[{"data":1,"prerenderedAt":539},["ShallowReactive",2],{"uc-live-sports-commentary-generation":3,"uc-regulations":315},{"useCase":4,"evidence":173,"blitsAiDeployments":224,"benchmarks":225,"indicative":226,"related":229,"indexability":313,"includeUnpublished":179},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":20,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"indicativeValue":41,"macroEstimates":76,"feasibility":77,"implementation":89,"risk":124,"blitsAi":143,"faq":145,"related":158,"datePublished":161,"dateModified":162,"lastVerified":162,"changelog":163,"slug":172},"AI generated live sports commentary and data storytelling","Live sports commentary","AI generated live sports commentary","AI turns live match data into real time commentary. MLB adds AI color commentary with Scout Insights; AWS's AI Live Ticker automates Bundesliga match commentary.","published","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.",[12,13,14],"AI sports commentary generator","automated play by play commentary","AI live match insights",[16],"media-and-entertainment",[18,19],"marketing","operations",[21,22,23],"content-generation","summarization","translation",[25],"mobile-app","customer-facing","autonomous","emerging","fan engagement","Top leagues now capture enormous amounts of live data: MLB says it is collecting over 15 million\nevents and data points per game, and AWS reports that the Bundesliga's data foundation processes\n200 million data points per match. Much of that data reaches fans only through the filter of a human\nbroadcast commentary team, which exists for the biggest matches, in the main broadcast languages,\nand not for every match, market or language a league wants to reach.\n\nA smaller league, a secondary market or a fan who does not speak the broadcast language gets the raw\nnumbers on a stats page at best, not the narrative that makes those numbers meaningful in the moment.\nProducing that narrative live, in many languages, for many concurrent matches, is not practical with\nhuman commentators alone.",[],"1. **Capture the live data.** Tracking and event systems record the game at high frequency: ball,\n   player and pose tracking, match events and historical statistics, arriving as a continuous feed\n   while the match is live.\n2. **Find the story in the data.** A model scans the incoming feed for statistically interesting or\n   narratively relevant moments, such as a record, a reversal or a repeated pattern, and turns each\n   one into a short narrative point for a broadcaster or directly for fans.\n3. **Generate the commentary.** A generative model turns the selected data point or event into\n   natural language in the target language, timed to the live action. Optionally, text to speech can\n   voice the output for a spoken version.\n4. **Publish alongside the live feed.** The commentary appears next to the existing play by play in\n   an app or web feed, or is delivered as an automated, multilingual live ticker running independently\n   of the human broadcast.\n5. **Monitor and correct.** Editorial or product staff sample the generated commentary for accuracy\n   and tone during and after live events, and can pull a specific feature if quality drops mid event.",[34,35,36],"customer-experience","inclusion-and-access","revenue-growth",[38,39,40],"users-served","accuracy","revenue-uplift",{"referenceOrg":42,"inputs":43,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A league with 380 matches a season and no live commentary in most fan languages",[44,50,57,64],{"key":45,"label":46,"low":47,"high":47,"unit":48,"note":49},"matchesPerSeason","Matches per season",380,"matches per season","The reference league, sized like a European top flight with 20 clubs playing home and away.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"fanLanguagesAdded","Additional languages automated commentary can cover beyond the broadcaster's own commentary team",3,8,"languages","Editorial assumption. Replace with your own target market list.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"incrementalStreamsPerLanguage","Additional streams unlocked per match, per new language covered",500,5000,"streams per match","Editorial assumption, replace with your own audience research.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"revenuePerStream","Average advertising or subscription revenue per incremental stream",0.02,0.1,"USD per stream","Editorial assumption for an ad supported or subscription stream. Replace with your own.","matchesPerSeason * fanLanguagesAdded * incrementalStreamsPerLanguage * revenuePerStream","USD","per season","Incremental streaming revenue from newly covered languages","Gross incremental revenue only, before the cost of running the AI commentary service, any cannibalization of existing paid commentary products, and the risk that fans value human commentary for marquee matches in a way the automated version does not yet replace.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"high","Real time is the hard constraint: the system has to ingest a live data feed, decide what is worth saying, generate language, and sometimes speech, and publish it within seconds of the event, for every match running concurrently, in every target language. That is a different engineering problem from generating commentary after the fact.",[81,82,83],"A live, structured data feed of the event, such as ball and player tracking, match events and historical statistics","A defined narrative style and vocabulary per sport and per language","Historical commentary or narration to tune tone and style, where it exists",[85,86,87,88],"Live sports data or tracking provider","A low latency generation pipeline that can run per match, concurrently across many matches","The publishing surface, such as an app feed, a live ticker or broadcast graphics","Text to speech, for a spoken rather than written commentary",{"steps":90,"guardrails":106,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":117},[91,94,97,100,103],{"title":92,"detail":93},"Start with insight, not full commentary","Begin with short, data grounded insights, such as a record, a streak or a comparison, delivered next to the existing play by play, rather than trying to replace a full human commentary track on day one.",{"title":95,"detail":96},"Fix the data feed before the language model","Live commentary is only as good as the tracking data behind it. Validate the accuracy and latency of the underlying data feed first.",{"title":98,"detail":99},"Localize deliberately, market by market","Treat each language as its own launch with its own vocabulary, idiom and tone, rather than a direct translation of the main broadcast commentary.",{"title":101,"detail":102},"Sample every live match for accuracy","Because commentary runs unattended in real time, review a sample of generated commentary from every match afterwards, not only when a fan complains.",{"title":104,"detail":105},"Keep marquee events human led","Reserve human commentators for the matches or moments that carry the most commercial or emotional weight, and use automated commentary to extend coverage where a human commentary team does not reach.",[107,108,109,110],"A fixed set of data sources and statistics the system may reference, so it cannot invent a statistic that is not in the feed","Automatic suppression or delay if the live data feed itself looks wrong or disconnects, rather than continuing to narrate stale or invented data","Human review of new languages and new sports before wide release","Clear labelling that the commentary is AI generated","Editorial and product staff sample generated commentary from live matches for accuracy and tone, tune the narrative style before a new language or sport goes live, and can pull a specific automated feature mid event if the underlying data or the generated language goes wrong.",[113,114,115,116],"Fan reach per language, before and after automated commentary","Accuracy of generated statements against the underlying data feed, sampled per match","Latency from the live event to the published commentary","Complaints or corrections per hour of generated commentary",[118,121],{"title":119,"detail":120},"Confidently wrong commentary","A generative model can state a plausible sounding but incorrect statistic if the underlying data feed lags or errors. Constrain the model to only state numbers it can trace to the live feed, and monitor for drift.",{"title":122,"detail":123},"Flat or repetitive narration at scale","Running the same generation approach across many concurrent matches can produce generic, repetitive commentary that feels automated. Vary the narrative templates and sample output across matches, not only within one.",{"euAiAct":125,"regulations":128,"guidance":130,"controls":137,"incidents":142},{"tier":126,"basis":127},"limited","Article 50(2): a system that generates synthetic audio, text or video content, such as AI generated commentary, must ensure its output is marked in a machine readable format and detectable as artificially generated, unless a narrow exemption applies. That is a technical marking duty on the provider, not necessarily a visible on screen label for viewers; a visible disclosure that commentary is AI generated, as listed in controls below, is a design choice. Article 50(4) can separately require a deployer to disclose that AI generated or manipulated text has been artificially generated, but only for text published with the purpose of informing the public on matters of public interest, and that duty does not apply where the text has undergone human review or editorial control and a natural or legal person holds editorial responsibility for publishing it. Routine live match commentary will often sit outside Article 50(4) for both reasons: it is not usually framed as informing the public on a matter of public interest, and an editorial or product team typically reviews it. This is not an Annex III use unless the generated commentary itself were used to make a decision about a natural person, which is not the case in the deployments we found.",[129],"eu-ai-act",[131],{"title":132,"issuer":133,"region":134,"url":135,"note":136},"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","Generated audio, text or video content must be marked as artificially generated and detectable as such.",[138,139,140,141],"Disclosure that commentary or insights are AI generated, in the app or feed itself","A fixed list of data sources the system may cite, with no fabricated statistics","Automatic fallback to silence or a generic message if the live data feed fails, rather than continuing to narrate","Sampling review of generated commentary for accuracy after every event, not only on complaint",[],{"howToBuild":144},"Blits.ai is built for conversational and workflow AI, not for a dedicated, low latency broadcast\npipeline generating an unattended commentary track for many concurrent matches, so a deployment like\nthis sits alongside a live sports data feed rather than inside it. A **custom function** pulls the\nlatest event and statistics from the data provider, and an agent, using the platform's **model\nagnostic routing** to switch to a fast, low cost model for high volume generation, turns that data\ninto a short commentary line grounded only in the values the function returned.\n\nFor the fan facing surface, a **conversational agent** can answer follow up questions about the\nmatch from the same live data, such as what a specific statistic means, through a **knowledge base**\nor a **SQL knowledge base** connected to the statistics feed, with **voice output through the\nplatform's text to speech providers** when a spoken answer fits the channel. **Output guardrails**\ngive the agent an LLM judged check against an admin authored policy, for example one that blocks a\nreply for stating a statistic at all; the guardrail judges the reply text against the policy, not\nagainst the live data feed, so it cannot itself confirm a number is correct. Grounding a reply's\nnumbers in the connected source is instead the job of the custom function and the agent's\ninstructions, checked with **test suites** and watched in production with **monitors** that sample\nlive outputs. **Analytics** show how often fans ask for commentary or insight overall; a per match\nbreakdown would need a **custom dashboard widget or calculated statistic** rather than the default\nview. Generating a continuous, unattended commentary track at broadcast latency for every match in\na season is outside what the platform is built for today.",[146,149,152,155],{"question":147,"answer":148},"Can AI really commentate a live sporting event?","Today it is mostly used to add insight and multilingual narration alongside the main broadcast, rather than replace a lead commentator outright. MLB's Scout Insights adds AI generated color commentary and insight to its Gameday play by play feed, and the Bundesliga's AI Live Ticker turns match data into automated, multilingual commentary as events happen on the pitch.",{"question":150,"answer":151},"How much data does this actually run on?","A lot. MLB says it is collecting over 15 million events and data points per game, and AWS reports that the Bundesliga's data foundation processes 200 million data points per match to power its live commentary and insight tools.",{"question":153,"answer":154},"Does AI generated commentary need to be labelled?","Under the EU AI Act, Article 50(2) requires the output itself to be marked in a machine readable format and detectable as artificially generated, unless a narrow exemption applies. That is a technical marking duty on the system, not automatically a visible label a fan sees on screen; showing one anyway is a sound design choice. Separate deployer obligations under Article 50(4) can apply, but only to text published with the purpose of informing the public on matters of public interest, and not to text that has had human review or editorial control by someone who holds editorial responsibility for it, so routine sports commentary will often fall outside that duty.",{"question":156,"answer":157},"What happens if the live data feed breaks mid match?","None of the deployments we found disclose this publicly. Treat a data feed failure as a guardrail case: the system should fall back to silence or a generic message rather than continue narrating stale or extrapolated data.",[159,160],"content-recommendation-and-personalization","automated-sports-highlights-and-clipping","2026-09-29","2026-09-30",[164,166,168,170],{"date":162,"note":165},"Published after review by an automated review workflow (independent skeptic review).",{"date":162,"note":167},"Editorial fix round. Reworded blitsAi.howToBuild: output guardrails are described as an LLM judged check against an admin authored policy (for example, blocking replies that state statistics), not as a check that compares a reply's numbers against the connected data source, since the guardrail never sees the data source; grounding a reply's numbers is now attributed to the custom function and agent instructions, tested with test suites and watched with monitors. Quoted Article 50(4) of the EU AI Act in full in faq and risk.euAiAct.basis: the duty applies only to text published to inform the public on matters of public interest, and does not apply to text that has had human review or editorial control by someone with editorial responsibility for it; noted that routine sports commentary will often fall outside it for both reasons, checked word for word against the archived regulation text (the EU's legal database blocks automated access, so we used the Wayback copy).",{"date":162,"note":169},"Unpublished by an automated review workflow (independent skeptic review).",{"date":161,"note":171},"First published","live-sports-commentary-generation",[174,200],{"title":175,"useCases":176,"organization":177,"vendors":181,"summary":185,"stage":186,"year":187,"channels":188,"languages":189,"metrics":190,"outcomeDisclosed":179,"sources":191,"verification":195,"grade":197,"id":198,"organizationSlug":199},"Bundesliga: AI Live Ticker and Captain AI companion for live match data and commentary",[172],{"name":178,"anonymized":179,"country":180,"region":134,"industry":16},"Bundesliga (DFL Deutsche Fußball Liga)",false,"DE",[182],{"name":183,"role":184},"AWS","platform","The Bundesliga uses an AWS data foundation that processes 200 million data points per match to power Data Story Finder, which surfaces narratives for broadcasters from match data, player statistics and tactical insights, delivered to commentators in milliseconds, and AI Live Ticker, which turns the same data into automated, multilingual commentary as a match happens. AWS says AI Live Ticker is built on a fully serverless AWS architecture using Amazon Bedrock; Data Story Finder is described only as powered by the same AWS data foundation, not tied to Bedrock by name. The official Bundesliga app separately offers Captain, also built on Amazon Bedrock (and Amazon Nova), an AI companion that answers fan questions in natural language from official match data. AWS's own marketing describes Captain, not AI Live Ticker, as agentic AI in production for 1B+ fans worldwide; that figure is AWS's framing of the Bundesliga's claimed global fan base, not a measured usage count for Captain, AI Live Ticker or any other product on this record.","production",2026,[25],[],[],[192],{"url":193,"title":194,"publisher":183},"https://aws.amazon.com/sports/bundesliga/","Bundesliga Insights - Bundesliga Match Facts - AWS",{"level":196,"checkedAt":162},"source-verified","C","bundesliga-ai-live-ticker-commentary",null,{"title":201,"useCases":202,"organization":203,"vendors":207,"summary":210,"stage":186,"year":187,"channels":211,"languages":212,"metrics":214,"outcomeDisclosed":179,"sources":215,"verification":222,"grade":197,"id":223,"organizationSlug":199},"MLB: Statcast live data and AI generated Scout Insights commentary",[172],{"name":204,"anonymized":179,"country":205,"region":206,"industry":16},"Major League Baseball (MLB)","US","north-america",[208],{"name":209,"role":184},"Google Cloud","Major League Baseball's Statcast system, built for scale, speed, security and flexibility using Google Cloud's Gemini Enterprise Agent Platform and BigQuery, analyzes ball, player and pose tracking data from every game and turns it into predictive models such as catch probability and steal success rate. Sean Curtis, MLB's Senior Vice President of Technology and Infrastructure, says MLB is collecting over 15 million events and data points per game. Google Cloud reports that Statcast improves stats delivery by 300 milliseconds, enabling real time analysis of ball and strike calls. For the 2026 season, MLB launched Scout Insights, which Google Cloud describes as bringing AI generated color commentary and insight to the Gameday play by play feed in the MLB App and MLB.com.",[25],[213],"en",[],[216,219],{"url":217,"title":218,"publisher":209},"https://cloud.google.com/customers/mlb","MLB case study | Google Cloud",{"url":220,"title":221,"publisher":209},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","101 real world generative AI use cases from industry leaders",{"level":196,"checkedAt":162},"mlb-scout-insights-ai-commentary",0,[],{"low":227,"high":228},11400,1520000,[230,252,268,286],{"slug":159,"title":231,"shortTitle":232,"definition":233,"status":9,"industries":234,"functions":235,"patterns":237,"audience":240,"autonomy":27,"adoptionStage":241,"segment":242,"evidenceCount":243,"publicEvidenceCount":243,"organizations":244,"bestGrade":249,"headline":199,"lastVerified":250,"indexable":251},"AI recommendation and personalization engine for streaming and media","Content recommendation and personalization","A recommendation system that decides, for each individual viewer or listener, what to show next on a home page, in search or in a personalized playlist, learned from that person's own viewing or listening history, ratings and context, and continuously updated as new content is added and behavior changes. It ranks the catalog's own content; it is not the marketing engine that decides which offers or campaigns to send, which is a separate use case in this library.",[16],[18,236],"analytics-and-reporting",[238,239],"recommendation-and-personalization","prediction-and-scoring","back-office","mainstream","content discovery",4,[245,246,247,248],"Golden State Warriors","La Liga","Netflix, Inc.","Spotify","B","2026-09-28",true,{"slug":160,"title":253,"shortTitle":254,"definition":255,"status":9,"industries":256,"functions":257,"patterns":258,"audience":261,"autonomy":262,"adoptionStage":241,"segment":263,"evidenceCount":264,"publicEvidenceCount":264,"organizations":265,"bestGrade":197,"headline":199,"lastVerified":162,"indexable":251},"AI agent for automated sports highlights and clipping","Sports highlights and clipping","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.",[16],[19,18],[259,260,21],"computer-vision","classification-and-routing","employee-facing","copilot","content operations",2,[266,267],"Fox Sports","National Basketball Association (NBA)",{"slug":269,"title":270,"shortTitle":271,"definition":272,"status":9,"industries":273,"functions":276,"patterns":278,"audience":240,"autonomy":262,"adoptionStage":279,"segment":280,"evidenceCount":243,"publicEvidenceCount":53,"organizations":281,"bestGrade":249,"headline":199,"lastVerified":285,"indexable":251},"portfolio-reporting-and-commentary","AI generated client portfolio reports and commentary","Portfolio commentary","AI that drafts each client's periodic portfolio commentary and report narrative (performance, attribution, what drove returns, positioning and outlook) in plain language and in the client's language, where every figure comes from the portfolio system of record and a reviewer approves the text before delivery.",[274,275],"wealth-and-asset-management","banking",[236,277,19],"customer-service",[21,22,23],"early-adopters","middle-office",[282,283,284],"AssetMark","Morgan Stanley","Quilter","2026-09-27",{"slug":287,"title":288,"shortTitle":289,"definition":290,"status":9,"industries":291,"functions":294,"patterns":295,"audience":240,"autonomy":296,"adoptionStage":241,"evidenceCount":297,"publicEvidenceCount":297,"organizations":298,"bestGrade":249,"headline":305,"lastVerified":285,"indexable":251},"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.",[292,293],"retail-and-ecommerce","cross-industry",[18,19],[21,259,260],"supervised-agent",6,[299,300,301,302,303,304],"Amazon","eBay","Elemis","Etsy","Walmart","Zalando",{"kpi":39,"label":306,"unit":307,"n":308,"nUpTo":224,"kind":309,"value":310,"qualifier":311,"claimant":312,"organization":304,"vendorReported":179},"Accuracy","percent",1,"reported",75,"approximately","organization",{"indexable":251,"reasons":314},[],[316,320,326,334,341,348,354,361,369,376,383,389,395,401,408,415,421,428,433,439,446,453,458,463,468,475,480,485,492,497,505,511,517,523,528,533],{"id":129,"label":317,"issuer":133,"region":134,"url":135,"description":318,"useCases":319,"indexable":251},"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":321,"label":322,"issuer":133,"region":134,"url":323,"description":324,"useCases":325,"indexable":251},"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":327,"label":328,"issuer":329,"region":330,"url":331,"description":332,"useCases":333,"indexable":251},"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":335,"label":336,"issuer":337,"region":206,"url":338,"description":339,"useCases":340,"indexable":251},"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":342,"label":343,"issuer":344,"region":134,"url":345,"description":346,"useCases":347,"indexable":251},"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":349,"label":350,"issuer":133,"region":134,"url":351,"description":352,"useCases":353,"indexable":251},"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":355,"label":356,"issuer":357,"region":134,"url":358,"description":359,"useCases":360,"indexable":251},"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.",50,{"id":362,"label":363,"issuer":364,"region":365,"url":366,"description":367,"useCases":368,"indexable":251},"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":370,"label":371,"issuer":372,"region":365,"url":373,"description":374,"useCases":375,"indexable":251},"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":377,"label":378,"issuer":379,"region":330,"url":380,"description":381,"useCases":382,"indexable":251},"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":384,"label":385,"issuer":386,"region":206,"url":387,"description":388,"useCases":382,"indexable":251},"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":390,"label":391,"issuer":133,"region":134,"url":392,"description":393,"useCases":394,"indexable":251},"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":396,"label":397,"issuer":398,"region":134,"url":399,"description":400,"useCases":394,"indexable":251},"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":402,"label":403,"issuer":404,"region":206,"url":405,"description":406,"useCases":407,"indexable":251},"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":409,"label":410,"issuer":411,"region":330,"url":412,"description":413,"useCases":414,"indexable":251},"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":416,"label":417,"issuer":133,"region":134,"url":418,"description":419,"useCases":420,"indexable":251},"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":422,"label":423,"issuer":424,"region":206,"url":425,"description":426,"useCases":427,"indexable":251},"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":429,"label":430,"issuer":133,"region":134,"url":431,"description":432,"useCases":427,"indexable":251},"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":434,"label":435,"issuer":436,"region":206,"url":437,"description":438,"useCases":427,"indexable":251},"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":440,"label":441,"issuer":442,"region":330,"url":443,"description":444,"useCases":445,"indexable":251},"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":447,"label":448,"issuer":449,"region":206,"url":450,"description":451,"useCases":452,"indexable":251},"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":454,"label":455,"issuer":133,"region":134,"url":456,"description":457,"useCases":452,"indexable":251},"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":459,"label":460,"issuer":133,"region":134,"url":461,"description":462,"useCases":452,"indexable":251},"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":464,"label":465,"issuer":133,"region":134,"url":466,"description":467,"useCases":452,"indexable":251},"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":469,"label":470,"issuer":471,"region":134,"url":472,"description":473,"useCases":474,"indexable":251},"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":476,"label":477,"issuer":364,"region":365,"url":478,"description":479,"useCases":474,"indexable":251},"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":481,"label":482,"issuer":133,"region":134,"url":483,"description":484,"useCases":474,"indexable":251},"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":486,"label":487,"issuer":488,"region":206,"url":489,"description":490,"useCases":491,"indexable":251},"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.",7,{"id":493,"label":494,"issuer":133,"region":134,"url":495,"description":496,"useCases":491,"indexable":251},"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":498,"label":499,"issuer":500,"region":501,"url":502,"description":503,"useCases":504,"indexable":251},"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":506,"label":507,"issuer":508,"region":134,"url":509,"description":510,"useCases":243,"indexable":251},"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":512,"label":513,"issuer":514,"region":134,"url":515,"description":516,"useCases":243,"indexable":251},"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":518,"label":519,"issuer":520,"region":365,"url":521,"description":522,"useCases":53,"indexable":251},"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":524,"label":525,"issuer":133,"region":134,"url":526,"description":527,"useCases":53,"indexable":251},"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":529,"label":530,"issuer":133,"region":134,"url":531,"description":532,"useCases":53,"indexable":251},"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":534,"label":535,"issuer":536,"region":206,"url":537,"description":538,"useCases":53,"indexable":251},"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.",1790783081682]