[{"data":1,"prerenderedAt":653},["ShallowReactive",2],{"uc-marketing-content-compliance-copilot":3,"uc-regulations":446},{"useCase":4,"evidence":221,"blitsAiDeployments":328,"benchmarks":329,"indicative":347,"related":350,"indexability":444,"includeUnpublished":227},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":24,"patterns":28,"channels":33,"audience":36,"autonomy":37,"adoptionStage":38,"problem":39,"problemStats":40,"howItWorks":41,"valueDrivers":42,"kpis":47,"indicativeValue":53,"macroEstimates":101,"feasibility":102,"implementation":115,"risk":162,"blitsAi":198,"faq":200,"related":210,"datePublished":216,"dateModified":216,"lastVerified":216,"changelog":217,"slug":220},"AI copilot for marketing content with compliance pre review","Marketing content and compliance","AI for marketing content and compliance review","A copilot drafts marketing copy from approved facts and checks it against ad rules before review. In a pilot, Ally's marketers reported 34% average time saved.","published","A copilot that drafts campaign copy, product explainers and social posts on brand and in the customer's language from approved product facts, then runs a first pass compliance check against advertising rules and required disclosures, flagging unsupported claims and missing warnings before a human in marketing compliance approves publication.",[12,13,14,15,16],"AI marketing copywriting","financial promotions review AI","marketing compliance review","generative AI content production","MLR pre review",[18,19,20,21,22,23],"cross-industry","banking","insurance","payments","wealth-and-asset-management","pharma-and-life-sciences",[25,26,27],"marketing","regulatory-compliance","legal",[29,30,31,32],"content-generation","rag-knowledge-assistant","classification-and-routing","translation",[34,35],"internal-tools","microsoft-teams","employee-facing","copilot","early-adopters","Marketing teams in regulated industries produce a steady stream of assets: emails, landing pages,\nsocial posts, product explainers, often in several languages and formats. Each one must be on\nbrand and must pass compliance review: in financial services a promotion must be clear, fair and\nnot misleading, show rates and fees correctly and carry the required risk warnings; in pharma,\nmedical, legal and regulatory review plays the same role. Review queues and rounds of redrafting\nbetween marketing and compliance add to the time it takes to launch a campaign.\n\nGenerative AI can draft quickly, but in these industries a fast draft that invents a rate, implies\na guarantee or drops a risk warning creates regulatory and conduct risk. The job is to speed up\ndrafting and give compliance a consistent first pass, while keeping product facts locked and a\nhuman approver accountable for every published asset.",[],"1. **Brief.** The marketer gives the product, audience, channel, language and message. The copilot\n   retrieves the approved product facts (rates, fees, eligibility), the brand voice guide and the\n   required disclosures for that product and channel.\n2. **Draft within the facts.** It drafts variants that use only the approved facts, inserting\n   numbers and disclosures from the source rather than generating them, and adapts length and tone\n   to the channel.\n3. **Compliance first pass.** A separate check compares the draft with the advertising rules and\n   house policy: unsupported or superlative claims, missing or misplaced risk warnings, balance of\n   benefits and risks, and terms that need a qualifier. Each flag cites the rule it relies on.\n4. **Localize.** Approved copy is translated and culturally adapted, and the compliance check runs\n   again in the target language.\n5. **Human approval.** Marketing edits, and a compliance reviewer approves or rejects with\n   comments; nothing is published without that approval.\n6. **Record.** The final asset, its claims, sources, flags and approvals are stored as a versioned\n   record for audit and for later complaints.",[43,44,45,46],"speed","employee-productivity","compliance","cost-to-serve",[48,49,50,51,52],"productivity-gain","cycle-time-days","cost-savings","processing-time-reduction","time-saved-per-task",{"referenceOrg":54,"inputs":55,"formula":96,"currency":97,"period":98,"resultLabel":99,"caveat":100},"A retail bank producing 1,500 marketing assets a year across two languages",[56,62,69,76,83,89],{"key":57,"label":58,"low":59,"high":59,"unit":60,"note":61},"assets","Marketing assets produced per year",1500,"assets per year","The reference organization. Replace with your own volume.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"hoursPerAsset","Marketing and compliance hours per asset today, including review rounds",4,8,"hours per asset","Editorial assumption.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"timeSaved","Share of those hours saved",0.15,0.3,"fraction of time","Conservative against the evidence on this page. Ally reports an average time saving of 34% in its marketing experiment and says the largest reductions came in early creative tasks such as research, first drafts and naming; none of the sources measures review time separately.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"hourlyCost","Blended marketing and compliance hour",60,100,"USD per hour","Editorial assumption. Replace with your own rate.",{"key":84,"label":85,"low":86,"high":87,"unit":88,"note":68},"agencySpend","External copywriting and translation spend per year",200000,600000,"USD per year",{"key":90,"label":91,"low":92,"high":93,"unit":94,"note":95},"agencyReduction","Reduction in that external spend",0.1,0.25,"fraction of spend","Editorial assumption; Klarna reports a 25% cut in external marketing supplier spend, used here as the high end.","assets * hoursPerAsset * timeSaved * hourlyCost + agencySpend * agencyReduction","USD","per year","Staff time released plus reduced agency spend","It leaves out any revenue effect of more or better targeted campaigns, the value of fewer compliance breaches and complaints, and the cost of maintaining the approved fact base and rule library.",[],{"complexity":103,"complexityNote":104,"dataPrerequisites":105,"integrations":110},"low","Drafting is easy to start. The work that makes it safe is a maintained source of approved product facts, a written rule library for the compliance check, and a workflow that records approvals.",[106,107,108,109],"Approved product facts (rates, fees, eligibility, terms) with an owner and effective dates","Brand voice and style guide with examples of approved assets","Required disclosures and risk warnings per product, channel and market","The advertising rules and house policy the compliance team applies, written as checkable rules",[111,112,113,114],"Content management system or digital asset management","Marketing automation and campaign tools","Approval workflow used by marketing compliance","Product information source for current rates and fees",{"steps":116,"guardrails":135,"humanInTheLoop":142,"kpisToInstrument":143,"failureModes":149},[117,120,123,126,129,132],{"title":118,"detail":119},"Lock the facts first","Build a single source of approved product facts and disclosures that the copilot reads from. Numbers and warnings are inserted from that source, never generated.",{"title":121,"detail":122},"Turn the compliance manual into checks","With compliance, write the rules the first pass applies (claims that need substantiation, banned phrases, warning placement, balance of risks and benefits) and test them on past approved and rejected assets.",{"title":124,"detail":125},"Start with one channel and product family","Pick high volume, lower risk assets such as service emails or social posts for savings products, and measure drafting time, review rounds and rejection reasons.",{"title":127,"detail":128},"Keep approval where it is","Plug the copilot into the existing approval workflow so every asset still gets a named approver. In Ally's experiment, an AI drafted blog article still went through its established regulatory review.",{"title":130,"detail":131},"Measure review rounds, not just drafting","Drafting gains are the easiest to see. Measure time in review as well, track first time approval rate and rejection reasons, and feed recurring flags back into the rules.",{"title":133,"detail":134},"Extend to images and languages carefully","Add image generation and translation once text is stable, with disclosure of AI generated imagery where required and a compliance check in each language.",[136,137,138,139,140,141],"Rates, fees, returns and other product numbers come only from the approved fact source, never from the model","The model may not promise guarantees, returns or outcomes the product terms do not support","Every asset gets a named human approver in marketing compliance before publication","Compliance flags cite the rule they rely on, and a pass by the first check never replaces human review","AI generated or manipulated images and video are marked as required by law and house policy","A versioned record of each asset's claims, sources and approvals is retained","Marketers own the message and edit every draft. A compliance reviewer approves every published asset and every material change, and product owners approve the fact base. The AI drafts and flags; it does not approve.",[144,145,146,147,148],"Time from brief to approved asset, before and after","Review rounds per asset and first time approval rate","Compliance flags per asset and share confirmed by reviewers","Post publication issues (withdrawn assets, complaints, regulator queries)","External agency and translation spend",[150,153,156,159],{"title":151,"detail":152},"Invented facts","The model writes a rate, fee or benefit that is not in the approved facts. Insert numbers from the fact source and block publication if a number in the draft has no source.",{"title":154,"detail":155},"Rubber stamp review","Reviewers trust the automated first pass and skim. Keep reviewers accountable, sample assets the check passed, and show them what the check did and did not cover.",{"title":157,"detail":158},"Outdated disclosures","A rate or warning changes but the fact source does not. Give every fact an owner and effective date and expire stale ones.",{"title":160,"detail":161},"Volume without control","Faster drafting multiplies assets and variants beyond what compliance can review. Plan review capacity together with production.",{"euAiAct":163,"regulations":166,"guidance":173,"controls":191,"incidents":197},{"tier":164,"basis":165},"limited","An internal drafting and review aid that makes no decisions about people. Article 50 transparency duties apply to generated content: providers must mark synthetic content, and deployers must disclose deep fake images, audio or video. Personalized targeting of individuals is governed mainly by data protection and consumer law rather than the AI Act.",[167,168,169,170,171,172],"eu-ai-act","gdpr","uk-consumer-duty","iso-42001","mifid-ii","eu-idd",[174,180,186],{"title":175,"issuer":176,"region":177,"url":178,"note":179},"Regulating financial promotions and adverts","Financial Conduct Authority","europe","https://www.fca.org.uk/firms/financial-promotions-adverts","All financial promotions must be clear, fair and not misleading regardless of media type, which is the standard AI drafted promotions are reviewed against in the UK.",{"title":181,"issuer":182,"region":183,"url":184,"note":185},"RG 234 Advertising financial products and services (including credit)","Australian Securities and Investments Commission","asia-pacific","https://asic.gov.au/regulatory-resources/find-a-document/regulatory-guides/rg-234-advertising-financial-products-and-services-including-credit-good-practice-guidance/","Guidance, reissued in June 2026, that helps promoters and publishers of financial product and credit advertising avoid false or misleading statements, a useful basis for the compliance rule library.",{"title":187,"issuer":188,"region":177,"url":189,"note":190},"Article 50: Transparency obligations for providers and deployers of certain AI systems","European Union","https://artificialintelligenceact.eu/article/50/","Covers marking of AI generated content and disclosure of deep fakes, relevant when campaigns use generated imagery, audio or video.",[192,193,194,195,196],"Approved product fact base with owners, effective dates and change control","Written rule library for the compliance first pass, reviewed by compliance","Named approval recorded for every published asset","Retention of each asset version with its claims, sources, flags and approvals","Inventory entry for the copilot with an accountable owner",[],{"howToBuild":199},"On Blits.ai the copilot is two **AI agents**: a drafting agent and a separate compliance check\nagent, each with its own **prompt versioning**. Both draw on a **knowledge base** with the\napproved product facts, brand guide, disclosures and the compliance rule library, retrieved with\n**hybrid search**, and **custom functions** pull current rates and fees from the product system so\nnumbers are inserted, not generated. **Structured output** returns the draft, its claims with\nsources and the compliance flags with the rule each relies on.\n\nAn **agentic workflow** routes drafts to the compliance reviewer with **human in the loop\napproval**, and **run history with a full audit trail** keeps every version and decision.\nMarketers work in **Microsoft Teams** or the web; **machine translation** and multi language\nsupport cover localization, and **guardrails** block banned claims before a draft reaches a\nreviewer. **Test suites** replay past approved and rejected assets after every change, and the\nplatform is model agnostic, so drafting and checking can run on different models.",[201,204,207],{"question":202,"answer":203},"How much time does generative AI save in regulated marketing?","Ally reports an average time saving of 34% in a month long experiment with its marketers; it says the largest reductions, of up to two to three weeks, came in early stages such as research, first drafts and naming. Klarna reports cutting its image development cycle from six weeks to seven days, including brand and legal compliance checks. None of these sources measures review time separately, so track review rounds as well as drafting time.",{"question":205,"answer":206},"Can AI approve financial promotions?","No. It can check drafts against written rules and flag issues with the rule cited, but a named compliance reviewer must approve every published asset. Promotions must still be clear, fair and not misleading, and the firm remains accountable.",{"question":208,"answer":209},"How do you stop the model inventing rates or guarantees?","Keep product numbers in an approved fact source and insert them into the copy rather than letting the model write them, block any draft that contains a number without a source, and add banned claims such as guarantees to the compliance rules.",[211,212,213,214,215],"personalized-marketing-at-scale","regulatory-horizon-scanning","policy-drafting-and-gap-analysis","investment-research-summarization","offers-and-rewards-agent","2026-09-27",[218],{"date":216,"note":219},"First published","marketing-content-compliance-copilot",[222,270,300],{"title":223,"useCases":224,"organization":225,"vendors":229,"summary":239,"stage":240,"year":241,"channels":242,"languages":243,"metrics":244,"outcomeDisclosed":259,"sources":260,"verification":264,"grade":267,"id":268,"organizationSlug":269},"Klarna: generative AI for marketing copy and imagery",[220],{"name":226,"anonymized":227,"country":228,"region":177,"industry":21},"Klarna",false,"SE",[230,233,235,237],{"name":231,"role":232},"OpenAI","model-provider",{"name":234,"role":232},"Midjourney",{"name":236,"role":232},"Adobe",{"name":226,"role":238},"in-house","Klarna uses generative AI across marketing: an in house copywriting tool (Copy Assistant) for most of its copy, and image generation tools for campaign imagery, while reducing spend on external agencies for translation, production, CRM and social. Klarna says the faster image cycle includes checks for brand consistency, image quality and legal compliance. It attributes a share of its sales and marketing savings to AI.","scaled",2024,[34],[],[245,253],{"kpi":50,"value":246,"unit":247,"currency":97,"qualifier":248,"period":249,"claimant":250,"quote":251,"sourceUrl":252},10000000,"currency","approximately","annualized, as of Q1 2024","organization","AI is responsible for 37% of the cost savings, or about $10 million on an annualized basis.","https://www.klarna.com/international/press/ai-helps-klarna-cut-marketing-agency-spend-by-25-and-run-more-campaigns/",{"kpi":49,"value":254,"unit":255,"qualifier":256,"baseline":257,"claimant":250,"quote":258,"sourceUrl":252},7,"days","exact","image development cycle of 6 weeks before generative AI","Increased Efficiency and Creativity: Generated over 1,000 images in the first three months of 2024 using genAI, reducing the image development cycle from 6 weeks to just 7 days.",true,[261],{"url":252,"title":262,"publisher":226,"date":263},"AI helps Klarna cut marketing agency spend by 25% and run more campaigns","2024-05-28",{"level":265,"checkedAt":266},"source-verified","2026-09-26","B","klarna-generative-ai-marketing-production",null,{"title":271,"useCases":272,"organization":273,"vendors":277,"summary":280,"stage":281,"year":282,"channels":283,"languages":284,"metrics":286,"outcomeDisclosed":259,"sources":294,"verification":298,"grade":267,"id":299,"organizationSlug":269},"Ally Financial: generative AI for marketing content with regulatory review",[220],{"name":274,"anonymized":227,"country":275,"region":276,"industry":19},"Ally Financial","US","north-america",[278],{"name":279,"role":238},"Ally","Ally ran a month long experiment in which a group of marketers used its in house Ally.ai platform, built on enterprise large language models, for tasks such as research, naming and first drafts of advertising copy, video scripts and social posts. In one example, an AI first draft of a blog article cut the time to create and edit it from four hours to one; the article was edited by Ally's content writers and still went through the bank's established regulatory review. Ally reported an average time saving, and says the largest reductions, of up to two to three weeks, came in early stages of the creative process such as research, first drafts and naming.","pilot",2023,[34],[285],"en",[287],{"kpi":48,"value":288,"unit":289,"qualifier":256,"period":290,"baseline":291,"claimant":250,"quote":292,"sourceUrl":293},34,"percent","month long experiment","time to produce creative campaigns and content without AI","Using the Ally.ai platform's large language model (LLM) chat and prompt functionality, a select group of marketers were able to reduce the time needed to produce creative campaigns and content by up to 2-3 weeks and reported an average time savings of 34%, compared to typical processes without AI.","https://media.ally.com/2023-11-16-Do-It-Right-with-AI-Ally-creators-experiment-with-generative-AI-in-marketing-test-case",[295],{"url":293,"title":296,"publisher":274,"date":297},"'Do It Right' with AI: Ally creators experiment with generative AI in marketing test case","2023-11-16",{"level":265,"checkedAt":266},"ally-financial-generative-ai-marketing-content",{"title":301,"useCases":302,"organization":303,"vendors":305,"summary":309,"stage":310,"year":311,"channels":312,"languages":313,"metrics":314,"outcomeDisclosed":259,"sources":315,"verification":324,"grade":325,"id":326,"organizationSlug":327},"JPMorgan Chase: AI generated marketing copy with Persado",[220],{"name":304,"anonymized":227,"country":275,"region":276,"industry":19},"JPMorgan Chase",[306],{"name":307,"role":308},"Persado","platform","After a pilot on Card and Mortgage marketing that started in 2016, JPMorgan Chase signed a five year, enterprise wide agreement in 2019 to use Persado's AI to write copy for direct response campaigns in personal banking, home lending and wealth management and for digital advertising. The pilot used Persado's Message Machine, a marketing language knowledge base of more than one million tagged and scored words and phrases, and the announcement does not describe how copy passes the bank's marketing compliance review.","production",2019,[34],[285],[],[316,320],{"url":317,"title":318,"publisher":307,"date":319},"https://www.persado.com/press-releases/jpmorgan-chase-announces-five-year-deal-with-persado-for-ai-powered-marketing-capabilities/","JPMorgan Chase Announces Five-Year Deal with Persado For AI-Powered Marketing Capabilities","2019-07-30",{"url":321,"title":322,"publisher":323},"https://www.marketingdive.com/news/jpmorgan-chase-inks-5-year-deal-to-generate-marketing-copy-via-ai/559836/","JPMorgan Chase inks 5-year deal to generate marketing copy via AI","Marketing Dive",{"level":265,"checkedAt":266},"C","jpmorgan-chase-ai-marketing-copy","jpmorgan-chase",2,[330,337,342],{"kpi":50,"label":331,"unit":247,"currency":97,"aggregate":227,"higherIsBetter":259,"n":332,"nUpTo":333,"median":246,"min":246,"max":246,"byClaimant":334,"vendorOnly":227,"points":335},"Cost savings",1,0,{"organization":332,"vendor":333,"regulator":333,"independent":333},[336],{"evidenceId":268,"organization":226,"value":246,"qualifier":248,"claimant":250,"grade":267,"pooled":259},{"kpi":49,"label":338,"unit":255,"aggregate":227,"higherIsBetter":227,"n":332,"nUpTo":333,"median":254,"min":254,"max":254,"byClaimant":339,"vendorOnly":227,"points":340},"Cycle time",{"organization":332,"vendor":333,"regulator":333,"independent":333},[341],{"evidenceId":268,"organization":226,"value":254,"qualifier":256,"claimant":250,"grade":267,"pooled":259},{"kpi":48,"label":343,"unit":289,"aggregate":259,"higherIsBetter":259,"n":332,"nUpTo":333,"median":288,"min":288,"max":288,"byClaimant":344,"vendorOnly":227,"points":345},"Productivity gain",{"organization":332,"vendor":333,"regulator":333,"independent":333},[346],{"evidenceId":299,"organization":274,"value":288,"qualifier":256,"claimant":250,"grade":267,"pooled":259},{"low":348,"high":349},74000,510000,[351,381,396,411,430],{"slug":211,"title":352,"shortTitle":353,"definition":354,"status":9,"industries":355,"functions":359,"patterns":361,"audience":364,"autonomy":365,"adoptionStage":366,"evidenceCount":66,"publicEvidenceCount":254,"organizations":367,"bestGrade":267,"headline":375,"lastVerified":216,"indexable":259},"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.",[18,356,357,358,19],"travel-and-hospitality","media-and-entertainment","retail-and-ecommerce",[25,360],"sales",[362,363,29],"recommendation-and-personalization","prediction-and-scoring","back-office","supervised-agent","mainstream",[368,369,370,371,372,373,374],"Amazon","Catchtable","Commonwealth Bank of Australia","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":376,"label":377,"unit":289,"n":332,"nUpTo":333,"kind":378,"value":379,"qualifier":256,"claimant":380,"organization":369,"vendorReported":259},"conversion-rate-uplift","Conversion uplift","reported",30,"vendor",{"slug":212,"title":382,"shortTitle":383,"definition":384,"status":9,"industries":385,"functions":387,"patterns":389,"audience":36,"autonomy":393,"adoptionStage":38,"segment":45,"evidenceCount":65,"publicEvidenceCount":328,"organizations":394,"bestGrade":267,"headline":269,"lastVerified":216,"indexable":259},"AI regulatory horizon scanning and obligation mapping","Regulatory horizon scanning","An AI system that continuously reads publications from the regulators and standard setters an organization answers to, classifies each item by relevance and urgency, breaks new rules into individual obligations and maps them to the internal policies and controls that meet them, so compliance owners see what changed and where the gaps are.",[18,19,20,21,22,23,386],"government",[26,27,388],"risk-management",[31,390,30,391,392],"document-processing","summarization","agentic-workflow","assist",[176,395],"Administration for Children and Families",{"slug":213,"title":397,"shortTitle":398,"definition":399,"status":9,"industries":400,"functions":402,"patterns":404,"audience":36,"autonomy":37,"adoptionStage":405,"segment":406,"evidenceCount":407,"publicEvidenceCount":407,"organizations":408,"bestGrade":267,"headline":269,"lastVerified":266,"indexable":259},"AI for policy drafting and policy gap analysis","Policy drafting and gaps","An assistant that takes a new or changed obligation, finds every internal policy, standard and procedure it touches, flags clauses that now conflict or are silent, and drafts the updated wording in house style as a redline for the policy owner to approve.",[18,19,20,401,386],"capital-markets",[26,27,403],"knowledge-management",[30,29,390,391],"emerging","second-line",3,[409,395,410],"Federal Deposit Insurance Corporation","Health Resources and Services Administration",{"slug":214,"title":412,"shortTitle":413,"definition":414,"status":9,"industries":415,"functions":416,"patterns":418,"audience":36,"autonomy":37,"adoptionStage":38,"segment":419,"evidenceCount":65,"publicEvidenceCount":65,"organizations":420,"bestGrade":267,"headline":425,"lastVerified":216,"indexable":259},"AI summaries of investment research and the house view","Research summaries","An AI assistant that condenses long research reports, overnight market moves and the house view into short, sourced briefings for advisors and analysts, answers \"what is our view on X\" on demand, and adapts approved research for different client segments and languages, with every figure traced to the original research.",[22,401,19],[417,360,403],"analytics-and-reporting",[391,30,29,32],"front-office",[421,422,423,424],"Citi","Deutsche Bank","Morgan Stanley","UBS",{"kpi":52,"label":426,"unit":427,"n":333,"nUpTo":332,"kind":378,"value":428,"qualifier":429,"claimant":250,"organization":422,"vendorReported":227},"Time saved per task","minutes",120,"up-to",{"slug":215,"title":431,"shortTitle":432,"definition":433,"status":9,"industries":434,"functions":435,"patterns":437,"audience":439,"autonomy":440,"adoptionStage":366,"segment":419,"evidenceCount":66,"publicEvidenceCount":407,"organizations":441,"bestGrade":267,"headline":269,"lastVerified":216,"indexable":259},"AI agent for personalized offers and rewards","Offers and rewards","A customer facing AI agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment from their transactions and context, delivers it in the app, in messaging or through a colleague, and helps the customer understand, track and redeem rewards in conversation. Unlike campaign personalization, it works inside the customer's own account and loyalty relationship, one moment at a time.",[19,21],[25,360,436],"customer-service",[362,363,438],"conversational-agent","customer-facing","autonomous",[442,370,443],"Bank of America","DBS Bank",{"indexable":259,"reasons":445},[],[447,452,457,464,471,477,484,489,496,503,510,516,523,530,536,541,548,554,560,566,572,578,584,588,593,600,606,611,616,624,630,636,642,647],{"id":167,"label":448,"issuer":188,"region":177,"url":449,"description":450,"useCases":451,"indexable":259},"EU AI Act","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":168,"label":453,"issuer":188,"region":177,"url":454,"description":455,"useCases":456,"indexable":259},"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":170,"label":458,"issuer":459,"region":460,"url":461,"description":462,"useCases":463,"indexable":259},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":465,"label":466,"issuer":467,"region":276,"url":468,"description":469,"useCases":470,"indexable":259},"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":472,"label":473,"issuer":188,"region":177,"url":474,"description":475,"useCases":476,"indexable":259},"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":478,"label":479,"issuer":480,"region":177,"url":481,"description":482,"useCases":483,"indexable":259},"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.",64,{"id":169,"label":485,"issuer":176,"region":177,"url":486,"description":487,"useCases":488,"indexable":259},"FCA Consumer Duty","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":490,"label":491,"issuer":492,"region":183,"url":493,"description":494,"useCases":495,"indexable":259},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":497,"label":498,"issuer":499,"region":183,"url":500,"description":501,"useCases":502,"indexable":259},"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":504,"label":505,"issuer":506,"region":460,"url":507,"description":508,"useCases":509,"indexable":259},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":511,"label":512,"issuer":513,"region":276,"url":514,"description":515,"useCases":509,"indexable":259},"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":517,"label":518,"issuer":519,"region":177,"url":520,"description":521,"useCases":522,"indexable":259},"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":524,"label":525,"issuer":526,"region":460,"url":527,"description":528,"useCases":529,"indexable":259},"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":531,"label":532,"issuer":188,"region":177,"url":533,"description":534,"useCases":535,"indexable":259},"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":537,"label":538,"issuer":188,"region":177,"url":539,"description":540,"useCases":535,"indexable":259},"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":542,"label":543,"issuer":544,"region":276,"url":545,"description":546,"useCases":547,"indexable":259},"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":549,"label":550,"issuer":188,"region":177,"url":551,"description":552,"useCases":553,"indexable":259},"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":555,"label":556,"issuer":557,"region":276,"url":558,"description":559,"useCases":553,"indexable":259},"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":561,"label":562,"issuer":563,"region":460,"url":564,"description":565,"useCases":553,"indexable":259},"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":567,"label":568,"issuer":188,"region":177,"url":569,"description":570,"useCases":571,"indexable":259},"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":573,"label":574,"issuer":575,"region":276,"url":576,"description":577,"useCases":571,"indexable":259},"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":579,"label":580,"issuer":492,"region":183,"url":581,"description":582,"useCases":583,"indexable":259},"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":171,"label":585,"issuer":188,"region":177,"url":586,"description":587,"useCases":583,"indexable":259},"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":589,"label":590,"issuer":188,"region":177,"url":591,"description":592,"useCases":583,"indexable":259},"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":594,"label":595,"issuer":596,"region":177,"url":597,"description":598,"useCases":599,"indexable":259},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":601,"label":602,"issuer":603,"region":276,"url":604,"description":605,"useCases":66,"indexable":259},"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":607,"label":608,"issuer":188,"region":177,"url":609,"description":610,"useCases":66,"indexable":259},"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":172,"label":612,"issuer":188,"region":177,"url":613,"description":614,"useCases":615,"indexable":259},"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":617,"label":618,"issuer":619,"region":620,"url":621,"description":622,"useCases":623,"indexable":259},"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":625,"label":626,"issuer":627,"region":177,"url":628,"description":629,"useCases":65,"indexable":259},"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":631,"label":632,"issuer":633,"region":177,"url":634,"description":635,"useCases":65,"indexable":259},"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":637,"label":638,"issuer":639,"region":183,"url":640,"description":641,"useCases":407,"indexable":259},"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":643,"label":644,"issuer":188,"region":177,"url":645,"description":646,"useCases":407,"indexable":259},"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":648,"label":649,"issuer":650,"region":276,"url":651,"description":652,"useCases":407,"indexable":259},"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.",1790598298074]