[{"data":1,"prerenderedAt":610},["ShallowReactive",2],{"uc-corporate-client-servicing-assistant":3,"uc-regulations":407},{"useCase":4,"evidence":197,"blitsAiDeployments":274,"benchmarks":275,"indicative":298,"related":301,"indexability":405,"includeUnpublished":203},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":22,"channels":27,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":52,"macroEstimates":86,"feasibility":87,"implementation":99,"risk":142,"blitsAi":173,"faq":175,"related":185,"datePublished":192,"dateModified":192,"lastVerified":192,"changelog":193,"slug":196},"AI assistant for corporate and commercial client servicing","Corporate client servicing","AI assistant for corporate banking client service","An AI assistant in the corporate banking portal answers payment and cut off questions and hands the rest to specialists. DBS Joy served about 4,000 clients a month.","published","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.",[12,13,14,15],"corporate banking virtual assistant","transaction banking chatbot","cash management service assistant","business banking virtual agent",[17,18],"banking","payments",[20,21],"customer-service","operations",[23,24,25,26],"conversational-agent","rag-knowledge-assistant","agentic-workflow","summarization",[28,29,30,31,32],"web-chat","mobile-app","agent-desktop","email","voice","customer-facing","supervised-agent","early-adopters","specialized-businesses","Corporate clients do not call about one card. Their finance and treasury teams ask where a\npayment is, why a file was rejected, what the cut off time is for a currency, how to add a user or\nreset a token, and what a fee on the analysis statement means. Many of these questions arrive at\nthe same moments (month end, payroll, a failed payment run), and every hour of delay can hold up a\nsupplier payment or a payroll.\n\nService teams for transaction banking are small and specialised, and much of their time goes to\nquestions whose answer already exists in a product guide or a status screen. Consumer style\nchatbots do not help much here: they lack the entitlement model of a corporate portal, cannot see\npayment status, and invent fees or cut off times when their content is thin.",[],"1. **Recognise the user and entitlements.** The assistant runs inside the authenticated portal\n   and only sees the accounts and functions the user is entitled to.\n2. **Answer from approved content.** Product guides, cut off tables, fee schedules and how to\n   articles come from the bank's own knowledge base, retrieved with citations.\n3. **Look things up.** Through read only APIs it checks payment status, balances, file\n   processing results and user administration status.\n4. **Complete simple requests.** Within an allow list (for example a token reset request or a\n   statement copy) it performs the action or opens a service request with the details filled in.\n5. **Hand over with context.** Anything outside the allow list, or where the client asks for a\n   person, goes to a service specialist with a summary, and the specialist's copilot drafts the\n   reply from the same knowledge base.",[41,42,43,44],"cost-to-serve","customer-experience","speed","employee-productivity",[46,47,48,49,50,51],"containment-rate","contact-deflection","interactions-handled","users-served","response-time-reduction","customer-satisfaction-uplift",{"referenceOrg":53,"inputs":54,"formula":81,"currency":82,"period":83,"resultLabel":84,"caveat":85},"A transaction bank serving 5,000 corporate and SME clients through its online platform",[55,60,67,74],{"key":56,"label":57,"low":58,"high":58,"unit":56,"note":59},"clients","Active corporate and SME clients",5000,"The reference bank.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"requestsPerClient","Servicing requests per client per year",10,20,"requests per client per year","Editorial assumption, replace with your own service desk volumes.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"containment","Share of requests the assistant resolves without a specialist",0.2,0.4,"fraction of requests","Editorial assumption. No bank on this page publishes a resolution rate. The high value is capped at the more than 40% of CashPro Chat client interactions Bank of America says Erica handles, which is a handled share used here as an upper bound, not a resolution rate.",{"key":75,"label":76,"low":77,"high":78,"unit":79,"note":80},"costPerRequest","Cost of a specialist handled request",15,30,"USD per request","Editorial assumption for a specialised transaction banking service desk. Replace with your own loaded cost.","clients * requestsPerClient * containment * costPerRequest","USD","per year","Specialist service cost avoided","Gross avoided service cost only. It leaves out the cost of the assistant and integrations, the time saved by the specialist copilot on the requests that are handed over, and the value to the client of faster answers at month end.",[],{"complexity":88,"complexityNote":89,"dataPrerequisites":90,"integrations":94},"medium","Answering from product content is straightforward. The effort is in the portal's entitlement model, read access to payment and file status, and a clean handover into the service desk tool.",[91,92,93],"Current product guides, cut off tables and fee schedules with named owners","Service request categories with volumes from the service desk","Payment, file and user administration status reachable through APIs",[95,96,97,98],"Corporate banking portal and app (authentication and entitlements)","Payment hub and file processing status","Service desk or CRM case management for handover","Specialist desktop for the copilot",{"steps":100,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[101,104,107,110,113],{"title":102,"detail":103},"Rank requests by volume and risk","Take a quarter of service desk tickets, group them into intents, and start with the high volume informational ones (payment status, cut off times, how to) before any action.",{"title":105,"detail":106},"Build on the portal's entitlements","Let the assistant call only the APIs the logged in user could use in the portal, and test that a user from one entity never sees another entity's data.",{"title":108,"detail":109},"Own the content","Give every guide and fee table an owner and a review date, and make the assistant refuse when retrieval finds nothing rather than guess a fee or cut off time.",{"title":111,"detail":112},"Equip the specialists","Put a copilot on the specialist desktop that drafts replies from the same content and shows the assistant's conversation summary, so handovers are fast.",{"title":114,"detail":115},"Review conversations, not only dashboards","Have experienced service staff review a sample of conversations every week and feed corrections back into the content. DBS uses experienced customer service agents as DBS Joy evaluators, who assess the quality of responses after the chat and suggest improvements.",[117,118,119,120,121],"Access limited to the user's portal entitlements, enforced on every API call","Answers on fees, cut off times and terms only from approved content, with citations","Read only by default; any action goes through an allow list with its own authentication level","A visible way to reach a human at any point","Full transcripts retained for disputes and complaints","Service specialists handle every request outside the allow list, every complaint and any case where the client asks for a person. Experienced staff review a sample of assistant conversations each week and approve content changes before they go live.",[124,125,126,127,128],"Containment per intent, counting a repeat request within seven days as not contained","Time to first answer and time to resolution against the specialist channel","Client satisfaction on assistant conversations and on handed over cases","Share of answers with a citation, and answers corrected by evaluators","Monthly active client users of the assistant",[130,133,136,139],{"title":131,"detail":132},"Invented fees or cut off times","The assistant answers from general knowledge when content is missing. Force refusal when retrieval is empty and test with questions outside the content.",{"title":134,"detail":135},"Entitlement leaks","A user sees another entity's payments through the assistant. Enforce entitlements in the API layer, not in the prompt, and include cross entity tests.",{"title":137,"detail":138},"Handover that loses context","The specialist starts again and the client repeats everything. Pass the summary, the identified entity and the steps already tried.",{"title":140,"detail":141},"Month end overload","Volume spikes expose slow integrations and time outs. Load test at month end volumes before launch.",{"euAiAct":143,"regulations":146,"guidance":153,"controls":166,"incidents":172},{"tier":144,"basis":145},"limited","A chatbot that interacts with people at client companies must disclose that it is AI (Article 50). It does not evaluate creditworthiness or decide on access to an essential service (Annex III point 5), so it is not high risk.",[147,148,149,150,151,152],"eu-ai-act","gdpr","dora","mas-ai-risk-management","hkma-genai","apra-cps-230",[154,160],{"title":155,"issuer":156,"region":157,"url":158,"note":159},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://artificialintelligenceact.eu/article/50/","Users must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":161,"issuer":162,"region":163,"url":164,"note":165},"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 issued on 13 November 2025 proposing supervisory expectations for all financial institutions on AI oversight, AI inventories and life cycle controls, covering generative AI and AI agents.",[167,168,169,170,171],"AI disclosure in the assistant and a documented route to a human","Inventory entry with an owner, the content sources and the action allow list","Entitlement tests and regression tests on every release","Transcript retention in line with the bank's record keeping rules","Weekly quality review by experienced service staff",[],{"howToBuild":174},"On Blits.ai this is an **AI agent** embedded in the corporate portal through the **web widget**\nor the REST and WebSocket **API channel**, with a **knowledge base** of product guides, fee\nschedules and cut off tables retrieved with hybrid search. **Custom functions** call the bank's\npayment status and service request APIs, which apply the portal's own entitlement checks, so\naccess control stays in the bank's API layer. Regulated steps, such as a token reset request, can run as a **flow** with fixed\nsteps that calls the bank's own authentication service.\n\n**Human handover** passes the conversation, optionally summarised by AI, to the service desk, including Salesforce,\nand **live takeover** lets a specialist join a running conversation. **Guardrails** and **PII\nmasking** apply to every message, **test suites** replay real questions on each content or\nprompt change, and **monitors** check the key answers on a schedule. Analytics show volumes, top\nintents and satisfaction, and the model can be chosen or switched per agent.",[176,179,182],{"question":177,"answer":178},"How is a corporate servicing assistant different from a retail chatbot?","It works inside the corporate portal's entitlement model, answers treasury and payments questions rather than card questions, and hands over to specialised service teams. DBS runs DBS Joy inside its IDEAL platform, and said in November 2025 that about 4,000 corporate clients used it every month.",{"question":180,"answer":181},"How much of the chat volume can it take on?","None of the banks cited on this page publishes a resolution rate for its corporate assistant. Bank of America says Erica handles more than 40% of client interactions in CashPro Chat, and that chats with a live agent fell 16% after the Erica integration while chat volume rose 41%. Results depend on the intent mix and on whether the assistant can see payment and file status.",{"question":183,"answer":184},"How do banks keep the answers accurate?","By grounding answers in the bank's own knowledge base, filtering responses through rule based checks, and having experienced service staff assess responses and suggest improvements, as DBS describes for DBS Joy.",[186,187,188,189,190,191],"payment-investigations-and-exceptions","client-briefing-and-call-report-copilot","developer-api-integration-assistant","treasury-cash-flow-forecasting","corporate-account-onboarding-orchestration","account-and-card-servicing-agent","2026-09-27",[194],{"date":192,"note":195},"First published","corporate-client-servicing-assistant",[198,243],{"title":199,"useCases":200,"organization":201,"vendors":205,"summary":208,"stage":209,"year":210,"channels":211,"languages":212,"metrics":213,"outcomeDisclosed":232,"sources":233,"verification":237,"grade":240,"id":241,"organizationSlug":242},"DBS: generative AI DBS Joy assistant for corporate and SME clients",[196],{"name":202,"anonymized":203,"country":204,"region":163,"industry":17},"DBS Bank",false,"SG",[206],{"name":202,"role":207},"in-house","DBS rolled out a generative AI version of DBS Joy, its virtual assistant for corporate clients, inside the IDEAL digital banking platform. It answers servicing questions around the clock from the bank's own knowledge base, passes complex requests to a service specialist who has a generative AI copilot, and its answers are reviewed afterwards by experienced customer service agents working as DBS Joy evaluators. DBS reports customer satisfaction scores improved by over 23% over the trial period. A later update made DBS Joy agentic (see the separate DBS Joy and digibot record).","scaled",2025,[28],[],[214,222,227],{"kpi":48,"value":215,"unit":216,"qualifier":217,"period":218,"claimant":219,"quote":220,"sourceUrl":221},120000,"count","at-least","unique chats since the start of trials","organization","Since early trials of the new features started in February, DBS Joy has managed over 120,000 unique chats and counting.","https://www.dbs.com/newsroom/DBS_rolls_out_Gen_AI_powered_chatbot_to_all_corporate_clients",{"kpi":49,"value":223,"unit":216,"qualifier":224,"period":225,"claimant":219,"quote":226,"sourceUrl":221},4000,"approximately","corporate clients per month","About 4,000 corporate clients, the vast majority of which are small and medium enterprises, now use the service every month.",{"kpi":51,"value":228,"unit":229,"qualifier":217,"period":230,"claimant":219,"quote":231,"sourceUrl":221},23,"percent","users of the virtual agent, from the start of trials in February to November 2025","In addition to quicker responses and shorter wait times, users of the virtual agent were also more satisfied with their experience, with customer satisfaction scores improving by over 23% in the same period.",true,[234],{"url":221,"title":235,"publisher":202,"date":236},"DBS rolls out Gen AI-powered chatbot to all corporate clients","2025-11-10",{"level":238,"checkedAt":239},"source-verified","2026-09-26","B","dbs-joy-corporate-virtual-assistant","dbs-bank",{"title":244,"useCases":245,"organization":246,"vendors":250,"summary":252,"stage":209,"year":253,"channels":254,"languages":255,"metrics":256,"outcomeDisclosed":232,"sources":263,"verification":271,"grade":240,"id":272,"organizationSlug":273},"Bank of America: CashPro Chat with Erica for business clients",[196],{"name":247,"anonymized":203,"country":248,"region":249,"industry":17},"Bank of America","US","north-america",[251],{"name":247,"role":207},"Bank of America brought the AI behind its Erica assistant into CashPro Chat, the service assistant inside the CashPro platform that corporate and commercial clients use for payments, deposits, loans and trade. It finds transactions and account information, guides users through the platform and routes complex requests to specialised service teams. After the Erica integration, chat volume rose 41% on the 2023 weekly average while chats with a live agent fell 16%. In August 2025 the bank said 65% of CashPro clients use CashPro Chat and that Erica handles more than 40% of client interactions in it.",2023,[28],[],[257],{"kpi":47,"value":258,"unit":229,"qualifier":259,"period":260,"claimant":219,"quote":261,"sourceUrl":262},16,"exact","chats with a live agent since the Erica integration, while chat volume rose 41% compared to the 2023 weekly average","Since launch, chat volume increased by 41% compared to the 2023 weekly average. Meanwhile, chats with a live agent decreased by 16%.","https://newsroom.bankofamerica.com/content/newsroom/press-releases/2023/09/enhancements-to-bofa-s-cashpro--chat-create-greater-efficiencies.html",[264,267],{"url":262,"title":265,"publisher":247,"date":266},"Enhancements to BofA's CashPro Chat Create Greater Efficiencies for Business Clients","2023-09-18",{"url":268,"title":269,"publisher":247,"date":270},"https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/08/a-decade-of-ai-innovation--bofa-s-virtual-assistant-erica-surpas.html","A Decade of AI Innovation: BofA's Virtual Assistant Erica Surpasses 3 Billion Client Interactions","2025-08-20",{"level":238,"checkedAt":192},"bank-of-america-cashpro-chat","bank-of-america",3,[276,283,288,293],{"kpi":47,"label":277,"unit":229,"aggregate":232,"higherIsBetter":232,"n":278,"nUpTo":279,"median":258,"min":258,"max":258,"byClaimant":280,"vendorOnly":203,"points":281},"Contact deflection",1,0,{"organization":278,"vendor":279,"regulator":279,"independent":279},[282],{"evidenceId":272,"organization":247,"value":258,"qualifier":259,"claimant":219,"grade":240,"pooled":232},{"kpi":48,"label":284,"unit":216,"aggregate":203,"higherIsBetter":232,"n":278,"nUpTo":279,"median":215,"min":215,"max":215,"byClaimant":285,"vendorOnly":203,"points":286},"Interactions handled",{"organization":278,"vendor":279,"regulator":279,"independent":279},[287],{"evidenceId":241,"organization":202,"value":215,"qualifier":217,"claimant":219,"grade":240,"pooled":232},{"kpi":51,"label":289,"unit":229,"aggregate":232,"higherIsBetter":232,"n":278,"nUpTo":279,"median":228,"min":228,"max":228,"byClaimant":290,"vendorOnly":203,"points":291},"Satisfaction uplift",{"organization":278,"vendor":279,"regulator":279,"independent":279},[292],{"evidenceId":241,"organization":202,"value":228,"qualifier":217,"claimant":219,"grade":240,"pooled":232},{"kpi":49,"label":294,"unit":216,"aggregate":203,"higherIsBetter":232,"n":278,"nUpTo":279,"median":223,"min":223,"max":223,"byClaimant":295,"vendorOnly":203,"points":296},"Users served",{"organization":278,"vendor":279,"regulator":279,"independent":279},[297],{"evidenceId":241,"organization":202,"value":223,"qualifier":224,"claimant":219,"grade":240,"pooled":232},{"low":299,"high":300},150000,1200000,[302,322,339,359,382,391],{"slug":186,"title":303,"shortTitle":304,"definition":305,"status":9,"industries":306,"functions":307,"patterns":308,"audience":312,"autonomy":34,"adoptionStage":313,"segment":312,"evidenceCount":314,"publicEvidenceCount":314,"organizations":315,"bestGrade":240,"headline":318,"lastVerified":192,"indexable":232},"AI for payment investigations and exceptions","Payment investigations and exceptions","AI that works the payments that fall out of straight through processing: it reads the failure, repairs or enriches the message, drafts the ISO 20022 or SWIFT investigation, chases the counterparty bank and proposes a return, recall or correction, while an operator approves anything that moves money.",[17,18],[21,20],[25,309,310,311],"document-processing","classification-and-routing","content-generation","back-office","emerging",2,[316,317],"BNY","JPMorgan Chase",{"kpi":319,"label":320,"unit":229,"n":278,"nUpTo":279,"kind":321,"value":63,"qualifier":217,"claimant":219,"organization":316,"vendorReported":203},"automation-rate","Automation rate","reported",{"slug":187,"title":323,"shortTitle":324,"definition":325,"status":9,"industries":326,"functions":329,"patterns":332,"audience":333,"autonomy":334,"adoptionStage":35,"segment":36,"evidenceCount":274,"publicEvidenceCount":274,"organizations":335,"bestGrade":240,"headline":338,"lastVerified":192,"indexable":232},"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.",[17,327,328],"wealth-and-asset-management","capital-markets",[330,331],"sales","knowledge-management",[24,26,311,25],"employee-facing","copilot",[247,336,337],"Scotiabank","Standard Chartered",null,{"slug":188,"title":340,"shortTitle":341,"definition":342,"status":9,"industries":343,"functions":346,"patterns":349,"audience":33,"autonomy":351,"adoptionStage":35,"segment":36,"evidenceCount":352,"publicEvidenceCount":352,"organizations":353,"bestGrade":240,"headline":358,"lastVerified":192,"indexable":232},"AI assistant for developers integrating a company's APIs","API integration assistant","An AI assistant on a developer portal and in its documentation that answers integration questions, recommends the right endpoints, helps debug connections and generates sample calls, grounded in the API catalogue, reference docs and test material, so clients and partners integrate faster with fewer support tickets.",[344,17,18,345],"cross-industry","technology",[347,20,348],"it-and-engineering","onboarding-and-kyc",[24,23,350],"code-generation","assist",4,[354,355,356,357],"CircleCI","Mapbox","monday.com","U.S. Bank",{"kpi":47,"label":277,"unit":229,"n":278,"nUpTo":279,"kind":321,"value":78,"qualifier":259,"claimant":219,"organization":355,"vendorReported":203},{"slug":189,"title":360,"shortTitle":361,"definition":362,"status":9,"industries":363,"functions":367,"patterns":371,"audience":333,"autonomy":351,"adoptionStage":35,"segment":36,"evidenceCount":373,"publicEvidenceCount":373,"organizations":374,"bestGrade":240,"headline":378,"lastVerified":192,"indexable":232},"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.",[17,344,364,365,366],"logistics-and-transportation","retail-and-ecommerce","manufacturing",[368,369,370],"treasury","finance-and-accounting","analytics-and-reporting",[372,310,23,25],"prediction-and-scoring",5,[375,247,376,317,377],"Amtrak","Domino's Pizza","Prysmian",{"kpi":379,"label":380,"unit":229,"n":314,"nUpTo":278,"kind":321,"value":381,"qualifier":224,"claimant":219,"organization":317,"vendorReported":203},"productivity-gain","Productivity gain",90,{"slug":190,"title":383,"shortTitle":384,"definition":385,"status":9,"industries":386,"functions":387,"patterns":388,"audience":33,"autonomy":334,"adoptionStage":313,"segment":36,"evidenceCount":314,"publicEvidenceCount":314,"organizations":389,"bestGrade":240,"headline":338,"lastVerified":192,"indexable":232},"AI orchestration of corporate account opening and channel setup","Corporate onboarding operations","An AI agent that runs the operational setup of a corporate client after the due diligence has been approved: it reads mandates, board resolutions and signatory documents, prepares accounts, users, roles and payment entitlements for approval, configures channel access, and chases outstanding items with the client, turning a manual setup that passes between several teams into a tracked, guided flow.",[17],[348,21],[25,309,23,311],[390,337],"Citi",{"slug":191,"title":392,"shortTitle":393,"definition":394,"status":9,"industries":395,"functions":396,"patterns":397,"audience":33,"autonomy":34,"adoptionStage":399,"segment":400,"evidenceCount":352,"publicEvidenceCount":314,"organizations":401,"bestGrade":240,"headline":403,"lastVerified":192,"indexable":232},"AI agent for account and card servicing","Account and card servicing","An AI agent that resolves routine account and card requests end to end, such as balances, statements, card blocks and replacements, PIN resets and limit changes, across app, web, messaging and phone, and hands anything sensitive or unusual to a human with the full context.",[17,18],[20,21],[23,398,25,24],"voice-agent","mainstream","front-office",[402,202],"Commonwealth Bank of Australia",{"kpi":46,"label":404,"unit":229,"n":314,"nUpTo":279,"kind":321,"value":381,"qualifier":224,"claimant":219,"organization":202,"vendorReported":203},"Containment rate",{"indexable":232,"reasons":406},[],[408,413,418,426,433,438,445,452,456,462,468,474,480,486,492,497,504,510,516,522,528,534,539,544,549,556,563,568,574,581,587,593,599,604],{"id":147,"label":409,"issuer":156,"region":157,"url":410,"description":411,"useCases":412,"indexable":232},"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":148,"label":414,"issuer":156,"region":157,"url":415,"description":416,"useCases":417,"indexable":232},"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":419,"label":420,"issuer":421,"region":422,"url":423,"description":424,"useCases":425,"indexable":232},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":427,"label":428,"issuer":429,"region":249,"url":430,"description":431,"useCases":432,"indexable":232},"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":149,"label":434,"issuer":156,"region":157,"url":435,"description":436,"useCases":437,"indexable":232},"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":439,"label":440,"issuer":441,"region":157,"url":442,"description":443,"useCases":444,"indexable":232},"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":446,"label":447,"issuer":448,"region":157,"url":449,"description":450,"useCases":451,"indexable":232},"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":150,"label":453,"issuer":162,"region":163,"url":164,"description":454,"useCases":455,"indexable":232},"MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":152,"label":457,"issuer":458,"region":163,"url":459,"description":460,"useCases":461,"indexable":232},"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":463,"label":464,"issuer":465,"region":422,"url":466,"description":467,"useCases":64,"indexable":232},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":469,"label":470,"issuer":471,"region":249,"url":472,"description":473,"useCases":64,"indexable":232},"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":475,"label":476,"issuer":477,"region":157,"url":478,"description":479,"useCases":258,"indexable":232},"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":481,"label":482,"issuer":483,"region":422,"url":484,"description":485,"useCases":77,"indexable":232},"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":487,"label":488,"issuer":156,"region":157,"url":489,"description":490,"useCases":491,"indexable":232},"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":493,"label":494,"issuer":156,"region":157,"url":495,"description":496,"useCases":491,"indexable":232},"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":498,"label":499,"issuer":500,"region":249,"url":501,"description":502,"useCases":503,"indexable":232},"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":505,"label":506,"issuer":156,"region":157,"url":507,"description":508,"useCases":509,"indexable":232},"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":511,"label":512,"issuer":513,"region":249,"url":514,"description":515,"useCases":509,"indexable":232},"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":517,"label":518,"issuer":519,"region":422,"url":520,"description":521,"useCases":509,"indexable":232},"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":523,"label":524,"issuer":156,"region":157,"url":525,"description":526,"useCases":527,"indexable":232},"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":529,"label":530,"issuer":531,"region":249,"url":532,"description":533,"useCases":527,"indexable":232},"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":535,"label":536,"issuer":162,"region":163,"url":537,"description":538,"useCases":63,"indexable":232},"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":540,"label":541,"issuer":156,"region":157,"url":542,"description":543,"useCases":63,"indexable":232},"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":545,"label":546,"issuer":156,"region":157,"url":547,"description":548,"useCases":63,"indexable":232},"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":550,"label":551,"issuer":552,"region":157,"url":553,"description":554,"useCases":555,"indexable":232},"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":557,"label":558,"issuer":559,"region":249,"url":560,"description":561,"useCases":562,"indexable":232},"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":564,"label":565,"issuer":156,"region":157,"url":566,"description":567,"useCases":562,"indexable":232},"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":569,"label":570,"issuer":156,"region":157,"url":571,"description":572,"useCases":573,"indexable":232},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":575,"label":576,"issuer":577,"region":578,"url":579,"description":580,"useCases":373,"indexable":232},"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":582,"label":583,"issuer":584,"region":157,"url":585,"description":586,"useCases":352,"indexable":232},"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":588,"label":589,"issuer":590,"region":157,"url":591,"description":592,"useCases":352,"indexable":232},"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":594,"label":595,"issuer":596,"region":163,"url":597,"description":598,"useCases":274,"indexable":232},"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":600,"label":601,"issuer":156,"region":157,"url":602,"description":603,"useCases":274,"indexable":232},"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":605,"label":606,"issuer":607,"region":249,"url":608,"description":609,"useCases":274,"indexable":232},"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.",1790598296590]