[{"data":1,"prerenderedAt":555},["ShallowReactive",2],{"uc-automated-sports-highlights-and-clipping":3,"uc-regulations":334},{"useCase":4,"evidence":174,"blitsAiDeployments":234,"benchmarks":235,"indicative":242,"related":245,"indexability":332,"includeUnpublished":180},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":24,"audience":25,"autonomy":26,"adoptionStage":27,"segment":28,"problem":29,"problemStats":30,"howItWorks":31,"valueDrivers":32,"kpis":36,"indicativeValue":41,"macroEstimates":82,"feasibility":83,"implementation":95,"risk":130,"blitsAi":150,"faq":152,"related":165,"datePublished":169,"dateModified":169,"lastVerified":169,"changelog":170,"slug":173},"AI agent for automated sports highlights and clipping","Sports highlights and clipping","Automated sports highlight clip generator","AI watches live broadcasts and cuts highlight clips automatically. AWS built a Fox Sports prototype that clips in 20 to 30 seconds; the NBA makes 1,000+ in minutes.","published","An AI system that watches a live sports broadcast, detects key moments such as goals, saves or penalties as they happen, automatically extracts and reformats a clip, and puts it in front of an editor to review and publish, instead of a person watching every feed and clipping moments by hand.",[12,13,14],"AI sports highlight generator","automated sports clipping","live sports clip detection",[16],"media-and-entertainment",[18,19],"operations","marketing",[21,22,23],"computer-vision","classification-and-routing","content-generation",[],"employee-facing","copilot","mainstream","content operations","Broadcasters run hundreds of live events across multiple leagues with a digital production team\nthat cannot watch every feed at once, let alone clip, reformat and publish a moment before it stops\nbeing timely. Fox Sports says nearly 90% of its Digital content is consumed vertically, yet\nthe traditional workflow, monitoring broadcasts across multiple screens, manually identifying key\nmoments, clipping and reformatting them for vertical social platforms, does not scale to that volume\nwithout automation.\n\nThe moments still happen whether or not a broadcaster has the staff to capture them: a goal, a save,\na controversial call. Without a scalable way to detect and clip those moments as they happen, the\nwindow to reach fans on social platforms in near real time closes, and the opportunity to grow\naudience and engagement around that moment goes unrealized.",[],"1. **Analyse the live feed continuously.** Computer vision and audio analysis watch the broadcast\n   stream in real time and detect key moments, such as a goal or a celebration, within seconds of\n   them happening on air.\n2. **Harvest the clip automatically.** A detection event triggers a pipeline that extracts the\n   matching segment from a rolling buffer of the live stream and reformats it, for example cropped\n   to 9:16 vertical video for social platforms.\n3. **Surface it for review.** The clip appears automatically in a web portal with AI generated tags\n   and a short description, so an editor can find, search and filter incoming clips as the broadcast\n   continues.\n4. **Edit and assemble.** Editors trim, merge or discard clips in a visual timeline, and can combine\n   clips from across a broadcast into a highlight reel.\n5. **Distribute.** Finished clips and reels are published to the organization's fan facing channels;\n   editor feedback on clip quality feeds back into tuning what the system detects.",[33,34,35],"speed","employee-productivity","revenue-growth",[37,38,39,40],"interactions-handled","processing-time-reduction","productivity-gain","revenue-uplift",{"referenceOrg":42,"inputs":43,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A regional sports broadcaster covering 200 live events a year",[44,50,57,63,70],{"key":45,"label":46,"low":47,"high":47,"unit":48,"note":49},"liveEvents","Live events covered per year",200,"events per year","The reference broadcaster.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"clipsPerEvent","Highlight clips produced per event",20,40,"clips per event","Editorial assumption based on a typical number of clippable moments per match. Replace with your own.",{"key":58,"label":59,"low":60,"high":53,"unit":61,"note":62},"editorMinutesPerClipManual","Editor minutes to find, cut and format one clip by hand",10,"minutes per clip","Editorial assumption for manual clipping and reformatting from a live feed. Replace with your own time study.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"automationShare","Share of that time an automated detection and clipping pipeline removes",0.5,0.8,"fraction of editor time per clip","Editorial assumption, conservative against the evidence on this page (AWS describes a prototype it built for Fox Sports that surfaces clips in a review portal within 20 to 30 seconds of the moment occurring on air, and WSC Sports reports the NBA produces over 1,000 highlight packages in a few minutes), because neither source states a prior manual baseline in minutes per clip that this share can be checked against directly.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"editorCostPerHour","Fully loaded cost of a video editor",25,50,"USD per hour","Editorial assumption for a broadcaster digital production role. Replace with your own.","liveEvents * clipsPerEvent * editorMinutesPerClipManual * automationShare / 60 * editorCostPerHour","USD","per year","Editor time cost avoided on live clipping","Gross editor time cost avoided only. It leaves out the cost of the AI service and integration, the editor time still spent reviewing and publishing every clip, and any extra advertising or engagement revenue faster clips may generate.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":90},"medium","Detecting a key moment in a live video feed and turning it into a broadcast quality vertical clip within seconds needs a low latency video pipeline, ingest, buffering, inference and transcoding, alongside the detection model itself. The harder work is usually the live production integration and the review portal, not the detection model.",[87,88,89],"A live video feed with enough delay or a rolling buffer to extract clips from","A taxonomy of the moments worth clipping per sport, such as a goal, a penalty or a celebration","Historical footage labelled with those moments, to train or tune detection",[91,92,93,94],"Live video ingest and packaging (encoder, rolling buffer)","Video transcoding for vertical reformatting and multiple output profiles","A review and editing portal for the digital production team","Distribution endpoints for social and app publishing",{"steps":96,"guardrails":112,"humanInTheLoop":117,"kpisToInstrument":118,"failureModes":123},[97,100,103,106,109],{"title":98,"detail":99},"Start with one sport and a short list of moments","Pick the highest volume sport or league and define which moment types matter, such as a goal or a red card, before expanding detection to other sports.",{"title":101,"detail":102},"Build the live pipeline before the model","Get ingest, the rolling buffer and transcoding solid on real broadcast feeds first; the detection model only creates value if a clip can actually be produced within the broadcast window.",{"title":104,"detail":105},"Put a review portal in front of every clip","No clip reaches a fan facing or social channel without an editor confirming it, tagging it correctly and checking it matches the moment.",{"title":107,"detail":108},"Measure against the manual process it replaces","Track how many clips a human team produced per event before automation, and compare volume and editor time after.",{"title":110,"detail":111},"Expand sport by sport","Each sport has different moments and pacing. Tune detection and add new moment types deliberately rather than assuming one model covers every sport equally well.",[113,114,115,116],"Editor review before any AI detected clip reaches a fan facing or social channel","A fixed, agreed list of moment types the system may detect, so it does not invent unofficial moments","Rights and licensing checks before clips leave the organization's own channels, since a highlight is still copyrighted broadcast content","Clear internal labelling of which clips were AI detected, so quality issues can be traced back to the pipeline","Editors review every AI detected clip in the portal before it is published, decide which clips become part of a highlight reel, and can trim, merge or discard a clip that was wrongly tagged or badly framed.",[119,120,121,122],"Clips produced per live event, before and after","Time from the moment happening to the clip being ready for review","Editor time spent per clip, before and after","Share of AI detected clips an editor rejects or heavily edits",[124,127],{"title":125,"detail":126},"Missed or duplicate moments","A fast paced passage of play can be detected twice or missed entirely if the model's moment definitions are too broad or too narrow. Track detection precision and recall against a labelled sample of real broadcasts, not just the volume of clips produced.",{"title":128,"detail":129},"Clips leave the review portal unpublished","A high clip count looks like an automation win in a dashboard, but only if the clips get published. Instrument publish rate per event, not only clips detected.",{"euAiAct":131,"regulations":134,"guidance":136,"controls":143,"incidents":149},{"tier":132,"basis":133},"minimal","Detecting a sporting moment in video the broadcaster already owns the rights to, and cutting or reframing a clip from that real footage, is not listed in Annex III, so it stays minimal risk. The system AWS describes does generate synthetic text: AI generated tags and a short AI generated description of each moment for the internal review portal. That falls inside Article 50(2)'s scope for synthetic content, but the marking duty there sits with the AI system's provider, and the outputs stay internal for an editor to check before anything reaches a fan facing channel, so the practical exposure stays minimal. Cropping real footage to a vertical frame is standard editing, not the kind of content generation Article 50 targets.",[135],"eu-ai-act",[137],{"title":138,"issuer":139,"region":140,"url":141,"note":142},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Article 50 covers several separate duties: 50(1) disclosure when a person interacts with an AI system, 50(2) marking AI generated or manipulated synthetic audio, image, video or text so it is machine readable and detectable, 50(3) informing people exposed to emotion recognition or biometric categorisation, and 50(4) labelling image, audio or video content that is a deepfake, or AI generated text published to inform the public on matters of public interest, unless it goes through human editorial review and control. The AI generated tags and moment descriptions in this use case fall under 50(2), but that duty sits with the provider and the outputs stay internal to the review portal rather than reaching fans. Reformatting real footage into a vertical crop is standard editing, not synthetic generation. This would change if a deployment started publishing AI generated descriptions or commentary directly to fans, or generated synthetic video or voice rather than only extracting real broadcast footage; that would trigger 50(2) or 50(4) directly for the deployer.",[144,145,146,147,148],"Editor sign off before publication of any AI detected clip","A documented, agreed list of moment types the model may detect","Rights and licensing review before any clip leaves the organization's own channels","Regular sampling of published clips against the source broadcast for accuracy","AI generated tags and moment descriptions stay internal to the review portal; only text an editor writes or approves reaches fans",[],{"howToBuild":151},"Blits.ai does not itself watch a live video feed and detect a sporting moment; that step calls an\nexternal computer vision or media processing service through a **custom function**, the same way\nthe platform integrates any specialised model it does not run itself. What Blits.ai adds is the\nworkflow around that detection: an **agentic workflow with human in the loop approval** turns every\ndetected moment into a task that waits for an editor's decision before a clip is published, rather\nthan a script that publishes automatically.\n\nA practical build is an internal **AI agent** for the digital production team: **custom functions**\ncall the detection and clipping pipeline and pull back each candidate clip with its tags, the agent\npresents them for approval inside an internal tool, and an editor approves, edits or rejects in\nconversation. **Agent instructions** keep the agent to the agreed list of moment types, the **run\nhistory and audit trail** record every approval decision, and **per bot analytics** show clip\nvolume and approval activity over time, so the production team can see where automation is and is\nnot keeping up with a live broadcast.",[153,156,159,162],{"question":154,"answer":155},"How fast can AI turn a live sports moment into a clip?","AWS describes a prototype it built for Fox Sports that detects a key moment within single digit seconds and surfaces the finished, reformatted clip in a review portal within 20 to 30 seconds of the moment happening on air. AWS says it plans to publish a reference implementation; how a production deployment performs depends on the live video pipeline's own latency, not only the detection model.",{"question":157,"answer":158},"Does automated clipping replace video editors?","Not at Fox Sports. AWS describes a portal where Fox Sports editors review, search, tag, edit and distribute clips before anything reaches a fan facing channel. WSC Sports reports that automation lets the NBA produce over 1,000 highlight packages in a few minutes, but the NBA quote we found says nothing about editorial review, so we cannot say whether or how the NBA reviews clips before publishing them.",{"question":160,"answer":161},"Is automated sports highlight clipping high risk under the EU AI Act?","Usually not. Extracting or reframing a clip from a broadcast the organization already owns the rights to is not an Annex III use, so this stays minimal risk. The AI generated tags and moment descriptions used inside the review portal fall under Article 50(2)'s synthetic content marking duty, but that duty sits with the AI system's provider, and the outputs never reach fans directly. It would raise the risk if a deployment started publishing AI generated text, video or audio to fans rather than only feeding it to editors reviewing real footage.",{"question":163,"answer":164},"Which sports and leagues use this today?","AWS names Fox Sports for live vertical clipping across multiple leagues, and WSC Sports shows logos of the NBA, La Liga, MLS, DAZN, the NHL and several other leagues and broadcasters among its clients, though not every named client discloses a metric.",[166,167,168],"media-archive-metadata-tagging","audio-and-video-transcription-and-captioning","live-sports-commentary-generation","2026-09-30",[171],{"date":169,"note":172},"First published","automated-sports-highlights-and-clipping",[175,205],{"title":176,"useCases":177,"organization":178,"vendors":183,"summary":187,"stage":188,"year":189,"channels":190,"languages":191,"metrics":193,"outcomeDisclosed":194,"sources":195,"verification":200,"grade":202,"id":203,"organizationSlug":204},"Fox Sports: AI detection and clipping of live sports highlights",[173],{"name":179,"anonymized":180,"country":181,"region":182,"industry":16},"Fox Sports",false,"US","north-america",[184],{"name":185,"role":186},"AWS","platform","Fox Sports worked with AWS, which built a solution for Fox Sports on its managed AWS Elemental Inference service (AWS calls it a prototype) that continuously analyzes a live sports broadcast, detects key moments such as goals and celebrations within single digit seconds, and automatically extracts, crops to 9:16 vertical video and surfaces the resulting clip in a review portal within 20 to 30 seconds of the moment happening on air. In a quote on the page, a Fox Sports executive describes it as having evolved from a hackathon concept into a production ready, machine learning driven solution integrated into the organization's live production and distribution workflows across multiple major sports leagues, and says the move responds to nearly 90% of Fox Sports Digital content being consumed vertically.","production",2026,[],[192],"en",[],true,[196],{"url":197,"title":198,"publisher":199},"https://aws.amazon.com/blogs/media/how-aws-built-a-live-ai-powered-vertical-video-capability-for-fox-sports-with-aws-elemental-inference/","How AWS Built a Live AI-Powered Vertical Video Capability for Fox Sports with AWS Elemental Inference","AWS for M&E Blog",{"level":201,"checkedAt":169},"source-verified","C","fox-sports-live-highlight-clipping",null,{"title":206,"useCases":207,"organization":208,"vendors":210,"summary":213,"stage":188,"year":214,"channels":215,"languages":216,"metrics":217,"outcomeDisclosed":194,"sources":226,"verification":232,"grade":202,"id":233,"organizationSlug":204},"NBA: automated highlight generation with WSC Sports",[173],{"name":209,"anonymized":180,"country":181,"region":182,"industry":16},"National Basketball Association (NBA)",[211],{"name":212,"role":186},"WSC Sports","WSC Sports, whose platform automatically detects moments in games and cuts them into highlight clips for leagues and broadcasters, publishes a quote on its own site describing highlight package production for the NBA sped up to a few minutes for more than 1,000 packages, framed as letting the league create tailored content for every digital platform it operates on.",2025,[],[192],[218],{"kpi":37,"value":219,"unit":220,"qualifier":221,"period":222,"claimant":223,"quote":224,"sourceUrl":225},1000,"count","at-least","in a few minutes","organization","WSC Sports enables us to create unique content for every single digital platform that we touch globally. Now it takes a few minutes to create over 1,000 highlight packages.","https://www.wsc-sports.com/",[227,228],{"url":225,"title":212,"publisher":212},{"url":229,"title":230,"publisher":212,"date":231},"https://wsc-sports.com/blog/customer-spotlight/nba-launches-genai-technology-with-wsc-sports-to-automate-multilingual-content/","NBA Using GenAI Technology from WSC Sports to Automate Multilingual Content","2025-04-06",{"level":201,"checkedAt":169},"nba-wsc-sports-automated-highlights",0,[236],{"kpi":37,"label":237,"unit":220,"aggregate":180,"higherIsBetter":194,"n":238,"nUpTo":234,"median":219,"min":219,"max":219,"byClaimant":239,"vendorOnly":180,"points":240},"Interactions handled",1,{"organization":238,"vendor":234,"regulator":234,"independent":234},[241],{"evidenceId":233,"organization":209,"value":219,"qualifier":221,"claimant":223,"grade":202,"pooled":194},{"low":243,"high":244},8333.333333333332,106666.66666666667,[246,264,292,308],{"slug":166,"title":247,"shortTitle":248,"definition":249,"status":9,"industries":250,"functions":251,"patterns":253,"audience":255,"autonomy":26,"adoptionStage":256,"segment":28,"evidenceCount":257,"publicEvidenceCount":257,"organizations":258,"bestGrade":262,"headline":204,"lastVerified":263,"indexable":194},"AI metadata tagging and indexing for media archives","Media archive metadata tagging","AI that watches and listens to a broadcaster's or publisher's video and audio archive and generates rich, structured metadata, such as what is shown, who appears, spoken content, on screen text, logos and objects, so that content makers can find and reuse footage through natural language search instead of relying on the sparse, inconsistent tags an archive accumulated by hand over decades.",[16],[252,18],"knowledge-management",[21,254,22],"speech-analytics","back-office","early-adopters",3,[259,260,261],"Australian Broadcasting Corporation","Bell Media","Radiotelevisión Española (RTVE)","B","2026-09-28",{"slug":167,"title":265,"shortTitle":266,"definition":267,"status":9,"industries":268,"functions":271,"patterns":272,"audience":255,"autonomy":26,"adoptionStage":27,"evidenceCount":274,"publicEvidenceCount":275,"organizations":276,"bestGrade":262,"headline":284,"lastVerified":291,"indexable":194},"AI transcription, subtitles and captions for audio and video","Transcription and captioning","AI that transcribes recorded audio and video, such as podcasts, broadcasts, lessons, interviews and hearings, in several languages, separates the speakers and produces timed transcripts, subtitles and captions for a human editor to check, delivered as files for publishing or the archive.",[16,269,270],"education","cross-industry",[18,252],[254,273,23],"translation",8,7,[277,278,279,280,281,282,283],"Ateme","BBC","Comeen","Pacers Sports & Entertainment","Sveriges Television (SVT)","University of Florida","Warner Bros. Discovery",{"kpi":285,"label":286,"unit":287,"n":238,"nUpTo":234,"kind":288,"value":74,"qualifier":289,"claimant":290,"organization":283,"vendorReported":194},"cost-reduction","Cost reduction","percent","reported","exact","vendor","2026-09-26",{"slug":168,"title":293,"shortTitle":294,"definition":295,"status":9,"industries":296,"functions":297,"patterns":298,"audience":300,"autonomy":301,"adoptionStage":302,"segment":303,"evidenceCount":304,"publicEvidenceCount":304,"organizations":305,"bestGrade":202,"headline":204,"lastVerified":169,"indexable":194},"AI generated live sports commentary and data storytelling","Live sports commentary","AI that turns a live sporting event's own data, such as ball and player tracking, match events and statistics, into written commentary and insight as the action happens, for fans following a game through an app or feed rather than, or in addition to, a human broadcaster. Optionally, text to speech can voice the output for a spoken version.",[16],[19,18],[23,299,273],"summarization","customer-facing","autonomous","emerging","fan engagement",2,[306,307],"Bundesliga (DFL Deutsche Fußball Liga)","Major League Baseball (MLB)",{"slug":309,"title":310,"shortTitle":311,"definition":312,"status":9,"industries":313,"functions":315,"patterns":316,"audience":255,"autonomy":317,"adoptionStage":27,"evidenceCount":318,"publicEvidenceCount":318,"organizations":319,"bestGrade":262,"headline":326,"lastVerified":331,"indexable":194},"product-content-and-catalog-enrichment","AI product content and catalog enrichment for online retail","Product content and catalog enrichment","AI that writes and repairs product content at catalog scale: it drafts titles, descriptions and image alt text, and extracts missing attributes such as color, size and material from supplier text and product images, then checks its own output before the content is published to the store and to search engines. A human owns the rules, the quality thresholds and the exceptions.",[314,270],"retail-and-ecommerce",[19,18],[23,21,22],"supervised-agent",6,[320,321,322,323,324,325],"Amazon","eBay","Elemis","Etsy","Walmart","Zalando",{"kpi":327,"label":328,"unit":287,"n":238,"nUpTo":234,"kind":288,"value":329,"qualifier":330,"claimant":223,"organization":325,"vendorReported":180},"accuracy","Accuracy",75,"approximately","2026-09-27",{"indexable":194,"reasons":333},[],[335,339,345,353,360,367,373,379,387,393,400,406,412,418,425,432,438,445,450,456,463,470,475,480,485,491,496,501,507,512,520,527,533,539,544,549],{"id":135,"label":336,"issuer":139,"region":140,"url":141,"description":337,"useCases":338,"indexable":194},"EU AI Act","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":340,"label":341,"issuer":139,"region":140,"url":342,"description":343,"useCases":344,"indexable":194},"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.",223,{"id":346,"label":347,"issuer":348,"region":349,"url":350,"description":351,"useCases":352,"indexable":194},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":354,"label":355,"issuer":356,"region":182,"url":357,"description":358,"useCases":359,"indexable":194},"nist-ai-rmf","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.",95,{"id":361,"label":362,"issuer":363,"region":140,"url":364,"description":365,"useCases":366,"indexable":194},"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":368,"label":369,"issuer":139,"region":140,"url":370,"description":371,"useCases":372,"indexable":194},"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":374,"label":375,"issuer":376,"region":140,"url":377,"description":378,"useCases":74,"indexable":194},"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":380,"label":381,"issuer":382,"region":383,"url":384,"description":385,"useCases":386,"indexable":194},"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":388,"label":389,"issuer":390,"region":383,"url":391,"description":392,"useCases":73,"indexable":194},"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":394,"label":395,"issuer":396,"region":349,"url":397,"description":398,"useCases":399,"indexable":194},"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":401,"label":402,"issuer":403,"region":182,"url":404,"description":405,"useCases":399,"indexable":194},"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":407,"label":408,"issuer":139,"region":140,"url":409,"description":410,"useCases":411,"indexable":194},"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":413,"label":414,"issuer":415,"region":140,"url":416,"description":417,"useCases":411,"indexable":194},"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":419,"label":420,"issuer":421,"region":182,"url":422,"description":423,"useCases":424,"indexable":194},"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":426,"label":427,"issuer":428,"region":349,"url":429,"description":430,"useCases":431,"indexable":194},"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":433,"label":434,"issuer":139,"region":140,"url":435,"description":436,"useCases":437,"indexable":194},"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":439,"label":440,"issuer":441,"region":182,"url":442,"description":443,"useCases":444,"indexable":194},"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":446,"label":447,"issuer":139,"region":140,"url":448,"description":449,"useCases":444,"indexable":194},"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":451,"label":452,"issuer":453,"region":182,"url":454,"description":455,"useCases":444,"indexable":194},"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":457,"label":458,"issuer":459,"region":349,"url":460,"description":461,"useCases":462,"indexable":194},"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.",12,{"id":464,"label":465,"issuer":466,"region":182,"url":467,"description":468,"useCases":469,"indexable":194},"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":471,"label":472,"issuer":139,"region":140,"url":473,"description":474,"useCases":469,"indexable":194},"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":476,"label":477,"issuer":139,"region":140,"url":478,"description":479,"useCases":469,"indexable":194},"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":481,"label":482,"issuer":139,"region":140,"url":483,"description":484,"useCases":469,"indexable":194},"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":486,"label":487,"issuer":488,"region":140,"url":489,"description":490,"useCases":60,"indexable":194},"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":492,"label":493,"issuer":382,"region":383,"url":494,"description":495,"useCases":60,"indexable":194},"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":497,"label":498,"issuer":139,"region":140,"url":499,"description":500,"useCases":60,"indexable":194},"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":502,"label":503,"issuer":504,"region":182,"url":505,"description":506,"useCases":275,"indexable":194},"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":508,"label":509,"issuer":139,"region":140,"url":510,"description":511,"useCases":275,"indexable":194},"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":513,"label":514,"issuer":515,"region":516,"url":517,"description":518,"useCases":519,"indexable":194},"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.",5,{"id":521,"label":522,"issuer":523,"region":140,"url":524,"description":525,"useCases":526,"indexable":194},"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":528,"label":529,"issuer":530,"region":140,"url":531,"description":532,"useCases":526,"indexable":194},"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":534,"label":535,"issuer":536,"region":383,"url":537,"description":538,"useCases":257,"indexable":194},"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":540,"label":541,"issuer":139,"region":140,"url":542,"description":543,"useCases":257,"indexable":194},"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":545,"label":546,"issuer":139,"region":140,"url":547,"description":548,"useCases":257,"indexable":194},"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":550,"label":551,"issuer":552,"region":182,"url":553,"description":554,"useCases":257,"indexable":194},"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.",1790783070976]