using Draft One, Anchorage citing zero time savings.",{"question":174,"answer":175},"Are AI drafted police reports accurate?","Not automatically. Axon's own double blind study found Draft One narratives performed as well as or better than officer only narratives on several quality measures, but independent evidence points the other way on some points: a 2024 peer reviewed study of the Manchester, New Hampshire police department found officers had to remove irrelevant information, fix inaccuracies and add missed facts on Draft One drafts, and King County prosecutor Daniel Clark has said he has seen AI written reports get the names of witnesses and officers wrong. A genuine officer review against the recording matters more than the technology itself.",{"question":177,"answer":178},"Is this high risk under the EU AI Act?","Usually not under Annex III, as long as the tool only drafts a narrative: Annex III's law enforcement category covers systems that evaluate the reliability of evidence or a person's risk of offending or reoffending, not drafting assistance. Separately, the tool's provider likely has to mark the generated text as artificially produced under Article 50(2) of the EU AI Act, which is what puts the system at limited risk rather than minimal, regardless of officer review. It would need Annex III's full high risk governance if the same system judged evidence reliability or predicted reoffending instead of drafting a narrative.",[180,181],"emergency-call-triage-support","non-emergency-service-request-routing","2026-09-29",[184],{"date":182,"note":185},"First published","police-incident-report-drafting",[188,224],{"title":189,"useCases":190,"organization":191,"vendors":195,"summary":199,"stage":200,"year":201,"channels":202,"languages":203,"metrics":205,"outcomeDisclosed":214,"sources":215,"verification":219,"grade":221,"id":222,"organizationSlug":223},"Rochester Police Department (Minnesota): Draft One AI report writing",[186],{"name":192,"anonymized":193,"country":194,"region":144,"industry":17},"Rochester Police Department (Minnesota)",false,"US",[196],{"name":197,"role":198},"Axon","platform","Rochester Police Department in Minnesota, a department of about 160 sworn officers, replaced a 30 year old records system with Axon Records and added Draft One, which Axon's own page describes as generating \"a first draft from officer interviews/inputs\". In an Axon customer story, the captain who led the rollout said the department's officers saved a total of hundreds of hours after writing 1,800 reports over two months with the tool, alongside faster access to linked video evidence.","production",2026,[26],[204],"en",[206],{"kpi":43,"value":207,"unit":208,"qualifier":209,"period":210,"claimant":211,"quote":212,"sourceUrl":213},750,"hours","approximately","over two months (1,800 reports written), as stated by the captain; not an annual figure","organization","If we've written 1,800 reports over the last two months, our officers have saved about 750 hours, and then we can reinvest that time into the community.","https://www.axon.com/resources/how-rochester-mn-pd-modernized-records-evidence-and-reporting",true,[216],{"url":213,"title":217,"publisher":197,"date":218},"How Rochester (MN) PD Modernized records, evidence, and reporting","2026-03-06",{"level":220,"checkedAt":182},"source-verified","C","rochester-police-draft-one-reports",null,{"title":225,"useCases":226,"organization":227,"vendors":229,"summary":231,"stage":232,"year":36,"channels":233,"languages":234,"metrics":235,"outcomeDisclosed":214,"sources":242,"verification":246,"grade":221,"id":247,"organizationSlug":223},"Fort Collins Police Services (Colorado): Draft One AI report writing trial",[186],{"name":228,"anonymized":193,"country":194,"region":144,"industry":17},"Fort Collins Police Services",[230],{"name":197,"role":198},"Fort Collins Police Services (Colorado) tested Axon's Draft One, which turns the transcript of body worn camera audio into a first draft police report narrative for an officer to review and sign. A department sergeant reported a large drop in the time officers spent writing reports during the trial, along with a substantial improvement in report quality, in Axon's own product launch announcement.","pilot",[26],[204],[236],{"kpi":42,"value":237,"unit":238,"qualifier":239,"period":240,"claimant":211,"quote":241,"sourceUrl":35},82,"percent","exact","during the agency's trial of Draft One","Our agency has been testing Draft One, and we have seen an 82% decrease in time spent writing reports.",[243],{"url":35,"title":34,"publisher":244,"date":245},"Axon (PR Newswire)","2024-04-23",{"level":220,"checkedAt":182},"fort-collins-police-draft-one-reports",0,[250,256],{"kpi":42,"label":251,"unit":238,"aggregate":214,"higherIsBetter":214,"n":252,"nUpTo":248,"median":237,"min":237,"max":237,"byClaimant":253,"vendorOnly":193,"points":254},"Handling time reduction",1,{"organization":252,"vendor":248,"regulator":248,"independent":248},[255],{"evidenceId":247,"organization":228,"value":237,"qualifier":239,"claimant":211,"grade":221,"pooled":214},{"kpi":43,"label":257,"unit":208,"aggregate":193,"higherIsBetter":214,"n":252,"nUpTo":248,"median":207,"min":207,"max":207,"byClaimant":258,"vendorOnly":193,"points":259},"Hours saved",{"organization":252,"vendor":248,"regulator":248,"independent":248},[260],{"evidenceId":222,"organization":192,"value":207,"qualifier":209,"claimant":211,"grade":221,"pooled":214},{"low":262,"high":263},81000,1125000,[265,283,312,333],{"slug":180,"title":266,"shortTitle":267,"definition":268,"status":9,"industries":269,"functions":271,"patterns":273,"audience":27,"autonomy":276,"adoptionStage":29,"evidenceCount":57,"publicEvidenceCount":57,"organizations":277,"bestGrade":281,"headline":223,"lastVerified":282,"indexable":214},"AI support for emergency call triage (112 and 911)","Emergency call triage support","AI that supports emergency call takers and dispatchers during 112 and 911 calls, with live transcription, translation, summaries, location cues and alerts for critical conditions such as cardiac arrest, while the call taker keeps every triage and dispatch decision.",[17,270],"healthcare",[272,20],"citizen-services",[22,274,24,275],"translation","prediction-and-scoring","assist",[278,279,280],"Baltimore City 911 (Emergency Communications)","Copenhagen Emergency Medical Services","Galt Police Department","B","2026-09-27",{"slug":181,"title":284,"shortTitle":285,"definition":286,"status":9,"industries":287,"functions":288,"patterns":290,"audience":295,"autonomy":296,"adoptionStage":29,"evidenceCount":297,"publicEvidenceCount":297,"organizations":298,"bestGrade":281,"headline":305,"lastVerified":311,"indexable":214},"AI for non emergency service requests and 311 routing","Non emergency service request routing","An AI agent on a city's 311 style phone, chat and messaging channels that answers routine municipal questions, takes service requests such as potholes, missed collections or broken street lights with the right location and details, creates the case in the work order system and routes anything urgent or complex to the right team.",[17],[272,289,19],"customer-service",[291,292,293,294],"conversational-agent","voice-agent","classification-and-routing","agentic-workflow","customer-facing","supervised-agent",7,[299,300,301,280,302,303,304],"Abu Dhabi Government (TAMM)","London Borough of Barnet","City of Kelowna","Montgomery County Government","Newcastle City Council","Rio de Janeiro City Data Office (Escritório de Dados)",{"kpi":306,"label":307,"unit":238,"n":252,"nUpTo":248,"kind":308,"value":309,"qualifier":239,"claimant":310,"organization":301,"vendorReported":214},"accuracy","Accuracy","reported",80,"vendor","2026-09-26",{"slug":313,"title":314,"shortTitle":315,"definition":316,"status":9,"industries":317,"functions":319,"patterns":321,"audience":27,"autonomy":28,"adoptionStage":29,"segment":320,"evidenceCount":58,"publicEvidenceCount":58,"organizations":324,"bestGrade":281,"headline":330,"lastVerified":282,"indexable":214},"health-prior-authorization-and-claims-adjudication","AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI that reads prior authorization requests, medical claims and appeals with their clinical and billing documents, extracts diagnoses, treatments and costs, checks them against the policy and published clinical criteria, and prepares a summary and recommendation for a clinician or adjudicator, who makes every adverse decision.",[318,270],"insurance",[320,19,20],"claims",[322,24,323,293,23],"document-processing","rag-knowledge-assistant",[325,326,327,328,329],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","ICICI Lombard","Manulife",{"kpi":42,"label":251,"unit":238,"n":331,"nUpTo":248,"kind":308,"value":332,"qualifier":209,"claimant":310,"organization":325,"vendorReported":214},2,50,{"slug":334,"title":335,"shortTitle":336,"definition":337,"status":9,"industries":338,"functions":341,"patterns":342,"audience":343,"autonomy":296,"adoptionStage":344,"segment":343,"evidenceCount":345,"publicEvidenceCount":345,"organizations":346,"bestGrade":281,"headline":353,"lastVerified":282,"indexable":214},"correspondence-triage-and-routing","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.",[339,340,318,17],"cross-industry","banking",[20,289,19],[293,322,24],"back-office","mainstream",6,[347,348,349,350,351,352],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":306,"label":307,"unit":238,"n":252,"nUpTo":248,"kind":308,"value":354,"qualifier":239,"claimant":310,"organization":351,"vendorReported":214},91,{"indexable":214,"reasons":356},[],[358,365,371,378,384,391,397,403,411,417,424,431,437,443,450,457,463,470,476,482,488,495,500,507,512,517,522,528,534,539,546,553,559,565,570,575],{"id":137,"label":359,"issuer":360,"region":361,"url":362,"description":363,"useCases":364,"indexable":214},"EU AI Act","European Union","europe","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.",230,{"id":366,"label":367,"issuer":360,"region":361,"url":368,"description":369,"useCases":370,"indexable":214},"gdpr","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.",207,{"id":139,"label":372,"issuer":373,"region":374,"url":375,"description":376,"useCases":377,"indexable":214},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":138,"label":379,"issuer":380,"region":144,"url":381,"description":382,"useCases":383,"indexable":214},"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.",92,{"id":385,"label":386,"issuer":387,"region":361,"url":388,"description":389,"useCases":390,"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.",71,{"id":392,"label":393,"issuer":360,"region":361,"url":394,"description":395,"useCases":396,"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.",66,{"id":398,"label":399,"issuer":400,"region":361,"url":401,"description":402,"useCases":332,"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":404,"label":405,"issuer":406,"region":407,"url":408,"description":409,"useCases":410,"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":412,"label":413,"issuer":414,"region":407,"url":415,"description":416,"useCases":64,"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":418,"label":419,"issuer":420,"region":144,"url":421,"description":422,"useCases":423,"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.",22,{"id":425,"label":426,"issuer":427,"region":374,"url":428,"description":429,"useCases":430,"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.",21,{"id":432,"label":433,"issuer":360,"region":361,"url":434,"description":435,"useCases":436,"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":438,"label":439,"issuer":440,"region":361,"url":441,"description":442,"useCases":436,"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":444,"label":445,"issuer":446,"region":144,"url":447,"description":448,"useCases":449,"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":451,"label":452,"issuer":453,"region":374,"url":454,"description":455,"useCases":456,"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":458,"label":459,"issuer":360,"region":361,"url":460,"description":461,"useCases":462,"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":464,"label":465,"issuer":466,"region":144,"url":467,"description":468,"useCases":469,"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":471,"label":472,"issuer":473,"region":144,"url":474,"description":475,"useCases":469,"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":477,"label":478,"issuer":360,"region":361,"url":479,"description":480,"useCases":481,"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.",12,{"id":483,"label":484,"issuer":485,"region":374,"url":486,"description":487,"useCases":481,"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":489,"label":490,"issuer":491,"region":144,"url":492,"description":493,"useCases":494,"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":496,"label":497,"issuer":360,"region":361,"url":498,"description":499,"useCases":494,"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":501,"label":502,"issuer":503,"region":361,"url":504,"description":505,"useCases":506,"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.",10,{"id":508,"label":509,"issuer":406,"region":407,"url":510,"description":511,"useCases":506,"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":513,"label":514,"issuer":360,"region":361,"url":515,"description":516,"useCases":506,"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":518,"label":519,"issuer":360,"region":361,"url":520,"description":521,"useCases":506,"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":523,"label":524,"issuer":360,"region":361,"url":525,"description":526,"useCases":527,"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.",9,{"id":529,"label":530,"issuer":531,"region":144,"url":532,"description":533,"useCases":297,"indexable":214},"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.",{"id":535,"label":536,"issuer":360,"region":361,"url":537,"description":538,"useCases":345,"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":540,"label":541,"issuer":542,"region":543,"url":544,"description":545,"useCases":58,"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":547,"label":548,"issuer":549,"region":361,"url":550,"description":551,"useCases":552,"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.",4,{"id":554,"label":555,"issuer":556,"region":361,"url":557,"description":558,"useCases":552,"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":560,"label":561,"issuer":562,"region":407,"url":563,"description":564,"useCases":57,"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.",{"id":566,"label":567,"issuer":360,"region":361,"url":568,"description":569,"useCases":57,"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":571,"label":572,"issuer":360,"region":361,"url":573,"description":574,"useCases":57,"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":576,"label":577,"issuer":578,"region":144,"url":579,"description":580,"useCases":57,"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.",1790683491304]