[{"data":1,"prerenderedAt":553},["ShallowReactive",2],{"uc-content-recommendation-and-personalization":3,"uc-regulations":342},{"useCase":4,"evidence":175,"blitsAiDeployments":231,"benchmarks":232,"indicative":233,"related":236,"indexability":340,"includeUnpublished":181},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":22,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":38,"indicativeValue":42,"macroEstimates":69,"feasibility":70,"implementation":82,"risk":126,"blitsAi":152,"faq":154,"related":167,"datePublished":170,"dateModified":170,"lastVerified":170,"changelog":171,"slug":174},"AI recommendation and personalization engine for streaming and media","Content recommendation and personalization","AI recommendation engine for streaming","AI decides what each viewer sees next. Netflix researchers estimate a popularity ranking cuts engagement 12%; Spotify's Discover Weekly hit 100 billion streams.","published","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.",[12,13,14,15,16],"AI content recommendation","streaming personalization engine","recommendation algorithm","personalized playlist and homepage","next best content",[18],"media-and-entertainment",[20,21],"marketing","analytics-and-reporting",[23,24],"recommendation-and-personalization","prediction-and-scoring",[26,27],"mobile-app","api","back-office","autonomous","mainstream","content discovery","A streaming service or publisher with a large catalog has a discovery problem, not a supply\nproblem: most of what would delight a given viewer or listener is not what they would have found by\nbrowsing. Left to manual curation or simple popularity ranking, the same hit titles and tracks\ndominate every home page, niche and new content struggles to be found, and people who cannot find\nsomething they like churn.\n\nRecommendation systems at large platforms have evolved considerably, and the work keeps shifting.\nEarly systems used collaborative filtering, matching people with similar histories. Netflix\nillustrates a newer direction: rather than a variety of specialized models each covering one need\n(for example \"Continue Watching\" or \"Today's Top Picks for You\"), a single foundation model learns\nfrom a person's comprehensive interaction history, tokenized the way text is tokenized for a large\nlanguage model, and shares that learning with other models through embeddings or fine tuning.",[],"1. **Collect signals.** Every play, pause, skip, rating, search and scroll is logged as an event,\n   alongside metadata about the content itself (genre, cast, tempo, mood, release date).\n2. **Build a shared representation.** A model learns embeddings for people and for content from\n   this interaction history at scale, so that people and titles with similar patterns end up close\n   together in the model's internal representation, and new, unwatched titles can still be placed\n   using their metadata (a cold start problem).\n3. **Rank for each surface.** The shared model, or models fine tuned from it, rank candidates for a\n   specific surface: the home page, a personalized playlist, a search result, an autoplay queue.\n4. **Serve within a latency budget.** Ranking has to return in milliseconds, so systems trade off\n   how much history they can consider against how fast they can score it, often using sparse\n   attention or similar techniques to fit long histories into a short serving budget.\n5. **Measure causally, not just by clicks.** Because recommendations are also what people see, raw\n   engagement with recommended content overstates the system's effect. Mature teams run\n   experiments, including replacing the system with a simpler baseline for a slice of users, to\n   isolate how much of the engagement the recommender actually causes.",[36,37],"customer-experience","revenue-growth",[39,40,41],"users-served","revenue-uplift","churn-reduction",{"referenceOrg":43,"inputs":44,"formula":64,"currency":65,"period":66,"resultLabel":67,"caveat":68},"A streaming service with 5 million active subscribers",[45,50,57],{"key":46,"label":47,"low":48,"high":48,"unit":46,"note":49},"subscribers","Active subscribers",5000000,"The reference service.",{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"annualArpuUsd","Average annual revenue per subscriber",60,150,"USD per subscriber per year","Editorial assumption for a mid tier subscription service. Replace with your own ARPU.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"retentionEffect","Share of subscribers retained per year because of personalized recommendations",0.01,0.03,"fraction of subscribers per year","Netflix's own finding is that replacing its recommender with a popularity based ranking would cut member engagement by 12% (arXiv:2511.07280, not attached here as a source since the paper does not measure retention). Editorial assumption, replace with your own: this range assumes only a small, unverified fraction of that engagement effect converts into an avoided cancellation.","subscribers * retentionEffect * annualArpuUsd","USD","per year","Annual subscription revenue retained through personalization","Gross retention value only, built on an unverified assumption about how much of Netflix's engagement effect converts into retention. It leaves out the cost of running the recommendation system, any effect on new subscriber acquisition, and the fact that churn has many causes besides content discovery, so treat this as a rough illustration, not a forecast.",[],{"complexity":71,"complexityNote":72,"dataPrerequisites":73,"integrations":77},"high","A production recommendation system is a standing data and machine learning platform, not a single project: it needs a real time event pipeline, a feature and embedding store, offline and online evaluation, and a serving layer that meets a strict latency budget across every surface it feeds.",[74,75,76],"Complete interaction event logs (plays, skips, ratings, searches) at the individual level","Content metadata (genre, cast, language, release date and, ideally, richer descriptors)","A way to measure outcomes beyond clicks, such as a holdout or interleaving experiment",[78,79,80,81],"Event streaming or logging pipeline from every client (app, web, TV)","Content catalog and metadata management system","Low latency online serving infrastructure for the ranking model","Experimentation platform for A/B and holdout testing",{"steps":83,"guardrails":102,"humanInTheLoop":107,"kpisToInstrument":108,"failureModes":113},[84,87,90,93,96,99],{"title":85,"detail":86},"Start from a strong baseline, not a blank page","Ship a well tuned popularity or collaborative filtering baseline first, and measure every later model against it with a real experiment, not just an offline metric.",{"title":88,"detail":89},"Instrument the full interaction history","Capture every meaningful event, not just completions, and decide early how to tokenize or aggregate them (for example, summing watch duration per title) so the signal survives compression into a manageable sequence length.",{"title":91,"detail":92},"Solve cold start explicitly","New content and new users have no history. Use content metadata to place new titles near similar existing ones, and use onboarding preferences or early signals to place new users, rather than defaulting everyone to the same popular list.",{"title":94,"detail":95},"Separate ranking from presentation","Keep the model's job (rank candidates) separate from product decisions (how many rows, how much diversity, whether to explain a pick), so the product team can adjust presentation without retraining the model.",{"title":97,"detail":98},"Run holdout and interleaving experiments","Periodically hold out a small population on an older or simpler algorithm, or interleave results from two algorithms in the same session, so you can measure the model's true incremental value instead of trusting raw engagement with recommended content.",{"title":100,"detail":101},"Watch diversity, not only accuracy","A model optimized purely for predicted engagement will over serve the same popular titles. Track catalog coverage and the share of recommendations going to mid and long tail content alongside accuracy metrics.",[103,104,105,106],"Content and safety filters applied before anything is recommended to a person, independent of the ranking model (age appropriate content, platform policy compliance)","A minimum diversity or exploration budget so the system keeps surfacing content outside a person's established pattern, rather than narrowing to a filter bubble","Human review of what the model associates with sensitive categories (for example, content aimed at children) before those associations reach production","Rate limits and monitoring on any interactive or agentic layer built on top of the ranking model","Editorial and content teams own the guardrails (what may never be recommended, and to whom), review model behavior on sensitive content categories, and set the exploration and diversity targets the ranking has to respect; data scientists own experiment design and causal measurement so engagement gains are not mistaken for value the system did not create.",[109,110,111,112],"Incremental engagement from a holdout or interleaving experiment, not raw engagement with recommended content","Catalog coverage and the share of engagement going to content that is not already popular","Subscriber retention or return rate for people who do and do not engage with recommendations","Time to first meaningful recommendation for a new user or new title (cold start latency)",[114,117,120,123],{"title":115,"detail":116},"Engagement that is not incremental","Recommended content also gets promoted placement, so raw clicks overstate the model's effect. Measure against a randomized or interleaved baseline, or model the counterfactual explicitly and validate it with a randomized experiment, rather than against a no recommendation control that never ships.",{"title":118,"detail":119},"Filter bubbles and catalog concentration","A model that only optimizes predicted engagement converges on already popular titles. Instrument and target catalog coverage explicitly, not just top line engagement.",{"title":121,"detail":122},"Silent bias in what gets amplified","Embeddings learned from historical behavior can encode and reinforce existing skew (for example, under exposing content from smaller creators). Audit exposure by creator or content category, not only by predicted relevance.",{"title":124,"detail":125},"Cold start dead zones","New titles and new users get poor recommendations until enough interaction data accumulates, which can suppress exactly the content a catalog most needs to surface. Use metadata based placement and monitor exposure for new content specifically.",{"euAiAct":127,"regulations":130,"guidance":134,"controls":146,"incidents":151},{"tier":128,"basis":129},"context-dependent","Recommendation and personalization systems are not listed in Annex III, so most deployments are minimal risk under the EU AI Act. They become a compliance question elsewhere: manipulative or deceptive techniques that materially distort a person's behavior in a way that causes significant harm, or that exploit vulnerabilities linked to age, disability or a specific social or economic situation, are a prohibited practice under Article 5(1)(a) and (b), which is relevant to recommendation systems that target children. A decision based solely on automated processing, including profiling, that produces legal or similarly significant effects on a person falls under GDPR Article 22, though routine content ranking rarely meets that bar on its own.",[131,132,133],"eu-ai-act","gdpr","uk-gdpr",[135,141],{"title":136,"issuer":137,"region":138,"url":139,"note":140},"EU AI Act Explorer: Article 5, prohibited AI practices","Future of Life Institute","europe","https://artificialintelligenceact.eu/article/5/","Third party plain language explainer of the AI Act, checked against its Article 5 text. Points 1(a) and 1(b) prohibit manipulative or deceptive techniques and the exploitation of vulnerabilities of a person or group \"due to their age, disability or a specific social or economic situation\", in each case only where they materially distort behavior in a way that causes or is reasonably likely to cause significant harm.",{"title":142,"issuer":143,"region":138,"url":144,"note":145},"Guidelines 05/2020 on consent under Regulation 2016/679","European Data Protection Board","https://www.edpb.europa.eu/our-work-tools/our-documents/guidelines/guidelines-052020-consent-under-regulation-2016679_en","Relevant where personalization relies on tracking or profiling that needs a lawful basis.",[147,148,149,150],"Inventory entry for the recommendation system with an accountable owner and a documented list of what it may never recommend, and to whom","Regular experiment based measurement of the system's true incremental effect, not only engagement dashboards","Bias and exposure audits by content category and, where relevant, by protected characteristic of the audience segment","Age appropriate design review for any surface reachable by minors",[],{"howToBuild":153},"Blits.ai is a conversational and agentic AI platform, not a ranking or embeddings platform, so\nthe core recommendation model here is built and served outside it, typically as an existing\ncatalog or personalization service the organization already runs or licenses. Where Blits.ai adds\nvalue is the layer people actually talk to: an **AI agent** with **custom functions** that call\nthe organization's own recommendation API can turn a ranked list into a conversation, explaining\nwhy a title or track was suggested, taking feedback (\"more like this\", \"not interested\"), and\nhandling requests the ranking model cannot, such as \"something short for tonight\" or \"what should\nI watch with my kids\", grounded in a **knowledge base** of the catalog's own metadata through\nhybrid retrieval.\n\nThis kind of recommendation concierge can run as an **agentic workflow** that calls the\nrecommendation API as a tool, respects a **tool execution policy** that limits which catalog\nactions it may take, and is delivered through **web chat, the mobile app's REST or WebSocket API\nchannel, or a digital human** for a more visual browsing experience. **Guardrails** keep the\nconversation inside age appropriate content policy, **analytics** and **response feedback** show\nwhich explanations and conversational picks users rate well, and **test suites** catch\nregressions when the underlying catalog or ranking API changes.",[155,158,161,164],{"question":156,"answer":157},"How do streaming services measure whether their recommendations actually work?","Raw engagement with recommended content overstates the effect, since recommended items also get more visibility. Netflix researchers, with one academic coauthor, isolated the causal effect with a structural model of viewing choices, validated by a randomized experiment that allocated members into eight treatment arms with different recommendation salience: their modeled counterfactual shows that replacing the current recommender with a simpler popularity based ranking would cut engagement by 12%, with most of the effect coming from effective targeting rather than exposure alone.",{"question":159,"answer":160},"Is a content recommendation engine high risk under the EU AI Act?","Usually not; recommendation and personalization are not listed in Annex III, so most deployments are minimal risk. The exception is manipulative or deceptive design that materially distorts behavior and causes significant harm, or that exploits a vulnerability linked to age, disability or a specific social or economic situation, which is a prohibited practice under Article 5, and matters most for surfaces reachable by children.",{"question":162,"answer":163},"How does a new title or a new user get good recommendations before there is any history?","This is the cold start problem. Modern systems place new content using its metadata (genre, cast, description) rather than waiting for interaction data, and place new users using onboarding preferences or early signals, blending in more behavioral data as it accumulates.",{"question":165,"answer":166},"Does personalization mean everyone sees a narrower catalog?","It can, if the system is optimized purely for predicted engagement, which tends to concentrate recommendations on already popular titles. Teams that also track catalog coverage and the share of engagement going to content that is not already popular, and build in a deliberate exploration budget, reduce this failure mode.",[168,169],"personalized-marketing-at-scale","churn-prediction-and-retention-offers","2026-09-28",[172],{"date":170,"note":173},"First published","content-recommendation-and-personalization",[176,207],{"title":177,"useCases":178,"organization":179,"vendors":184,"summary":185,"stage":186,"year":187,"channels":188,"languages":189,"metrics":190,"outcomeDisclosed":181,"sources":191,"verification":202,"grade":204,"id":205,"organizationSlug":206},"Netflix: a foundation model for personalized recommendation",[174],{"name":180,"anonymized":181,"country":182,"region":183,"industry":18},"Netflix, Inc.",false,"US","north-america",[],"Netflix built a foundation model that learns members' preferences from their comprehensive interaction history in one place, tokenizing user actions the way text is tokenized for a large language model, and shares those learned preferences with other models through embeddings or through fine tuning, instead of each model learning from scratch. Netflix says it sees \"promising results from downstream integrations\" of the model, without disclosing whether it serves members' recommendations directly, or how many surfaces or how much volume it covers; live deployment status beyond those integrations is not disclosed. Netflix's own research team, with one academic coauthor, separately published a causal study of the value of personalization in its recommender system: replacing the current recommender with a simpler popularity based ranking would cut member engagement by 12%, most of it from effective targeting rather than just showing content to more people.","pilot",2025,[26,27],[],[],[192,197],{"url":193,"title":194,"publisher":195,"archivedUrl":196},"https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39","Foundation Model for Personalized Recommendation","Netflix Technology Blog","https://web.archive.org/web/2026/https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39",{"url":198,"title":199,"publisher":200,"date":201},"https://arxiv.org/abs/2511.07280","The Value of Personalized Recommendations: Evidence from Netflix","arXiv","2025-11-10",{"level":203,"checkedAt":170},"source-verified","B","netflix-foundation-model-recommendation",null,{"title":208,"useCases":209,"organization":210,"vendors":213,"summary":214,"stage":215,"year":187,"channels":216,"languages":217,"metrics":218,"outcomeDisclosed":181,"sources":219,"verification":229,"grade":204,"id":230,"organizationSlug":206},"Spotify: Discover Weekly personalized playlist",[174],{"name":211,"anonymized":181,"country":212,"region":138,"industry":18},"Spotify","SE",[],"Discover Weekly, \"Spotify's first personalized playlist\", updates every Monday with songs and artists \"handpicked just for them\". Spotify's own support documentation lists Discover Weekly among its personalized playlists, which are \"created by Spotify's algorithms that look at factors like what the person is listening to and when, which songs they're adding to their playlists, the listening habits of people who have similar tastes, and much more\". Ten years after launch, Spotify says the playlist has driven \"more than 100 billion tracks streamed\" and \"ignites more than 56 million new artist discoveries\" every week, \"with 77% coming from emerging artists\". These are cumulative and weekly volume figures Spotify discloses about the whole feature, not a measured before and after effect. In 2025 Spotify added up to five genre options, \"personalized based on your listening history\", that generate a fresh 30 track playlist \"inspired by your selection\".","scaled",[26],[],[],[220,225],{"url":221,"title":222,"publisher":223,"date":224},"https://newsroom.spotify.com/2025-06-30/discover-weekly-turns-10-celebrating-100-billion-tracks-streamed-and-a-decade-of-personalized-discovery/","Discover Weekly Turns 10: Celebrating 100 Billion+ Tracks Streamed and a Decade of Personalized Discovery","Spotify Newsroom","2025-06-30",{"url":226,"title":227,"publisher":228},"https://support.spotify.com/us/artists/article/types-of-spotify-playlists/","Types of Spotify playlists","Spotify Support",{"level":203,"checkedAt":170},"spotify-discover-weekly-personalization",0,[],{"low":234,"high":235},3000000,22500000,[237,272,293,318],{"slug":168,"title":238,"shortTitle":239,"definition":240,"status":9,"industries":241,"functions":246,"patterns":248,"audience":28,"autonomy":250,"adoptionStage":30,"evidenceCount":251,"publicEvidenceCount":252,"organizations":253,"bestGrade":204,"headline":261,"lastVerified":271,"indexable":270},"AI marketing personalization at scale","Marketing personalization at scale","AI that runs marketing campaigns at the level of the individual: it decides for each customer which product, offer, message or content to show next across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the marketing team's engine across many campaigns and channels, not an agent that converses with the customer.",[242,243,18,244,245],"cross-industry","travel-and-hospitality","retail-and-ecommerce","banking",[20,247],"sales",[23,24,249],"content-generation","supervised-agent",8,7,[254,255,256,257,258,259,260],"Amazon","Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":262,"label":263,"unit":264,"n":265,"nUpTo":231,"kind":266,"value":267,"qualifier":268,"claimant":269,"organization":255,"vendorReported":270},"conversion-rate-uplift","Conversion uplift","percent",1,"reported",30,"exact","vendor",true,"2026-09-27",{"slug":169,"title":273,"shortTitle":274,"definition":275,"status":9,"industries":276,"functions":278,"patterns":280,"audience":28,"autonomy":250,"adoptionStage":30,"segment":282,"evidenceCount":283,"publicEvidenceCount":283,"organizations":284,"bestGrade":204,"headline":289,"lastVerified":271,"indexable":270},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals, explains the likely reason, and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[277],"telecommunications",[20,279,247,21],"customer-service",[24,23,281],"conversational-agent","middle-office",4,[285,286,287,288],"Etisalat","Telenet","Virgin Media O2","Vodafone UK",{"kpi":41,"label":290,"unit":264,"n":291,"nUpTo":231,"kind":266,"value":292,"qualifier":268,"claimant":269,"organization":286,"vendorReported":270},"Churn reduction",2,20,{"slug":294,"title":295,"shortTitle":296,"definition":297,"status":9,"industries":298,"functions":300,"patterns":301,"audience":302,"autonomy":303,"adoptionStage":304,"segment":305,"evidenceCount":306,"publicEvidenceCount":306,"organizations":307,"bestGrade":204,"headline":313,"lastVerified":271,"indexable":270},"next-best-action-for-advisors","AI next best action prompts for wealth advisors","Advisor next best action","An AI engine for wealth advisors, not customers, that scans an advisor's whole book and surfaces a short, ranked list of client specific prompts, such as idle cash, a maturing deposit, a concentration to review, a life event or an early sign of attrition, each with the reasoning and data behind it, for the advisor to act on or dismiss.",[299,245],"wealth-and-asset-management",[247,20,21],[23,24,249],"employee-facing","assist","early-adopters","front-office",5,[308,309,310,311,312],"CIMB Niaga","Citi","JPMorgan Chase","Morgan Stanley","UBS",{"kpi":314,"label":315,"unit":264,"n":265,"nUpTo":231,"kind":266,"value":316,"qualifier":268,"claimant":317,"organization":312,"vendorReported":181},"employee-adoption","Employee adoption",80,"organization",{"slug":319,"title":320,"shortTitle":321,"definition":322,"status":9,"industries":323,"functions":326,"patterns":327,"audience":302,"autonomy":329,"adoptionStage":304,"evidenceCount":330,"publicEvidenceCount":252,"organizations":331,"bestGrade":339,"headline":206,"lastVerified":271,"indexable":270},"outbound-sales-prospecting-agent","AI agent for outbound sales prospecting and personalized outreach","Outbound sales prospecting","An AI agent that researches target accounts and contacts, drafts personalized outbound outreach (emails, LinkedIn messages and call scripts) from the campaign, the prospect's context and the sales goals, and sequences the follow ups, with a sales development rep approving or sending every message.",[242,324,325],"technology","professional-services",[247,20],[249,328,23,24],"agentic-workflow","copilot",9,[332,333,334,335,336,337,338],"A-LIGN","ANS","Dun & Bradstreet","Lumen Technologies","Merge","Oyster","Unifonic","C",{"indexable":270,"reasons":341},[],[343,349,354,362,369,375,381,388,396,403,409,415,422,429,435,440,447,453,459,465,471,477,483,488,493,499,505,510,516,523,529,535,542,547],{"id":131,"label":344,"issuer":345,"region":138,"url":346,"description":347,"useCases":348,"indexable":270},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":132,"label":350,"issuer":345,"region":138,"url":351,"description":352,"useCases":353,"indexable":270},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":355,"label":356,"issuer":357,"region":358,"url":359,"description":360,"useCases":361,"indexable":270},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":363,"label":364,"issuer":365,"region":183,"url":366,"description":367,"useCases":368,"indexable":270},"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.",83,{"id":370,"label":371,"issuer":345,"region":138,"url":372,"description":373,"useCases":374,"indexable":270},"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":133,"label":376,"issuer":377,"region":138,"url":378,"description":379,"useCases":380,"indexable":270},"UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":382,"label":383,"issuer":384,"region":138,"url":385,"description":386,"useCases":387,"indexable":270},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":389,"label":390,"issuer":391,"region":392,"url":393,"description":394,"useCases":395,"indexable":270},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":397,"label":398,"issuer":399,"region":392,"url":400,"description":401,"useCases":402,"indexable":270},"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":404,"label":405,"issuer":406,"region":358,"url":407,"description":408,"useCases":292,"indexable":270},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":410,"label":411,"issuer":412,"region":183,"url":413,"description":414,"useCases":292,"indexable":270},"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":416,"label":417,"issuer":418,"region":138,"url":419,"description":420,"useCases":421,"indexable":270},"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.",16,{"id":423,"label":424,"issuer":425,"region":358,"url":426,"description":427,"useCases":428,"indexable":270},"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":430,"label":431,"issuer":345,"region":138,"url":432,"description":433,"useCases":434,"indexable":270},"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":436,"label":437,"issuer":345,"region":138,"url":438,"description":439,"useCases":434,"indexable":270},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":441,"label":442,"issuer":443,"region":183,"url":444,"description":445,"useCases":446,"indexable":270},"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":448,"label":449,"issuer":345,"region":138,"url":450,"description":451,"useCases":452,"indexable":270},"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":454,"label":455,"issuer":456,"region":183,"url":457,"description":458,"useCases":452,"indexable":270},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":460,"label":461,"issuer":462,"region":358,"url":463,"description":464,"useCases":452,"indexable":270},"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":466,"label":467,"issuer":345,"region":138,"url":468,"description":469,"useCases":470,"indexable":270},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":472,"label":473,"issuer":474,"region":183,"url":475,"description":476,"useCases":470,"indexable":270},"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":478,"label":479,"issuer":391,"region":392,"url":480,"description":481,"useCases":482,"indexable":270},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":484,"label":485,"issuer":345,"region":138,"url":486,"description":487,"useCases":482,"indexable":270},"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":489,"label":490,"issuer":345,"region":138,"url":491,"description":492,"useCases":482,"indexable":270},"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":494,"label":495,"issuer":496,"region":138,"url":497,"description":498,"useCases":330,"indexable":270},"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":500,"label":501,"issuer":502,"region":183,"url":503,"description":504,"useCases":251,"indexable":270},"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.",{"id":506,"label":507,"issuer":345,"region":138,"url":508,"description":509,"useCases":251,"indexable":270},"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":511,"label":512,"issuer":345,"region":138,"url":513,"description":514,"useCases":515,"indexable":270},"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.",6,{"id":517,"label":518,"issuer":519,"region":520,"url":521,"description":522,"useCases":306,"indexable":270},"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":524,"label":525,"issuer":526,"region":138,"url":527,"description":528,"useCases":283,"indexable":270},"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":530,"label":531,"issuer":532,"region":138,"url":533,"description":534,"useCases":283,"indexable":270},"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":536,"label":537,"issuer":538,"region":392,"url":539,"description":540,"useCases":541,"indexable":270},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":543,"label":544,"issuer":345,"region":138,"url":545,"description":546,"useCases":541,"indexable":270},"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":548,"label":549,"issuer":550,"region":183,"url":551,"description":552,"useCases":541,"indexable":270},"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.",1790598303450]