[{"data":1,"prerenderedAt":606},["ShallowReactive",2],{"uc-client-briefing-and-call-report-copilot":3,"uc-regulations":402},{"useCase":4,"evidence":196,"blitsAiDeployments":276,"benchmarks":277,"indicative":278,"related":281,"indexability":400,"includeUnpublished":202},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":23,"channels":28,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":43,"indicativeValue":49,"macroEstimates":83,"feasibility":84,"implementation":98,"risk":141,"blitsAi":172,"faq":174,"related":184,"datePublished":191,"dateModified":191,"lastVerified":191,"changelog":192,"slug":195},"AI copilot for corporate client briefings and call reports","Client briefing and call reports","Relationship manager AI copilot for call reports","AI builds client briefing packs and drafts call reports that bankers approve. Evidence: Bank of America's meeting prep tool, Standard Chartered's AI client insights.","published","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.",[12,13,14,15],"relationship manager copilot","meeting preparation assistant","call report automation","banker briefing pack generator",[17,18,19],"banking","wealth-and-asset-management","capital-markets",[21,22],"sales","knowledge-management",[24,25,26,27],"rag-knowledge-assistant","summarization","content-generation","agentic-workflow",[29,30,31],"internal-tools","microsoft-teams","email","employee-facing","copilot","early-adopters","specialized-businesses","Preparing for a corporate client meeting means assembling information from many places. The\nrelationship manager pulls the latest financials and filings, scans news about the client and its\nsector, checks product holdings, limits and upcoming maturities in several systems, and looks for\nshare of wallet gaps. After the meeting the notes have to become a call report and a CRM update,\nwhich can happen late, briefly or not at all.\n\nWhen this work is manual, preparation depends on how much time each banker has, CRM data stays\nthin, and cross sell opportunities are missed because the information sits in different systems.\nA copilot takes over the gathering and first drafting across those sources, cites where every fact\ncame from, and leaves the judgement and the client conversation with the banker.",[],"1. **Trigger from the calendar or CRM.** A scheduled client meeting, or a banker's request, starts\n   the preparation a day or two ahead.\n2. **Gather from approved sources.** The copilot retrieves internal notes, product holdings,\n   exposures and maturities from the CRM and core systems, and public filings and news from\n   licensed or approved external sources.\n3. **Draft the briefing pack.** It writes a short pack: client snapshot, recent events, open\n   items, maturities and renewals, possible needs, and a suggested agenda, with a source link on\n   every fact.\n4. **Capture the meeting.** With the client's consent, or from the banker's own notes or\n   dictation, it produces a structured summary of decisions and next steps.\n5. **Draft the call report and CRM update.** It fills the call report template and proposes CRM\n   updates and follow up tasks; the banker edits and approves before anything is saved.",[40,41,42],"employee-productivity","revenue-growth","customer-experience",[44,45,46,47,48],"time-saved-per-task","employee-adoption","users-served","hours-saved","productivity-gain",{"referenceOrg":50,"inputs":51,"formula":78,"currency":79,"period":80,"resultLabel":81,"caveat":82},"A commercial bank with 300 relationship managers",[52,57,64,71],{"key":53,"label":54,"low":55,"high":55,"unit":53,"note":56},"bankers","Relationship managers using the copilot",300,"The reference bank.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"meetingsPerBanker","Client meetings per banker per year",150,250,"meetings per banker per year","Editorial assumption, replace with your own CRM activity data.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"minutesSaved","Preparation and call report time saved per meeting",30,60,"minutes per meeting","Editorial assumption. Deliberately far below the up to four hours per meeting that Bank of America says its meeting tool can save, because that figure is a stated potential, not a measured result.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"hourlyCost","Loaded cost of a relationship manager hour",80,120,"USD per hour","Editorial assumption, replace with your own loaded cost.","bankers * meetingsPerBanker * minutesSaved / 60 * hourlyCost","USD","per year","Relationship manager time released, valued at loaded cost","Values released time, not revenue. It leaves out the cost of the copilot and its data feeds, the revenue effect of better prepared meetings, and the gain from more complete CRM data, which is often the larger benefit but hard to measure.",[],{"complexity":85,"complexityNote":86,"dataPrerequisites":87,"integrations":92},"medium","The drafting is the easy part. The work is in reaching CRM, exposure and product data through APIs, licensing news and filings content for AI use, and respecting information barriers between client teams.",[88,89,90,91],"CRM with client hierarchy, contacts, notes and pipeline reachable through an API","Product holdings, limits, exposures and maturities per client group","Licensed or approved news and filings sources","A call report template and the bank's rules on what must be recorded",[93,94,95,96,97],"CRM (read and write, with banker approval)","Core banking, lending and treasury systems for holdings and maturities","News, filings and market data providers","Calendar and email or collaboration suite","Meeting transcription, where client consent is obtained",{"steps":99,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[100,103,106,109,112],{"title":101,"detail":102},"Start with one segment and one meeting type","Pick annual review meetings in one commercial segment, where the pack content is predictable, and agree with a group of bankers what a good briefing looks like.",{"title":104,"detail":105},"Map sources and entitlements","List every source the pack draws on, who may see it, and which information barriers apply. The copilot inherits the banker's entitlements; it never widens them.",{"title":107,"detail":108},"Make every fact traceable","Require a source link on every figure and statement in the pack, and let the copilot say \"not found\" rather than fill gaps from general model knowledge.",{"title":110,"detail":111},"Draft call reports, do not file them","Generate the call report and CRM changes as drafts in the banker's queue; saving requires an explicit approval, so accountability for the record stays with the banker.",{"title":113,"detail":114},"Measure time and quality together","Track preparation time, call report completeness and timeliness, and banker ratings of the packs, and review a sample of packs each month for errors.",[116,117,118,119,120],"The copilot uses only the requesting banker's entitlements and respects information barriers","Every fact in a briefing carries a link to its source; unsupported statements are removed","External news and documents are treated as data, never as instructions, to resist prompt injection","No call report or CRM update is saved without the banker's approval","Meeting recording and transcription only with client consent, and with retention rules applied","The banker reviews every briefing and approves every call report and CRM update. Advice and any product recommendation to the client remain the banker's responsibility. A team lead samples packs and call reports each month for accuracy and completeness.",[123,124,125,126,127],"Preparation time per meeting, from a time study before and after","Share of meetings with a call report filed within 48 hours","Banker adoption, weekly active users against licensed users","Error rate found in monthly sampling of briefings","Follow up tasks created and completed after meetings",[129,132,135,138],{"title":130,"detail":131},"Confident but stale briefings","The pack repeats an old exposure or a superseded news item. Show the as of date of every source and refresh data on the day of the meeting.",{"title":133,"detail":134},"Leakage across information barriers","Content from a deal team or another client group appears in a pack. Enforce entitlements at retrieval time and test barrier cases in the regression set.",{"title":136,"detail":137},"Rubber stamped call reports","Bankers approve drafts without reading them and errors enter the CRM. Sample reports, and require edits on key fields such as next steps.",{"title":139,"detail":140},"Prompt injection from external content","A news article or document contains text that steers the model. Strip instructions from retrieved content and keep tool permissions narrow.",{"euAiAct":142,"regulations":145,"guidance":152,"controls":165,"incidents":171},{"tier":143,"basis":144},"minimal","Bankers interact with the copilot directly, but Article 50(1) does not bite here: it requires telling people they are dealing with an AI system unless that is obvious to a reasonably well informed person, and an internal tool that is openly presented and labelled as an AI assistant meets that bar by design. The copilot never interacts with the client. Article 50(2) marking of generated text falls on the provider of the system, including a bank that builds it in house, but the copilot turns a banker's own notes into a call report, an assistive function for standard editing of the banker's input that does not substantially alter it, so the Article 50(2) exception applies and no machine readable marking is required. It is not an Annex III use: credit context about corporate clients is not the creditworthiness assessment of natural persons in Annex III point 5(b), so it falls outside the high risk tier. If a deployment starts to score individuals for credit, the tier changes. AI literacy duties under Article 4 still apply. If meeting capture is used, recording and transcription rules under data protection law apply separately.",[146,147,148,149,150,151],"eu-ai-act","gdpr","dora","mas-ai-risk-management","nist-ai-rmf","iso-42001",[153,159],{"title":154,"issuer":155,"region":156,"url":157,"note":158},"MAS Guidelines for Artificial Intelligence (AI) Risk Management","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Consultation paper of 13 November 2025 proposing supervisory expectations for AI oversight, AI inventories and risk materiality assessments at all financial institutions, explicitly covering generative AI and AI agents. Comments were due by 31 January 2026; this page cites the proposals, not final guidelines.",{"title":160,"issuer":161,"region":162,"url":163,"note":164},"OWASP Top 10 for Large Language Model Applications","OWASP","global","https://genai.owasp.org/llm-top-10/","Prompt injection through retrieved news and documents is the main technical risk for a copilot that reads external content.",[166,167,168,169,170],"Inventory entry with an accountable owner and a list of approved data sources","Entitlement checks at retrieval time, including information barriers","Source logging for every briefing, retained with the call report","Consent capture before any meeting recording","Monthly quality sampling with results reported to the business owner",[],{"howToBuild":173},"On Blits.ai this is an **AI agent** for bankers, reachable in **Microsoft Teams** or a web\nwidget, that uses **custom functions** (REST calls) to read CRM, exposure and maturity data and a\n**knowledge base** with hybrid retrieval over approved internal documents. Public news can come\nthrough the built in web search tools or a connected **MCP** server for a licensed data provider.\n**Structured output** turns the pack and the call report into fixed templates.\n\nAn **agentic workflow** can prepare packs on a schedule before meetings and put CRM updates\nbehind **human in the loop approval**, so nothing is written back without the banker's\nconfirmation. **Guardrails** and **PII masking** protect client data in prompts, execution\ntracing records each agent turn for review, and **test suites** replay sample meetings on every\nprompt change. The platform is model agnostic and can run in the EU or UAE region.",[175,178,181],{"question":176,"answer":177},"How much time does an AI copilot save a relationship manager?","None of the deployments on this page reports a measured, quantified time saving. Bank of America says its meeting tool can save advisors up to four hours per meeting, but presents that as potential, not as a measured result, and Scotiabank's statement that a client report which took weeks now takes seconds comes from a proof of concept. Measure your own baseline preparation and call report time before rollout and compare on the same meeting types.",{"question":179,"answer":180},"Does the copilot give advice to clients?","No. It prepares information and drafts records for the banker. Advice and recommendations stay with the banker, who reviews every pack and approves every call report.",{"question":182,"answer":183},"What makes adoption stick?","Trust in the content matters as much as the tool, and a briefing is only as good as the CRM data behind it. Standard Chartered cleaned out most duplicated and outdated contacts as part of moving its corporate and investment bankers onto one CRM platform, the base for its AI client insights. Start with a meeting type bankers find tedious, show the source of every fact, and track weekly active use against licensed users.",[185,186,187,188,189,190],"client-meeting-notes-and-crm-update","credit-memo-drafting-agent","corporate-client-servicing-assistant","sales-call-coaching-and-crm-update","treasury-cash-flow-forecasting","credit-early-warning-monitoring","2026-09-27",[193],{"date":191,"note":194},"First published","client-briefing-and-call-report-copilot",[197,226,254],{"title":198,"useCases":199,"organization":200,"vendors":205,"summary":208,"stage":209,"year":210,"channels":211,"languages":212,"metrics":214,"outcomeDisclosed":202,"sources":215,"verification":220,"grade":223,"id":224,"organizationSlug":225},"Merrill and Bank of America Private Bank: AI Powered Meeting Journey",[195,185],{"name":201,"anonymized":202,"country":203,"region":204,"industry":18},"Bank of America",false,"US","north-america",[206],{"name":201,"role":207},"in-house","Merrill Wealth Management and Bank of America Private Bank rolled out an AI meeting solution at full scale in March 2026. It consolidates client relationship insights and recent activity into meeting preparation material, takes notes in virtual meetings with client consent, and turns the decisions into a summary, tasks and documentation afterwards. The bank says the capability can save advisors up to four hours per meeting; it presents this as potential, not as a measured result, so it is not recorded as a metric here.","scaled",2026,[29],[213],"en",[],[216],{"url":217,"title":218,"publisher":201,"date":219},"https://newsroom.bankofamerica.com/content/newsroom/press-releases/2026/03/merrill-and-bank-of-america-private-bank-launch-ai-powered-meeti.html","Merrill and Bank of America Private Bank Launch AI-Powered Meeting Journey","2026-03-26",{"level":221,"checkedAt":222},"source-verified","2026-09-26","B","bank-of-america-merrill-ai-meeting-journey","bank-of-america",{"title":227,"useCases":228,"organization":229,"vendors":232,"summary":239,"stage":240,"year":241,"channels":242,"languages":243,"metrics":244,"outcomeDisclosed":245,"sources":246,"verification":251,"grade":223,"id":252,"organizationSlug":253},"Scotiabank: AI agents that assemble the Client Insight Report for payments clients",[195],{"name":230,"anonymized":202,"country":231,"region":204,"industry":17},"Scotiabank","CA",[233,236],{"name":234,"role":235},"Microsoft","platform",{"name":237,"role":238},"EY","integrator","Scotiabank's Global Transaction Banking business prototyped a team of five specialised AI agents that transform, reconcile and explain a client's payment data and assemble the Client Insight Report, an analysis of what was processed, what failed and what remediation was needed, which the bank uses to discuss with the client the value it delivers. It is a payments analytics report that feeds client conversations rather than a full meeting briefing pack. The report used to be a manual, high touch service for a select set of clients; the bank says the work that took weeks now takes seconds, which unlocks the ability to serve all clients. The prototype was built with EY on Microsoft's Copilot platform in under three months.","announced",2025,[29],[213],[],true,[247],{"url":248,"title":249,"publisher":250},"https://gtb.scotiabank.com/en/global-transaction-banking/resources/insights/article.insights.how-ai-agents-are-transforming-scotiabank-s-payment-operations.html","How AI Agents are Transforming Scotiabank's Payment Operations","Scotiabank Global Transaction Banking",{"level":221,"checkedAt":191},"scotiabank-client-insight-report-agents",null,{"title":255,"useCases":256,"organization":257,"vendors":260,"summary":262,"stage":209,"year":263,"channels":264,"languages":265,"metrics":266,"outcomeDisclosed":245,"sources":267,"verification":272,"grade":273,"id":274,"organizationSlug":275},"Standard Chartered: Client Insights and automated call reports for corporate bankers",[195],{"name":258,"anonymized":202,"country":259,"region":162,"industry":17},"Standard Chartered","GB",[261],{"name":234,"role":235},"Standard Chartered's Corporate and Investment Banking division began moving its client relationship management onto one Dynamics 365 platform in 2021, to improve client engagement for the division's clients in the 67 countries in which it operates. The programme was meant to make the platform's more than 6,000 users more effective and productive. The platform combines news feeds and internal information with AI in a Client Insights feature that surfaces opportunities for proactive engagement, and makes automated call reports that document client engagement easy to create and share in Outlook; the story does not say that AI drafts those call reports. It says the bank plans to adopt further AI capabilities through Microsoft Copilot for Sales next.",2021,[29],[213],[],[268],{"url":269,"title":270,"publisher":271},"https://www.microsoft.com/en/customers/story/1762958431882021291-standard-chartered-bank-dynamics-365-sales-banking-and-capital-markets-en-united-kingdom","Standard Chartered sets new standard in innovation with Dynamics 365","Microsoft Customer Stories",{"level":221,"checkedAt":191},"C","standard-chartered-cib-client-insights-crm","standard-chartered",0,[],{"low":279,"high":280},1800000,9000000,[282,309,324,343,364,387],{"slug":185,"title":283,"shortTitle":284,"definition":285,"status":9,"industries":286,"functions":287,"patterns":290,"audience":32,"autonomy":33,"adoptionStage":292,"segment":293,"evidenceCount":294,"publicEvidenceCount":294,"organizations":295,"bestGrade":223,"headline":301,"lastVerified":191,"indexable":245},"AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","An AI notetaker for wealth advisors that turns a client advice meeting, recorded with the client's consent, into the file note, follow up message and CRM record the firm needs to evidence its advice; unlike a general meeting summarizer, its output becomes part of the regulated client record. It drafts a structured note with the client's goals, circumstances, decisions and action items, and writes it into the CRM once the advisor has approved it.",[18,17],[21,288,289],"regulatory-compliance","operations",[25,291,27,26],"speech-analytics","mainstream","front-office",6,[201,296,297,298,299,300],"Commerzbank","Morgan Stanley","Quilter","SEB","UniSuper",{"kpi":48,"label":302,"unit":303,"n":304,"nUpTo":276,"kind":305,"value":306,"qualifier":307,"claimant":308,"organization":299,"vendorReported":245},"Productivity gain","percent",1,"reported",15,"exact","vendor",{"slug":186,"title":310,"shortTitle":311,"definition":312,"status":9,"industries":313,"functions":314,"patterns":318,"audience":32,"autonomy":33,"adoptionStage":34,"segment":35,"evidenceCount":320,"publicEvidenceCount":320,"organizations":321,"bestGrade":223,"headline":253,"lastVerified":191,"indexable":245},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.",[17],[315,316,317],"lending-and-credit","underwriting","risk-management",[319,27,24,26],"document-processing",2,[322,323],"Banestes","DBS Bank",{"slug":187,"title":325,"shortTitle":326,"definition":327,"status":9,"industries":328,"functions":330,"patterns":332,"audience":334,"autonomy":335,"adoptionStage":34,"segment":35,"evidenceCount":336,"publicEvidenceCount":320,"organizations":337,"bestGrade":223,"headline":338,"lastVerified":191,"indexable":245},"AI assistant for corporate and commercial client servicing","Corporate client servicing","A conversational assistant inside the corporate banking portal, app and messaging channels that answers finance and treasury teams' servicing questions, such as payment status, balances, cut off times, fees and how to submit an instruction, resolves routine requests end to end and hands the rest to a service specialist who has an AI copilot.",[17,329],"payments",[331,289],"customer-service",[333,24,27,25],"conversational-agent","customer-facing","supervised-agent",5,[201,323],{"kpi":339,"label":340,"unit":303,"n":304,"nUpTo":276,"kind":305,"value":341,"qualifier":307,"claimant":342,"organization":201,"vendorReported":202},"contact-deflection","Contact deflection",16,"organization",{"slug":188,"title":344,"shortTitle":345,"definition":346,"status":9,"industries":347,"functions":352,"patterns":353,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":354,"publicEvidenceCount":354,"organizations":355,"bestGrade":273,"headline":360,"lastVerified":191,"indexable":245},"AI sales call coaching and CRM update","Sales call coaching and CRM update","AI for sales teams that analyses sales calls and meetings against the team's own sales method to coach sellers and their managers, and writes the call summary, next steps and opportunity updates into the CRM for the seller to confirm. Its purpose is winning deals and building selling skill, not the regulated advice record or general meeting notes.",[348,349,350,351],"cross-industry","telecommunications","manufacturing","insurance",[21],[291,25,26],4,[356,357,358,359],"Hughes Network Systems","Lumen Technologies","Sandvik Coromant","Zurich Insurance Group",{"kpi":44,"label":361,"unit":362,"n":304,"nUpTo":304,"kind":305,"value":363,"qualifier":307,"claimant":342,"organization":358,"vendorReported":202},"Time saved per task","minutes",3,{"slug":189,"title":365,"shortTitle":366,"definition":367,"status":9,"industries":368,"functions":371,"patterns":375,"audience":32,"autonomy":378,"adoptionStage":34,"segment":35,"evidenceCount":336,"publicEvidenceCount":336,"organizations":379,"bestGrade":223,"headline":384,"lastVerified":191,"indexable":245},"AI cash flow forecasting for corporate treasury","Treasury cash forecasting","Machine learning and conversational analytics, offered by some banks inside their cash management platforms, that categorise a company's cash flows, forecast positions across accounts and currencies, and answer treasurers' questions in plain language, so the treasury team decides on funding and idle balances with better information and less spreadsheet work.",[17,348,369,370,350],"logistics-and-transportation","retail-and-ecommerce",[372,373,374],"treasury","finance-and-accounting","analytics-and-reporting",[376,377,333,27],"prediction-and-scoring","classification-and-routing","assist",[380,201,381,382,383],"Amtrak","Domino's Pizza","JPMorgan Chase","Prysmian",{"kpi":48,"label":302,"unit":303,"n":320,"nUpTo":304,"kind":305,"value":385,"qualifier":386,"claimant":342,"organization":382,"vendorReported":202},90,"approximately",{"slug":190,"title":388,"shortTitle":389,"definition":390,"status":9,"industries":391,"functions":392,"patterns":393,"audience":32,"autonomy":378,"adoptionStage":34,"segment":395,"evidenceCount":363,"publicEvidenceCount":363,"organizations":396,"bestGrade":273,"headline":253,"lastVerified":191,"indexable":245},"AI early warning and covenant monitoring for loan portfolios","Credit early warning and covenants","A monitoring system that tracks covenant tests and borrower reporting across a loan book, reads financials, filings and news, and combines them with payment and sector signals to flag borrowers whose credit is deteriorating, with the evidence and a suggested next step for the relationship manager.",[17],[317,315],[394,319,27,25],"anomaly-detection","lending",[397,398,399],"OakNorth Bank","PNC Financial Services","Sumitomo Mitsui Banking Corporation",{"indexable":245,"reasons":401},[],[403,410,415,421,427,432,439,446,450,457,464,470,476,482,488,493,500,506,512,518,524,530,536,541,546,553,560,565,570,577,583,589,595,600],{"id":146,"label":404,"issuer":405,"region":406,"url":407,"description":408,"useCases":409,"indexable":245},"EU AI Act","European Union","europe","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":411,"issuer":405,"region":406,"url":412,"description":413,"useCases":414,"indexable":245},"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":151,"label":416,"issuer":417,"region":162,"url":418,"description":419,"useCases":420,"indexable":245},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":150,"label":422,"issuer":423,"region":204,"url":424,"description":425,"useCases":426,"indexable":245},"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":428,"issuer":405,"region":406,"url":429,"description":430,"useCases":431,"indexable":245},"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":433,"label":434,"issuer":435,"region":406,"url":436,"description":437,"useCases":438,"indexable":245},"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":440,"label":441,"issuer":442,"region":406,"url":443,"description":444,"useCases":445,"indexable":245},"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":447,"issuer":155,"region":156,"url":157,"description":448,"useCases":449,"indexable":245},"MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":451,"label":452,"issuer":453,"region":156,"url":454,"description":455,"useCases":456,"indexable":245},"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":458,"label":459,"issuer":460,"region":162,"url":461,"description":462,"useCases":463,"indexable":245},"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":465,"label":466,"issuer":467,"region":204,"url":468,"description":469,"useCases":463,"indexable":245},"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":471,"label":472,"issuer":473,"region":406,"url":474,"description":475,"useCases":341,"indexable":245},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":477,"label":478,"issuer":479,"region":162,"url":480,"description":481,"useCases":306,"indexable":245},"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.",{"id":483,"label":484,"issuer":405,"region":406,"url":485,"description":486,"useCases":487,"indexable":245},"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":489,"label":490,"issuer":405,"region":406,"url":491,"description":492,"useCases":487,"indexable":245},"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":494,"label":495,"issuer":496,"region":204,"url":497,"description":498,"useCases":499,"indexable":245},"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":501,"label":502,"issuer":405,"region":406,"url":503,"description":504,"useCases":505,"indexable":245},"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":507,"label":508,"issuer":509,"region":204,"url":510,"description":511,"useCases":505,"indexable":245},"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":513,"label":514,"issuer":515,"region":162,"url":516,"description":517,"useCases":505,"indexable":245},"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":519,"label":520,"issuer":405,"region":406,"url":521,"description":522,"useCases":523,"indexable":245},"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":525,"label":526,"issuer":527,"region":204,"url":528,"description":529,"useCases":523,"indexable":245},"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":531,"label":532,"issuer":155,"region":156,"url":533,"description":534,"useCases":535,"indexable":245},"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":537,"label":538,"issuer":405,"region":406,"url":539,"description":540,"useCases":535,"indexable":245},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":542,"label":543,"issuer":405,"region":406,"url":544,"description":545,"useCases":535,"indexable":245},"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":547,"label":548,"issuer":549,"region":406,"url":550,"description":551,"useCases":552,"indexable":245},"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":554,"label":555,"issuer":556,"region":204,"url":557,"description":558,"useCases":559,"indexable":245},"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":561,"label":562,"issuer":405,"region":406,"url":563,"description":564,"useCases":559,"indexable":245},"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":566,"label":567,"issuer":405,"region":406,"url":568,"description":569,"useCases":294,"indexable":245},"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":571,"label":572,"issuer":573,"region":574,"url":575,"description":576,"useCases":336,"indexable":245},"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":578,"label":579,"issuer":580,"region":406,"url":581,"description":582,"useCases":354,"indexable":245},"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":584,"label":585,"issuer":586,"region":406,"url":587,"description":588,"useCases":354,"indexable":245},"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":590,"label":591,"issuer":592,"region":156,"url":593,"description":594,"useCases":363,"indexable":245},"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":596,"label":597,"issuer":405,"region":406,"url":598,"description":599,"useCases":363,"indexable":245},"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":601,"label":602,"issuer":603,"region":204,"url":604,"description":605,"useCases":363,"indexable":245},"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.",1790598297842]