[{"data":1,"prerenderedAt":553},["ShallowReactive",2],{"uc-portfolio-reporting-and-commentary":3,"uc-regulations":349},{"useCase":4,"evidence":194,"blitsAiDeployments":254,"benchmarks":255,"indicative":256,"related":259,"indexability":347,"includeUnpublished":201},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":47,"macroEstimates":80,"feasibility":81,"implementation":94,"risk":137,"blitsAi":173,"faq":175,"related":185,"datePublished":189,"dateModified":189,"lastVerified":189,"changelog":190,"slug":193},"AI generated client portfolio reports and commentary","Portfolio commentary","AI portfolio commentary and client reporting","AI drafts client portfolio commentary from system data for human approval. Morgan Stanley adopted BlackRock's Aladdin Wealth Auto Commentary for talking points.","published","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.",[12,13,14,15],"automated portfolio commentary","AI client reporting","fund commentary generation","investment commentary drafting",[17,18],"wealth-and-asset-management","banking",[20,21,22],"analytics-and-reporting","customer-service","operations",[24,25,26],"content-generation","summarization","translation",[28,29],"internal-tools","email","back-office","copilot","early-adopters","middle-office","Clients expect a periodic report that explains what happened to their money and why, not just a\ntable of numbers. Writing that narrative is slow: portfolio managers and specialist writers draft\ncommentary for each strategy, and advisors adapt it for individual clients, often in several\nlanguages. At Quilter, specialist writers interview a portfolio manager and then need a few days\nto turn that into a commentary; Neurons Lab describes a monthly investor report that took an\ninvestment firm 20 days to complete. The result can be generic text that says little about the\nclient's own portfolio, or reports that reach clients well after the period they describe.\n\nThe risk is also real. A commentary is a client communication under conduct rules; a wrong number,\nan unbalanced claim about performance or a forward looking statement without the right disclaimer\nis a compliance issue.",[],"1. **Take numbers from the record.** Performance, attribution, holdings and transactions come from\n   the portfolio accounting and performance systems, not from the model.\n2. **Add the context.** The house view, market commentary and the portfolio manager's notes are\n   retrieved for the period.\n3. **Draft within a template.** The model writes the narrative sections in an approved structure,\n   inserting figures from the data and explaining drivers in plain language, per client or per\n   strategy.\n4. **Localize.** The approved narrative is adapted to the client's language and segment.\n5. **Check and approve.** Automated checks compare every number in the text with the source data\n   and flag banned phrases; a reviewer approves before the report is assembled and delivered, and a\n   log of sources and edits is kept.",[38,39,40,41],"employee-productivity","speed","customer-experience","compliance",[43,44,45,46],"cycle-time-days","processing-time-reduction","error-reduction","hours-saved",{"referenceOrg":48,"inputs":49,"formula":75,"currency":76,"period":77,"resultLabel":78,"caveat":79},"A wealth manager sending quarterly reports to 20,000 client portfolios",[50,55,61,68],{"key":51,"label":52,"low":53,"high":53,"unit":51,"note":54},"portfolios","Client portfolios with a personalized commentary",20000,"The reference firm.",{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"reportsPerYear","Reports per portfolio per year",4,"reports per year","Quarterly reporting.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"minutesSaved","Minutes of drafting and adaptation saved per report",10,30,"minutes per report","Editorial assumption, replace with your own. No deployment on this page publishes a measured saving per report; the only test on this page (Quilter) was a single commentary drafted in about 45 minutes of prompting and editing instead of a few days.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"hourlyCost","Fully loaded cost per hour of the staff who write and adapt commentary",60,120,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","portfolios * reportsPerYear * minutesSaved / 60 * hourlyCost","USD","per year","Value of staff time released from commentary drafting","Where commentary is not personalized per client today, the saving may show up as better reports rather than fewer hours. The figure leaves out the review effort, platform costs and the effect on client retention.",[],{"complexity":82,"complexityNote":83,"dataPrerequisites":84,"integrations":89},"medium","Data is the hard part: reliable performance and attribution data per portfolio, mapped to a template. The generation is well understood when numbers are inserted, not generated, and output goes through review.",[85,86,87,88],"Performance, attribution and holdings data per portfolio and period","House view and market commentary for the period","Approved templates, disclaimers and banned phrases per market","Client language and segment preferences",[90,91,92,93],"Portfolio accounting and performance measurement systems","Research and CIO content","Report assembly and document generation","Client portal or email for delivery",{"steps":95,"guardrails":111,"humanInTheLoop":117,"kpisToInstrument":118,"failureModes":124},[96,99,102,105,108],{"title":97,"detail":98},"Start at strategy level","Generate commentary per strategy or model portfolio first, where one reviewed text serves many clients, before personalizing per client.",{"title":100,"detail":101},"Insert numbers, do not generate them","Pass figures as structured data and require the model to reference them, then compare every number in the output with the source automatically.",{"title":103,"detail":104},"Agree the compliance rules up front","Encode disclaimers, fair and balanced presentation rules and banned phrases, and have compliance approve the templates.",{"title":106,"detail":107},"Review by exception at scale","When personalizing per client, review all outputs at first, then move to risk based sampling once error rates are proven low, keeping full review for outliers.",{"title":109,"detail":110},"Keep the decision log","Store the data, retrieved context, draft, edits and approver for every report, so any sentence can be traced later.",[112,113,114,115,116],"Every figure from the system of record, checked automatically against the source","Approved templates, disclaimers and banned phrase lists per market","Human approval before delivery, with risk based sampling only after proven accuracy","No forecasts or promises beyond the approved house view wording","Log of data, context, draft and approver for every report","Portfolio managers or specialist writers approve strategy commentary; reviewers or advisors approve personalized reports; compliance approves templates and samples output.",[119,120,121,122,123],"Days from period end to report delivery","Number mismatches caught by automated checks per thousand reports","Reviewer edit rate and rejection reasons","Share of clients receiving personalized commentary","Client feedback or complaints about reports",[125,128,131,134],{"title":126,"detail":127},"A wrong number reaches a client","The model restates or rounds a figure incorrectly. Insert numbers from data and check every figure before release.",{"title":129,"detail":130},"Unbalanced performance claims","Commentary highlights gains and glosses over losses. Encode fair and balanced rules and review for them.",{"title":132,"detail":133},"Generic text at scale","Personalized reports all say the same thing. Require references to the client's own holdings and drivers.",{"title":135,"detail":136},"Review becomes a formality","Reviewers approve thousands of reports without reading them. Use risk based sampling with clear accountability.",{"euAiAct":138,"regulations":141,"guidance":149,"controls":166,"incidents":172},{"tier":139,"basis":140},"context-dependent","Drafting client reports for human review is not listed in Annex III and is not a practice prohibited by Article 5, so the tier turns on the firm's role under Article 50. A firm that deploys a third party generator (for example a feature of its portfolio platform) for private client reports has no Article 50 duty: the Article 50(4) disclosure duty covers AI generated text published to inform the public on matters of public interest, which private client reports are not, and it lapses anyway after human review under editorial responsibility. For that firm the tier is minimal. A firm that builds the generating system or places it on the market under its own name is a provider under Article 50(2) and must mark the synthetic text in a machine readable format; drafting whole commentaries goes beyond the exemption for an assistive function for standard editing, so for that firm the tier is limited.",[142,143,144,145,146,147,148],"eu-ai-act","gdpr","uk-consumer-duty","dora","mas-ai-risk-management","iso-42001","mifid-ii",[150,156,161],{"title":151,"issuer":152,"region":153,"url":154,"note":155},"ESMA public statement on the use of AI in the provision of retail investment services","European Securities and Markets Authority","europe","https://www.esma.europa.eu/sites/default/files/2024-05/ESMA35-335435667-5924__Public_Statement_on_AI_and_investment_services.pdf","MiFID II duties on accurate, fair and not misleading client information apply to AI drafted communications, and ESMA expects records of AI use.",{"title":157,"issuer":158,"region":153,"url":159,"note":160},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","https://artificialintelligenceact.eu/article/50/","Providers must mark AI generated text in a machine readable format (paragraph 2); the disclosure duty for text published on matters of public interest does not apply after human review under editorial responsibility (paragraph 4).",{"title":162,"issuer":163,"region":153,"url":164,"note":165},"PRIN 2A.5 Consumer Duty: retail customer outcome on consumer understanding","Financial Conduct Authority","https://www.handbook.fca.org.uk/handbook/PRIN/2A/5.html","The consumer understanding outcome of the Consumer Duty applies to how performance and risks are explained in reports to retail clients.",[167,168,169,170,171],"Automated number reconciliation between text and source data","Compliance approved templates with version control","Decision log per report kept for the retention period","Risk based sampling with documented reviewer accountability","Inventory entry for the generation system with an accountable owner",[],{"howToBuild":174},"On Blits.ai an **agentic workflow** runs after period end: **custom functions** or a **SQL\nknowledge base** read performance and holdings from the firm's systems, a **knowledge base**\nsupplies the house view and manager notes, and an **AI agent** with **structured output** drafts\neach section in the approved template, referencing figures by field. A second function compares\nevery number in the draft with the source data and checks that the required disclaimers are\npresent.\n\nDrafts pause for **human in the loop** approval, and **multi language** support and **machine\ntranslation** adapt approved text per market. **Guardrails** block banned phrases, the workflow's\n**audit trail** and downloadable run data serve as the decision log,\nand **test suites** replay reference portfolios on every template, prompt or model change.",[176,179,182],{"question":177,"answer":178},"Who is doing this today?","BlackRock announced in October 2025 that Morgan Stanley Wealth Management's Portfolio Risk Platform would be the first to implement Auto Commentary, a generative AI feature of Aladdin Wealth, with advisors getting access from that month. The tool drafts concise talking points for advisors from risk analytics, the Chief Investment Office outlook and the client's portfolio, not full client reports. Quilter tested turning a portfolio manager interview into an investment commentary in about 15 minutes of prompting and half an hour of editing instead of a few days, which it called a one off test.",{"question":180,"answer":181},"How do you prevent wrong numbers?","Never let the model produce figures. Pass them in from the system of record, reference them in the draft, and reconcile every number automatically before a human reviews the text.",{"question":183,"answer":184},"Can personalized reports go out without review?","Start with full review. Move to risk based sampling only when automated checks and error rates justify it, and keep the decision log for every report.",[186,187,188],"investment-research-summarization","portfolio-drift-monitoring-and-rebalancing","wealth-advisor-knowledge-assistant","2026-09-27",[191],{"date":189,"note":192},"First published","portfolio-reporting-and-commentary",[195,229],{"title":196,"useCases":197,"organization":199,"vendors":204,"summary":208,"stage":209,"year":210,"channels":211,"languages":212,"metrics":214,"outcomeDisclosed":201,"sources":215,"verification":224,"grade":226,"id":227,"organizationSlug":228},"Morgan Stanley: BlackRock Aladdin Wealth Auto Commentary in its Portfolio Risk Platform",[193,187,198],"suitability-assessment-assistant",{"name":200,"anonymized":201,"country":202,"region":203,"industry":17},"Morgan Stanley",false,"US","north-america",[205],{"name":206,"role":207},"BlackRock","platform","BlackRock announced on 2 October 2025 that Morgan Stanley Wealth Management's Portfolio Risk Platform would be the first to implement Auto Commentary, a generative AI feature of Aladdin Wealth, with advisors in the U.S. getting access from October. The tool combines Aladdin risk analytics, the firm's Chief Investment Office outlook and the client's holdings and investment preferences to draft concise insights for the advisor, highlighting issues such as overweights or misalignment with the client's objectives or the firm's market view. Trade press describes the output as bullet point insights inside a template, not full scripts or emails, so it supports the advisor's conversation rather than producing a finished client report. No outcome figures were published.","announced",2025,[28],[213],"en",[],[216,220],{"url":217,"title":218,"publisher":206,"date":219},"https://www.blackrock.com/aladdin/discover/press-release/aladdin-wealth-launches-ai-enabled-commentary-tool-at-morgan-stanley","Aladdin Wealth™ Launches AI-Enabled Commentary Tool for Wealth Advisors; Morgan Stanley's Portfolio Risk Platform First to Implement","2025-10-02",{"url":221,"title":222,"publisher":223,"date":219},"https://www.investmentnews.com/alternatives/blackrock-debuts-ai-powered-commentary-tool-for-advisors-lands-morgan-stanley-as-first-client/262370","BlackRock debuts AI-powered commentary tool for advisors, lands Morgan Stanley as first client","InvestmentNews",{"level":225,"checkedAt":189},"source-verified","C","morgan-stanley-aladdin-auto-commentary","morgan-stanley",{"title":230,"useCases":231,"organization":233,"vendors":236,"summary":239,"stage":240,"year":210,"channels":241,"languages":243,"metrics":244,"outcomeDisclosed":201,"sources":245,"verification":251,"grade":226,"id":252,"organizationSlug":253},"Quilter: Microsoft 365 Copilot for meeting notes and investment writing",[232,193],"client-meeting-notes-and-crm-update",{"name":234,"anonymized":201,"country":235,"region":153,"industry":17},"Quilter","GB",[237],{"name":238,"role":207},"Microsoft","Quilter, a UK wealth manager, rolled out Microsoft 365 Copilot and names meetings and transcriptions as its biggest use case. Microsoft reports that Quilter estimates Copilot will save more than 13,000 hours per month of post call admin time; an investment manager at Quilter Cheviot builds that estimate from an assumed 45 minutes saved per client meeting across 174 investment managers doing about 100 meetings each. Both figures are projections, not measured savings, so neither is recorded as a metric. Quilter also tested turning a portfolio manager interview transcript into an investment commentary: about 15 minutes of prompting and half an hour of editing instead of a few days, which it describes as a one off test.","production",[242,28],"microsoft-teams",[213],[],[246],{"url":247,"title":248,"publisher":249,"archivedUrl":250},"https://www.microsoft.com/en/customers/story/23237-quilter-microsoft-365-copilot","Quilter achieves fastest-ever tech ROI with Microsoft 365 Copilot","Microsoft Customer Stories","https://web.archive.org/web/20250517152254/https://www.microsoft.com/en/customers/story/23237-quilter-microsoft-365-copilot",{"level":225,"checkedAt":189},"quilter-copilot-meeting-notes",null,0,[],{"low":257,"high":258},800000,4800000,[260,287,303,319],{"slug":186,"title":261,"shortTitle":262,"definition":263,"status":9,"industries":264,"functions":266,"patterns":269,"audience":271,"autonomy":31,"adoptionStage":32,"segment":272,"evidenceCount":58,"publicEvidenceCount":58,"organizations":273,"bestGrade":277,"headline":278,"lastVerified":189,"indexable":286},"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.",[17,265,18],"capital-markets",[20,267,268],"sales","knowledge-management",[25,270,24,26],"rag-knowledge-assistant","employee-facing","front-office",[274,275,200,276],"Citi","Deutsche Bank","UBS","B",{"kpi":279,"label":280,"unit":281,"n":254,"nUpTo":282,"kind":283,"value":72,"qualifier":284,"claimant":285,"organization":275,"vendorReported":201},"time-saved-per-task","Time saved per task","minutes",1,"reported","up-to","organization",true,{"slug":187,"title":288,"shortTitle":289,"definition":290,"status":9,"industries":291,"functions":292,"patterns":294,"audience":30,"autonomy":31,"adoptionStage":298,"segment":33,"evidenceCount":58,"publicEvidenceCount":299,"organizations":300,"bestGrade":277,"headline":253,"lastVerified":189,"indexable":286},"AI portfolio drift monitoring and rebalancing proposals","Drift and rebalancing","Continuous monitoring of every client portfolio against its mandate or model, which detects drift beyond agreed bands and prepares a tax aware, low turnover rebalancing proposal with its rationale for an advisor or portfolio manager to approve before any trade is placed.",[17,18],[22,293,20],"risk-management",[295,296,297,24],"anomaly-detection","agentic-workflow","prediction-and-scoring","emerging",3,[200,301,302],"SimCorp","Vanguard",{"slug":188,"title":304,"shortTitle":305,"definition":306,"status":9,"industries":307,"functions":308,"patterns":309,"audience":271,"autonomy":311,"adoptionStage":312,"segment":272,"evidenceCount":313,"publicEvidenceCount":313,"organizations":314,"bestGrade":277,"headline":253,"lastVerified":318,"indexable":286},"AI knowledge assistant for wealth advisors and relationship managers","Advisor knowledge assistant","A conversational assistant that answers a wealth advisor's or relationship manager's questions in seconds from the firm's own research, house view, product documentation and policies, with every answer linked to the source document so the advisor can check it before using it with a client.",[17,18],[268,267,21],[270,310],"conversational-agent","assist","mainstream",6,[315,274,316,200,276,317],"Bank of America","JPMorgan Chase","Yes Bank","2026-09-26",{"slug":320,"title":321,"shortTitle":322,"definition":323,"status":9,"industries":324,"functions":329,"patterns":333,"audience":271,"autonomy":31,"adoptionStage":32,"segment":30,"evidenceCount":334,"publicEvidenceCount":334,"organizations":335,"bestGrade":277,"headline":340,"lastVerified":318,"indexable":286},"outbound-notice-drafting","AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[325,18,326,327,328,17],"cross-industry","insurance","government","healthcare",[22,21,330,331,332],"collections-and-recovery","regulatory-compliance","claims",[24,270,26],5,[336,337,338,339],"Acentra Health","Hiscox","Health Resources and Services Administration","SS&C Technologies",{"kpi":44,"label":341,"unit":342,"n":282,"nUpTo":254,"kind":283,"value":343,"qualifier":344,"claimant":345,"organization":346,"vendorReported":286},"Cycle time reduction","percent",25,"exact","vendor","SS&C GIDS and RS",{"indexable":286,"reasons":348},[],[350,355,360,367,374,379,386,391,398,404,411,417,424,431,437,442,449,455,461,467,473,479,484,488,493,500,507,512,517,524,530,536,542,547],{"id":142,"label":351,"issuer":158,"region":153,"url":352,"description":353,"useCases":354,"indexable":286},"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":143,"label":356,"issuer":158,"region":153,"url":357,"description":358,"useCases":359,"indexable":286},"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":147,"label":361,"issuer":362,"region":363,"url":364,"description":365,"useCases":366,"indexable":286},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":368,"label":369,"issuer":370,"region":203,"url":371,"description":372,"useCases":373,"indexable":286},"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":145,"label":375,"issuer":158,"region":153,"url":376,"description":377,"useCases":378,"indexable":286},"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":380,"label":381,"issuer":382,"region":153,"url":383,"description":384,"useCases":385,"indexable":286},"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":144,"label":387,"issuer":163,"region":153,"url":388,"description":389,"useCases":390,"indexable":286},"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":146,"label":392,"issuer":393,"region":394,"url":395,"description":396,"useCases":397,"indexable":286},"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":399,"label":400,"issuer":401,"region":394,"url":402,"description":403,"useCases":343,"indexable":286},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":405,"label":406,"issuer":407,"region":363,"url":408,"description":409,"useCases":410,"indexable":286},"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":412,"label":413,"issuer":414,"region":203,"url":415,"description":416,"useCases":410,"indexable":286},"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":418,"label":419,"issuer":420,"region":153,"url":421,"description":422,"useCases":423,"indexable":286},"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":425,"label":426,"issuer":427,"region":363,"url":428,"description":429,"useCases":430,"indexable":286},"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":432,"label":433,"issuer":158,"region":153,"url":434,"description":435,"useCases":436,"indexable":286},"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":438,"label":439,"issuer":158,"region":153,"url":440,"description":441,"useCases":436,"indexable":286},"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":443,"label":444,"issuer":445,"region":203,"url":446,"description":447,"useCases":448,"indexable":286},"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":450,"label":451,"issuer":158,"region":153,"url":452,"description":453,"useCases":454,"indexable":286},"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":456,"label":457,"issuer":458,"region":203,"url":459,"description":460,"useCases":454,"indexable":286},"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":462,"label":463,"issuer":464,"region":363,"url":465,"description":466,"useCases":454,"indexable":286},"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":468,"label":469,"issuer":158,"region":153,"url":470,"description":471,"useCases":472,"indexable":286},"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":474,"label":475,"issuer":476,"region":203,"url":477,"description":478,"useCases":472,"indexable":286},"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":480,"label":481,"issuer":393,"region":394,"url":482,"description":483,"useCases":64,"indexable":286},"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":148,"label":485,"issuer":158,"region":153,"url":486,"description":487,"useCases":64,"indexable":286},"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":158,"region":153,"url":491,"description":492,"useCases":64,"indexable":286},"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":153,"url":497,"description":498,"useCases":499,"indexable":286},"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":501,"label":502,"issuer":503,"region":203,"url":504,"description":505,"useCases":506,"indexable":286},"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.",8,{"id":508,"label":509,"issuer":158,"region":153,"url":510,"description":511,"useCases":506,"indexable":286},"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":513,"label":514,"issuer":158,"region":153,"url":515,"description":516,"useCases":313,"indexable":286},"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":518,"label":519,"issuer":520,"region":521,"url":522,"description":523,"useCases":334,"indexable":286},"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":525,"label":526,"issuer":527,"region":153,"url":528,"description":529,"useCases":58,"indexable":286},"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":531,"label":532,"issuer":533,"region":153,"url":534,"description":535,"useCases":58,"indexable":286},"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":537,"label":538,"issuer":539,"region":394,"url":540,"description":541,"useCases":299,"indexable":286},"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":543,"label":544,"issuer":158,"region":153,"url":545,"description":546,"useCases":299,"indexable":286},"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":203,"url":551,"description":552,"useCases":299,"indexable":286},"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.",1790598302284]