[{"data":1,"prerenderedAt":626},["ShallowReactive",2],{"uc-investment-research-summarization":3,"uc-regulations":421},{"useCase":4,"evidence":201,"blitsAiDeployments":320,"benchmarks":321,"indicative":333,"related":336,"indexability":419,"includeUnpublished":207},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":24,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":51,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":141,"blitsAi":178,"faq":180,"related":190,"datePublished":196,"dateModified":196,"lastVerified":196,"changelog":197,"slug":200},"AI summaries of investment research and the house view","Research summaries","AI investment research summaries for advisors","AI assistants turn research and the house view into cited briefings. Deutsche Bank analysts report saving up to two hours per research report with DB Lumina.","published","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.",[12,13,14,15],"research summarization assistant","house view briefing generator","research question answering for sales and advisors","market commentary summarizer",[17,18,19],"wealth-and-asset-management","capital-markets","banking",[21,22,23],"analytics-and-reporting","sales","knowledge-management",[25,26,27,28],"summarization","rag-knowledge-assistant","content-generation","translation",[30,31,32],"internal-tools","email","microsoft-teams","employee-facing","copilot","early-adopters","front-office","Morgan Stanley alone publishes more than 70,000 proprietary research reports a year. No advisor or\nsalesperson can read that, so client conversations lean on the few notes someone happened to see,\nand the firm's own view reaches clients unevenly. Analysts, in turn, spend much of their time on the\nmechanical parts of writing: sifting through financial statements, regulatory filings and industry\nreports, and summarizing earnings releases and investor transcripts.\n\nRewriting research for segments and languages multiplies the work, and every rewrite is a chance\nfor a number to drift from the approved report. The job is to make the research usable at the\nmoment of need without changing what it says.",[],"1. **Ingest approved research.** Published reports, the house view, earnings summaries and market\n   notes are indexed with their publication date, author and distribution rules.\n2. **Answer and summarize on demand.** An advisor or salesperson asks a question or requests a\n   briefing; the assistant retrieves the relevant passages and writes a short answer with links to\n   each source report.\n3. **Produce standard briefings.** A morning note, a sector summary or a \"what changed\" digest is\n   generated from a template, with figures and price targets copied from the source rather than\n   generated.\n4. **Adapt for audience and language.** Approved summaries are rewritten for a client segment or\n   translated, and the adapted version is checked against the source before use.\n5. **Review before anything branded goes out.** Research or compliance signs off on any client\n   facing summary; internal answers carry citations so the reader can verify them.",[41,42,43,44],"employee-productivity","speed","customer-experience","compliance",[46,47,48,49,50],"time-saved-per-task","users-served","response-time-reduction","employee-adoption","accuracy",{"referenceOrg":52,"inputs":53,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A research and advisory team of 100 analysts and strategists",[54,60,66,73],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"analysts","Analysts and strategists producing research",100,"people","The reference team.",{"key":61,"label":62,"low":63,"high":57,"unit":64,"note":65},"documentsPerYear","Notes and reports per person per year",50,"documents per person per year","Editorial assumption, replace with your own publication volumes.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"hoursSaved","Hours saved per document",0.5,0.75,"hours per document","In line with the Deutsche Bank figure of 30 to 45 minutes saved on earnings note templates. The reported ceiling of up to two hours applies to full research reports and roadshow updates and is not assumed here.",{"key":74,"label":75,"low":57,"high":76,"unit":77,"note":78},"hourlyCost","Fully loaded analyst cost per hour",200,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","analysts * documentsPerYear * hoursSaved * hourlyCost","USD","per year","Value of analyst time released from summarizing and drafting","Covers production time only. It leaves out the value on the distribution side (faster answers to client questions), the cost of running the tool and the review effort for client facing output.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Retrieval and summarization are straightforward. The effort is in distribution rights (which research may be shown to whom), keeping numbers exact, and a review workflow for anything that reaches clients under the firm's name.",[89,90,91,92],"Research archive with publication dates, authors, ratings and distribution permissions","Current house view and CIO publications","Market data feeds where briefings reference prices or moves","Style guides and disclaimers per client segment and market",[94,95,96,97],"Research publishing platform and archive","Market data provider","Document management and translation workflow","Advisor or sales desktop, email and collaboration tools",{"steps":99,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[100,103,106,109,112],{"title":101,"detail":102},"Start internal, with citations","Launch as an internal question answering tool over published research, where every answer links to the source report. Internal use builds trust and shows which questions matter.",{"title":104,"detail":105},"Copy numbers, never generate them","Extract figures, ratings and price targets from the source with structured extraction and insert them into the text, then check the final output against the source automatically.",{"title":107,"detail":108},"Respect distribution rules","Filter research by audience, market and embargo before retrieval so restricted or institutional only content never appears in an answer for the wrong reader.",{"title":110,"detail":111},"Add templated briefings","Once answers are reliable, generate recurring digests (morning note, weekly house view changes) from templates owned by the research team.",{"title":113,"detail":114},"Put client facing output through review","Any summary sent to clients goes through the same approval as other research or marketing communications, with the AI draft and the source retained.",[116,117,118,119,120],"Answers only from published, approved research with citations and dates","Numbers, ratings and price targets copied from source and verified, never generated","Distribution and embargo rules applied before retrieval","Human sign off before any branded or client facing summary is sent","Refusal to give personalized recommendations; the assistant summarizes the firm's view","Analysts approve summaries of their own work before external use; research management or compliance signs off on client facing templates; readers of internal answers verify through the cited source.",[123,124,125,126,127],"Share of answers with a valid citation and a matching figure check","Time from report publication to advisor ready summary","Weekly active users by desk","Error rate on a monthly sample checked by analysts","Client facing summaries rejected at review, with reasons",[129,132,135,138],{"title":130,"detail":131},"Drifting numbers","A price target or percentage in the summary differs from the report. Copy numbers from structured extraction and check them automatically.",{"title":133,"detail":134},"Stale view presented as current","An older note outranks the latest update. Weight recency, show dates and retire superseded views.",{"title":136,"detail":137},"Distribution breach","Institutional or embargoed research reaches a retail audience. Filter by entitlement before retrieval.",{"title":139,"detail":140},"Summaries that read as advice","A generic summary is sent to a client as if it were personal advice. Keep client facing use behind review and templates.",{"euAiAct":142,"regulations":145,"guidance":153,"controls":171,"incidents":177},{"tier":143,"basis":144},"context-dependent","Summarizing research for staff is not an Annex III use and is not a practice prohibited by Article 5, so the tier turns on the firm's role under Article 50. It is minimal for a purchased internal tool with no client or public facing exposure. Article 50 transparency applies when the firm builds the generating system itself, which brings the Article 50(2) duty to mark synthetic text in a machine readable format; when the assistant is offered to clients as a chatbot, which brings the Article 50(1) duty to tell them they are interacting with AI; or when AI generated text is published to inform the public on matters of public interest, which brings the Article 50(4) disclosure duty unless the text has gone through human review or editorial control and a person holds editorial responsibility for it.",[146,147,148,149,150,151,152],"eu-ai-act","gdpr","dora","mas-ai-risk-management","iso-42001","mifid-ii","eu-mar",[154,160,165],{"title":155,"issuer":156,"region":157,"url":158,"note":159},"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","Names hallucination and overreliance as risks and expects accuracy controls and records when AI supports investment services.",{"title":161,"issuer":162,"region":157,"url":163,"note":164},"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":166,"issuer":167,"region":168,"url":169,"note":170},"Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT)","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/publications/monographs-or-information-paper/2018/feat","Principles for the responsible use of AI and data analytics by Singapore financial firms, including accountability for AI driven outputs and transparency to customers about AI use.",[172,173,174,175,176],"Source traceability for every summary, retained with the output","Automated figure check against the source before release","Review and approval workflow for client facing summaries","Entitlement and embargo filtering tested on every change","Inventory entry with owners in research and distribution",[],{"howToBuild":179},"On Blits.ai the research archive sits in a **knowledge base** with **hybrid retrieval**, loaded\nfrom PDF, Word and PowerPoint reports or crawled from the research portal, with version control in\nthe central document library. An **AI agent** answers questions from the retrieved research, using\n**structured output** to return the answer, its figures and the source report in separate fields,\nso a **custom function** can check each figure against the source before release. An **agentic\nworkflow** triggered on a schedule produces the recurring briefings.\n\nSending a client facing draft is an agentic action that waits for **human in the loop** approval.\n**Machine translation** adapts approved summaries per market, and the translated version goes\nthrough the same figure check and review.\n**Guardrails** block personalized recommendations, **test suites** check answers and figures on\nevery change, and the platform is **model agnostic**, so the firm can switch models per agent and\nuse regional model routing to keep data in the EU or UAE region.",[181,184,187],{"question":182,"answer":183},"Will the AI invent numbers or price targets?","It can, if you let it write numbers freely. The safe design copies every figure from the source report, checks the output against it, and keeps citations visible. Deutsche Bank's DB Lumina, for example, grounds answers in internal research with inline citations and source viewers.",{"question":185,"answer":186},"How much time does it save?","Deutsche Bank analysts report saving 30 to 45 minutes on earnings note templates and up to two hours on research reports and roadshow updates. On the distribution side, Morgan Stanley's global director of research told CNBC that a salesperson needs one tenth of the time to answer the average client inquiry with AskResearchGPT.",{"question":188,"answer":189},"Can summaries go straight to clients?","Only through the same review as other research and marketing communications. Internal use with citations is the low risk starting point; client facing output needs templates and sign off.",[191,192,193,194,195],"wealth-advisor-knowledge-assistant","portfolio-reporting-and-commentary","next-best-action-for-advisors","marketing-content-compliance-copilot","client-briefing-and-call-report-copilot","2026-09-27",[198],{"date":196,"note":199},"First published","investment-research-summarization",[202,232,261,299],{"title":203,"useCases":204,"organization":205,"vendors":210,"summary":213,"stage":214,"year":215,"channels":216,"languages":217,"metrics":219,"outcomeDisclosed":207,"sources":220,"verification":226,"grade":229,"id":230,"organizationSlug":231},"Citi Wealth: AskWealth assistant and Advisor Insights",[191,200,193],{"name":206,"anonymized":207,"country":208,"region":209,"industry":17},"Citi",false,"US","global",[211],{"name":206,"role":212},"in-house","Citi Wealth launched two AI tools built by its Data, Analytics and Innovation team. AskWealth is a generative AI assistant that gives service teams, advisors and managers answers across the wealth business, so that advisors can reach market insights and research when clients ask questions; after a launch in Asia it became available to Citi Wealth colleagues worldwide. Advisor Insights is a dashboard of timely messages about market moves, portfolios and events, including Chief Investment Office insights, piloted with Citigold and Citi Private Client advisors in North America with a wider rollout planned for Q4 2025 and Q1 2026. Citi says the tools will save hours of time but published no figures.","production",2025,[30],[218],"en",[],[221],{"url":222,"title":223,"publisher":224,"date":225},"https://www.citigroup.com/global/news/press-release/2025/citi-wealth-launches-advisor-insights-askwealth","Citi Wealth Launches \"Advisor Insights\" Pilot and \"AskWealth,\" AI-Driven \"Gamechangers\" for Client Communications","Citigroup","2025-08-25",{"level":227,"checkedAt":228},"source-verified","2026-09-26","B","citi-wealth-askwealth-and-advisor-insights","citi",{"title":233,"useCases":234,"organization":235,"vendors":238,"summary":242,"stage":214,"year":243,"channels":244,"languages":245,"metrics":246,"outcomeDisclosed":247,"sources":248,"verification":258,"grade":229,"id":259,"organizationSlug":260},"Morgan Stanley: AskResearchGPT for institutional sales, trading and banking staff",[200],{"name":236,"anonymized":207,"country":208,"region":237,"industry":18},"Morgan Stanley","north-america",[239],{"name":240,"role":241},"OpenAI","model-provider","Morgan Stanley Research launched AskResearchGPT, a GPT-4 based assistant that lets investment banking, sales and trading and research staff search and summarize the firm's research (more than 70,000 proprietary reports a year), with hyperlinks to the source reports and a one click transfer of findings into an email draft that staff edit before sending to clients. It is available in the browser, Microsoft Teams and Outlook. Morgan Stanley's global director of research told CNBC that a salesperson needs one tenth of the time to answer the average client inquiry with the tool, and the bank said staff ask three times as many questions as with the traditional AI tool it had used since 2017.",2024,[30,32,31],[218],[],true,[249,254],{"url":250,"title":251,"publisher":236,"date":252,"archivedUrl":253},"https://www.morganstanley.com/press-releases/morgan-stanley-research-announces-askresearchgpt","Morgan Stanley Research Announces AskResearchGPT","2024-10-23","https://web.archive.org/web/2026/https://www.morganstanley.com/press-releases/morgan-stanley-research-announces-askresearchgpt",{"url":255,"title":256,"publisher":257,"date":252},"https://www.cnbc.com/2024/10/23/morgan-stanley-rolls-out-openai-powered-chatbot-for-wall-street-division.html","AI on the trading floor: Morgan Stanley expands OpenAI-powered chatbot tools to Wall Street division","CNBC",{"level":227,"checkedAt":228},"morgan-stanley-askresearchgpt","morgan-stanley",{"title":262,"useCases":263,"organization":264,"vendors":267,"summary":272,"stage":214,"year":243,"channels":273,"languages":274,"metrics":275,"outcomeDisclosed":247,"sources":290,"verification":295,"grade":296,"id":297,"organizationSlug":298},"Deutsche Bank: DB Lumina research agent for analysts",[200],{"name":265,"anonymized":207,"country":266,"region":157,"industry":18},"Deutsche Bank","DE",[268,271],{"name":269,"role":270},"Google Cloud","platform",{"name":265,"role":212},"Deutsche Bank Research built DB Lumina, a research agent on Google Cloud and Gemini models that helps analysts ingest documents, summarize earnings releases and investor transcripts, answer questions with inline citations and edit notes, with guardrails, access control and audit logging. It went live in September 2024 and, when the bank described it in September 2025, was used by around 5,000 people across Deutsche Bank Research and divisions such as Investment Bank Origination and Advisory and Fixed Income and Currencies. Analysts report saving 30 to 45 minutes on earnings note templates and up to two hours on research reports and roadshow updates. The bank evaluates it with stable test sets, automated metrics and human review.",[30],[218],[276,284],{"kpi":47,"value":277,"unit":278,"qualifier":279,"period":280,"claimant":281,"quote":282,"sourceUrl":283},5000,"count","approximately","users at publication in September 2025","organization","Currently, DB Lumina is already in the hands of around 5,000 users across Deutsche Bank Research, specifically in divisions like Investment Bank Origination & Advisory and Fixed Income & Currencies.","https://cloud.google.com/blog/topics/financial-services/deutsche-bank-delivers-ai-powered-financial-research-with-db-lumina",{"kpi":46,"value":285,"unit":286,"qualifier":287,"period":288,"claimant":281,"quote":289,"sourceUrl":283},120,"minutes","up-to","per research report or roadshow update","Time savings: Analysts reported significant time savings, saving 30 to 45 minutes on preparing earnings note templates and up to two hours when writing research reports and roadshow updates.",[291],{"url":283,"title":292,"publisher":293,"date":294},"Deutsche Bank delivers AI-powered financial research with DB Lumina","Google Cloud Blog","2025-09-24",{"level":227,"checkedAt":196},"C","deutsche-bank-db-lumina-research","deutsche-bank",{"title":300,"useCases":301,"organization":302,"vendors":305,"summary":308,"stage":214,"year":243,"channels":309,"languages":310,"metrics":311,"outcomeDisclosed":207,"sources":312,"verification":317,"grade":296,"id":318,"organizationSlug":319},"UBS: UBS Red smart assistants for client advisors",[191,200],{"name":303,"anonymized":207,"country":304,"region":157,"industry":17},"UBS","CH",[306],{"name":307,"role":270},"Microsoft","UBS built two domain specific assistants, together called UBS Red, on Azure AI Search and Azure OpenAI Service to give client advisors fast, multilingual access to the bank's investment advice and product content during client work. UBS digitized about 60,000 investment advice and product documents into a queryable knowledge base, which it says saves considerable time in meeting preparation and research. Within 10 months the wider Azure OpenAI footprint reached key wealth, banking and operations divisions in the Switzerland, Hong Kong and Singapore booking centres. No usage or time saving figure specific to UBS Red is published.",[30],[],[],[313],{"url":314,"title":315,"publisher":316},"https://www.microsoft.com/en/customers/story/19796-ubs-azure","UBS and Microsoft unite: Co-creating the future of banking with Azure AI","Microsoft Customer Stories",{"level":227,"checkedAt":228},"ubs-red-client-advisor-assistants",null,0,[322,328],{"kpi":47,"label":323,"unit":278,"aggregate":207,"higherIsBetter":247,"n":324,"nUpTo":320,"median":277,"min":277,"max":277,"byClaimant":325,"vendorOnly":207,"points":326},"Users served",1,{"organization":324,"vendor":320,"regulator":320,"independent":320},[327],{"evidenceId":297,"organization":265,"value":277,"qualifier":279,"claimant":281,"grade":296,"pooled":247},{"kpi":46,"label":329,"unit":286,"aggregate":247,"higherIsBetter":247,"n":320,"nUpTo":324,"median":319,"min":319,"max":319,"byClaimant":330,"vendorOnly":207,"points":331},"Time saved per task",{"organization":320,"vendor":320,"regulator":320,"independent":320},[332],{"evidenceId":297,"organization":265,"value":285,"qualifier":287,"claimant":281,"grade":296,"pooled":207},{"low":334,"high":335},250000,1500000,[337,353,367,386,407],{"slug":191,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":342,"patterns":344,"audience":33,"autonomy":346,"adoptionStage":347,"segment":36,"evidenceCount":348,"publicEvidenceCount":348,"organizations":349,"bestGrade":229,"headline":319,"lastVerified":228,"indexable":247},"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.",[17,19],[23,22,343],"customer-service",[26,345],"conversational-agent","assist","mainstream",6,[350,206,351,236,303,352],"Bank of America","JPMorgan Chase","Yes Bank",{"slug":192,"title":354,"shortTitle":355,"definition":356,"status":9,"industries":357,"functions":358,"patterns":360,"audience":361,"autonomy":34,"adoptionStage":35,"segment":362,"evidenceCount":363,"publicEvidenceCount":364,"organizations":365,"bestGrade":296,"headline":319,"lastVerified":196,"indexable":247},"AI generated client portfolio reports and commentary","Portfolio commentary","AI that drafts each client's periodic portfolio commentary and report narrative (performance, attribution, what drove returns, positioning and outlook) in plain language and in the client's language, where every figure comes from the portfolio system of record and a reviewer approves the text before delivery.",[17,19],[21,343,359],"operations",[27,25,28],"back-office","middle-office",3,2,[236,366],"Quilter",{"slug":193,"title":368,"shortTitle":369,"definition":370,"status":9,"industries":371,"functions":372,"patterns":374,"audience":33,"autonomy":346,"adoptionStage":35,"segment":36,"evidenceCount":377,"publicEvidenceCount":377,"organizations":378,"bestGrade":229,"headline":380,"lastVerified":196,"indexable":247},"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.",[17,19],[22,373,21],"marketing",[375,376,27],"recommendation-and-personalization","prediction-and-scoring",5,[379,206,351,236,303],"CIMB Niaga",{"kpi":49,"label":381,"unit":382,"n":324,"nUpTo":320,"kind":383,"value":384,"qualifier":385,"claimant":281,"organization":303,"vendorReported":207},"Employee adoption","percent","reported",80,"exact",{"slug":194,"title":387,"shortTitle":388,"definition":389,"status":9,"industries":390,"functions":395,"patterns":398,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":377,"publicEvidenceCount":363,"organizations":400,"bestGrade":229,"headline":403,"lastVerified":196,"indexable":247},"AI copilot for marketing content with compliance pre review","Marketing content and compliance","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.",[391,19,392,393,17,394],"cross-industry","insurance","payments","pharma-and-life-sciences",[373,396,397],"regulatory-compliance","legal",[27,26,399,28],"classification-and-routing",[401,351,402],"Ally Financial","Klarna",{"kpi":404,"label":405,"unit":382,"n":324,"nUpTo":320,"kind":383,"value":406,"qualifier":385,"claimant":281,"organization":401,"vendorReported":207},"productivity-gain","Productivity gain",34,{"slug":195,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":412,"patterns":413,"audience":33,"autonomy":34,"adoptionStage":35,"segment":415,"evidenceCount":363,"publicEvidenceCount":363,"organizations":416,"bestGrade":229,"headline":319,"lastVerified":196,"indexable":247},"AI copilot for corporate client briefings and call reports","Client briefing and call reports","An AI copilot for relationship managers, mainly in corporate and commercial banking, whose main job is preparation: before a client meeting it assembles a briefing pack from filings, news, internal notes, product holdings and upcoming maturities, and afterwards it turns the banker's notes into a structured call report and CRM update. Unlike a meeting notetaker, which centres on capturing the conversation, it centres on the credit and cross sell context around the meeting; wealth advisor tools that also prepare meetings overlap with it. The banker reviews every output.",[19,17,18],[22,23],[26,25,27,414],"agentic-workflow","specialized-businesses",[350,417,418],"Scotiabank","Standard Chartered",{"indexable":247,"reasons":420},[],[422,427,432,438,445,450,457,464,469,476,483,489,496,503,509,514,521,527,533,539,545,551,557,561,566,573,580,585,590,597,604,610,616,620],{"id":146,"label":423,"issuer":162,"region":157,"url":424,"description":425,"useCases":426,"indexable":247},"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":147,"label":428,"issuer":162,"region":157,"url":429,"description":430,"useCases":431,"indexable":247},"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":150,"label":433,"issuer":434,"region":209,"url":435,"description":436,"useCases":437,"indexable":247},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":439,"label":440,"issuer":441,"region":237,"url":442,"description":443,"useCases":444,"indexable":247},"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":148,"label":446,"issuer":162,"region":157,"url":447,"description":448,"useCases":449,"indexable":247},"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":451,"label":452,"issuer":453,"region":157,"url":454,"description":455,"useCases":456,"indexable":247},"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":458,"label":459,"issuer":460,"region":157,"url":461,"description":462,"useCases":463,"indexable":247},"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":149,"label":465,"issuer":167,"region":168,"url":466,"description":467,"useCases":468,"indexable":247},"MAS AI risk management guidelines","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":470,"label":471,"issuer":472,"region":168,"url":473,"description":474,"useCases":475,"indexable":247},"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":477,"label":478,"issuer":479,"region":209,"url":480,"description":481,"useCases":482,"indexable":247},"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":484,"label":485,"issuer":486,"region":237,"url":487,"description":488,"useCases":482,"indexable":247},"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":490,"label":491,"issuer":492,"region":157,"url":493,"description":494,"useCases":495,"indexable":247},"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":497,"label":498,"issuer":499,"region":209,"url":500,"description":501,"useCases":502,"indexable":247},"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":504,"label":505,"issuer":162,"region":157,"url":506,"description":507,"useCases":508,"indexable":247},"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":510,"label":511,"issuer":162,"region":157,"url":512,"description":513,"useCases":508,"indexable":247},"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":515,"label":516,"issuer":517,"region":237,"url":518,"description":519,"useCases":520,"indexable":247},"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":522,"label":523,"issuer":162,"region":157,"url":524,"description":525,"useCases":526,"indexable":247},"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":528,"label":529,"issuer":530,"region":237,"url":531,"description":532,"useCases":526,"indexable":247},"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":534,"label":535,"issuer":536,"region":209,"url":537,"description":538,"useCases":526,"indexable":247},"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":540,"label":541,"issuer":162,"region":157,"url":542,"description":543,"useCases":544,"indexable":247},"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":546,"label":547,"issuer":548,"region":237,"url":549,"description":550,"useCases":544,"indexable":247},"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":552,"label":553,"issuer":167,"region":168,"url":554,"description":555,"useCases":556,"indexable":247},"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":151,"label":558,"issuer":162,"region":157,"url":559,"description":560,"useCases":556,"indexable":247},"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":562,"label":563,"issuer":162,"region":157,"url":564,"description":565,"useCases":556,"indexable":247},"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":567,"label":568,"issuer":569,"region":157,"url":570,"description":571,"useCases":572,"indexable":247},"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":574,"label":575,"issuer":576,"region":237,"url":577,"description":578,"useCases":579,"indexable":247},"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":581,"label":582,"issuer":162,"region":157,"url":583,"description":584,"useCases":579,"indexable":247},"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":586,"label":587,"issuer":162,"region":157,"url":588,"description":589,"useCases":348,"indexable":247},"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":591,"label":592,"issuer":593,"region":594,"url":595,"description":596,"useCases":377,"indexable":247},"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":598,"label":599,"issuer":600,"region":157,"url":601,"description":602,"useCases":603,"indexable":247},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":605,"label":606,"issuer":607,"region":157,"url":608,"description":609,"useCases":603,"indexable":247},"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":611,"label":612,"issuer":613,"region":168,"url":614,"description":615,"useCases":363,"indexable":247},"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":152,"label":617,"issuer":162,"region":157,"url":618,"description":619,"useCases":363,"indexable":247},"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":621,"label":622,"issuer":623,"region":237,"url":624,"description":625,"useCases":363,"indexable":247},"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.",1790598306384]