[{"data":1,"prerenderedAt":577},["ShallowReactive",2],{"uc-credit-memo-drafting-agent":3,"uc-regulations":372},{"useCase":4,"evidence":202,"blitsAiDeployments":266,"benchmarks":267,"indicative":274,"related":277,"indexability":370,"includeUnpublished":208},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":27,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"problem":33,"problemStats":34,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":52,"macroEstimates":87,"feasibility":88,"implementation":102,"risk":145,"blitsAi":179,"faq":181,"related":191,"datePublished":197,"dateModified":197,"lastVerified":197,"changelog":198,"slug":201},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","AI credit memo drafting for corporate banks","AI agents spread borrower financials and draft credit memos for bankers to challenge and sign. DBS rolled one out to about 1,500 staff after a 150 user pilot.","published","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.",[12,13,14,15],"credit memo generator","AI credit analyst","financial spreading automation","credit proposal drafting",[17],"banking",[19,20,21],"lending-and-credit","underwriting","risk-management",[23,24,25,26],"document-processing","agentic-workflow","rag-knowledge-assistant","content-generation",[28],"internal-tools","employee-facing","copilot","early-adopters","specialized-businesses","A corporate credit memo is a long document built from many sources: audited accounts, management\naccounts, projections, industry research, bureau data, internal exposure and conduct records, and\nthe bank's own credit policy. Relationship managers sift through annual reports, industry research\nand internal records to build each memo; DBS says preparing credit memos and related credit\nactivities can take up to 40% of a relationship manager's time.\n\nThat is time bankers do not spend with clients. An agent can do the assembly, spreading and first\ndraft, and a shared template with deterministic calculations also keeps memos consistent between\nauthors. But credit is a regulated decision: the value only holds if every number is traceable and\npeople still own the judgement and the approval.",[35],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"DBS says preparing credit memos and related credit activities can account for up to 40% of a relationship manager's time.","DBS scales agentic AI to transform way of working for corporate bankers","https://www.dbs.com/newsroom/DBS_scales_agentic_AI_to_transform_way_of_working_for_corporate_bankers_freeing_up_time_for_more_strategic_client_engagements",2026,"1. **Collect the file.** The agent gathers financial statements, projections and supporting\n   documents from the client portal, email and document store, and lists what is missing.\n2. **Extract and spread.** Document AI extracts line items and maps them into the bank's spreading\n   template, flagging items that need an analyst's judgement.\n3. **Analyse.** It calculates ratios, trends and covenant headroom, and pulls bureau data, news,\n   internal exposure and conduct records through approved connectors.\n4. **Check against policy.** Retrieval over the credit policy and sector guidelines highlights\n   exceptions and required approvals.\n5. **Draft the memo.** It writes each section of the memo in the bank's format, with a link from\n   every figure to its source page and a list of risk flags and open questions.\n6. **Iterate and sign.** The relationship manager and credit risk manager challenge the draft, ask\n   the agent for deeper research, edit, and take it through the normal approval.",[42,43,44,45],"employee-productivity","speed","risk-reduction","compliance",[47,48,49,50,51],"time-saved-per-task","processing-time-reduction","productivity-gain","users-served","accuracy",{"referenceOrg":53,"inputs":54,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A corporate bank preparing 2,000 credit memos a year for new facilities and annual reviews",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"memos","Credit memos per year",2000,"memos per year","The reference bank.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"hoursPerMemo","Hours of relationship manager and analyst work per memo",20,40,"hours per memo","Editorial assumption, replace with your own time study.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74,"sourceUrl":38},"timeSaved","Share of that time saved",0.15,0.3,"fraction of time","Editorial assumption. The upper bound equals the at least 30% goal DBS has set, which is a target and not yet a measured result.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"hourlyCost","Loaded cost of a banker or analyst hour",90,140,"USD per hour","Editorial assumption, replace with your own loaded cost.","memos * hoursPerMemo * timeSaved * hourlyCost","USD","per year","Banker and analyst time released, valued at loaded cost","Values released time only. It leaves out the cost of the agent, data licences and model risk validation, and any effect on credit quality, faster time to yes for clients or revenue from the time bankers win back.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":96},"high","Many systems, a regulated decision and model risk validation. Spreading accuracy on messy financial statements, integration with the loan origination system and the bank's approval workflow, and a clear audit trail are the hard parts.",[92,93,94,95],"The bank's spreading template, memo template and credit policy in machine readable form","Historical memos and spreads to test against","Access to bureau, rating and news sources licensed for this use","Internal exposure, limit and conduct data per client group",[97,98,99,100,101],"Loan origination or credit workflow system","Document management and client portal","Bureau, rating agency and news data providers","Core lending and limits systems","CRM for client context",{"steps":103,"guardrails":119,"humanInTheLoop":125,"kpisToInstrument":126,"failureModes":132},[104,107,110,113,116],{"title":105,"detail":106},"Start with annual reviews","Annual reviews of existing clients have prior memos and spreads to compare with, which makes accuracy measurable and the change less risky than new to bank credit.",{"title":108,"detail":109},"Decompose the memo into tasks","Break the memo into its tasks (spreading, ratio analysis, industry section, peer comparison, policy exceptions) and automate them one by one; DBS says its agents handle more than 70 tasks.",{"title":111,"detail":112},"Make traceability non negotiable","Link every figure to a source page and every statement to a document or data source, and block the draft from moving on while any figure lacks a source.",{"title":114,"detail":115},"Validate like a model","Run the agent on past files, compare spreads and ratios with the approved versions, and take the results through model risk validation before live use.",{"title":117,"detail":118},"Pilot with a small group, then scale","Pilot with experienced relationship and credit managers, capture their corrections, and widen the rollout only when error rates are stable; DBS went from 150 pilot users to about 1,500.",[120,121,122,123,124],"The agent drafts; credit decisions and approvals follow the bank's existing authority matrix","Every figure and statement in the memo links to its source","Numbers are calculated by deterministic code, not generated by the language model","Retrieved documents and news are treated as data, never as instructions","Version history of each draft, with the human edits, is retained with the credit file","Relationship managers and credit risk managers review, challenge and complete every draft, and the approval follows the normal credit authority. Model validation reviews the agent before use and periodically after, and credit risk samples memos to check that the analysis did not become thinner.",[127,128,129,130,131],"Elapsed time from complete file to memo ready for approval","Analyst and banker hours per memo, from time studies","Spreading accuracy against approved spreads on a sample","Share of memo figures edited by humans, by section","Credit committee questions or returns per memo, before and after",[133,136,139,142],{"title":134,"detail":135},"Fluent but wrong numbers","A misread statement or unit error flows into ratios and the narrative. Use deterministic calculation, reconciliation checks and source links on every figure.",{"title":137,"detail":138},"Automation bias","Reviewers accept the draft instead of analysing the credit. Track edit rates and committee challenges, and keep sections that need judgement explicitly blank for the banker.",{"title":140,"detail":141},"Stale or unlicensed data","News or ratings are outdated or not licensed for AI use. Record the as of date and licence of every external source.",{"title":143,"detail":144},"Scope creep into individual lending","The same agent is reused for sole traders or personal guarantors, which changes the regulatory tier. Assess each new borrower segment before use.",{"euAiAct":146,"regulations":149,"guidance":154,"controls":172,"incidents":178},{"tier":147,"basis":148},"context-dependent","Annex III point 5(b) makes AI used to evaluate the creditworthiness of natural persons high risk. Credit analysis of companies is outside that point, but the tier can change when the same system evaluates the creditworthiness of natural persons, such as sole traders, partners who are personally liable or personal guarantors. Design the scope explicitly and document it.",[150,151,152,153],"eu-ai-act","eba-loan-origination","gdpr","apra-cps-230",[155,161,166],{"title":156,"issuer":157,"region":158,"url":159,"note":160},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(b) covers creditworthiness evaluation and credit scoring of natural persons, which sets the boundary for this use case.",{"title":162,"issuer":163,"region":158,"url":164,"note":165},"EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/activities/single-rulebook/regulatory-activities/credit-risk/guidelines-loan-origination-and-monitoring","Expectations on creditworthiness assessment, the information to collect and the use of automated models in credit decisions.",{"title":167,"issuer":168,"region":169,"url":170,"note":171},"MAS consultation paper on proposed Guidelines on 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 issued 13 November 2025, not final guidelines. Proposed expectations on AI inventory, risk materiality and human oversight, relevant for agents that support credit decisions.",[173,174,175,176,177],"Model inventory entry and independent validation before live use","Documented borrower scope, with a check that no natural persons are assessed without the high risk controls","Source logging and draft version history retained with the credit file","Periodic back testing of spreads and memo quality","Clear accountability, with the approver named in the authority matrix owning the decision",[],{"howToBuild":180},"On Blits.ai this is an **agentic workflow**: an agent loop with **custom functions** for the\nspreading and ratio calculations (deterministic code in the isolated sandbox), connectors to the\nloan origination, exposure and bureau systems through REST calls or **MCP**, and a **knowledge\nbase** with hybrid retrieval over the credit policy and sector guidelines. Borrower documents in\nPDF, XLSX and DOCX go through document ingestion into vector storage, and **structured output**\nfills the memo template section by section.\n\n**Human in the loop approval** can be required before steps that write to the credit workflow, and\neach run keeps a full **audit trail**. **Guardrails** and **PII\nmasking** protect client data in prompts, **test suites** replay past credit files to catch\nregressions, and the platform is model agnostic, so the bank can choose a model per task and keep\ndata in the EU or UAE region.",[182,185,188],{"question":183,"answer":184},"How much faster can AI make a credit memo?","Public results are still mostly targets. DBS has set a goal of reducing the time spent by at least 30% and rolled its agentic solution out to about 1,500 employees. Measure your own baseline per memo type before claiming a saving.",{"question":186,"answer":187},"Is AI credit memo drafting high risk under the EU AI Act?","Not for companies. Annex III point 5(b) covers creditworthiness evaluation of natural persons, so corporate credit analysis is outside it, but assessing sole traders or personal guarantors can bring the system into scope.",{"question":189,"answer":190},"Who is accountable for the memo?","The relationship manager and credit risk manager who complete it, and the approver in the credit authority matrix. The agent prepares a draft; it does not recommend approval on its own.",[192,193,194,195,196],"sme-cash-flow-underwriting","credit-early-warning-monitoring","client-briefing-and-call-report-copilot","business-onboarding-and-ubo-discovery","underwriting-risk-assessment-copilot","2026-09-27",[199],{"date":197,"note":200},"First published","credit-memo-drafting-agent",[203,236],{"title":204,"useCases":205,"organization":206,"vendors":210,"summary":213,"stage":214,"year":39,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":226,"sources":227,"verification":231,"grade":233,"id":234,"organizationSlug":235},"DBS: agentic AI that drafts credit memos for corporate bankers",[201],{"name":207,"anonymized":208,"country":209,"region":169,"industry":17},"DBS Bank",false,"SG",[211],{"name":207,"role":212},"in-house","DBS rolled out an agentic AI solution in which specialised agents handle more than 70 tasks to turn raw data (annual reports, industry research, internal records) into a review ready first draft of a credit memo for large and mid sized corporate clients. Relationship managers and credit risk managers iterate with the agents to reach the final memo. After a pilot with 150 users it reached about 1,500 employees globally in August 2026. DBS states a goal of cutting the time spent by at least 30%; that is a target, not a measured result.","production",[28],[217],"en",[219],{"kpi":50,"value":220,"unit":221,"qualifier":222,"period":223,"claimant":224,"quote":225,"sourceUrl":38},1500,"count","approximately","employees globally, August 2026 rollout","organization","After an initial pilot phase involving 150 participants, the capability has been rolled out to approximately 1,500 employees globally.",true,[228],{"url":38,"title":229,"publisher":207,"date":230},"DBS scales agentic AI to transform way of working for corporate bankers, freeing up time for more strategic client engagements","2026-08-19",{"level":232,"checkedAt":197},"source-verified","B","dbs-agentic-credit-memo","dbs-bank",{"title":237,"useCases":238,"organization":239,"vendors":243,"summary":247,"stage":214,"year":248,"channels":249,"languages":250,"metrics":252,"outcomeDisclosed":208,"sources":253,"verification":262,"grade":263,"id":264,"organizationSlug":265},"Banestes: Gemini to speed up credit analysis and balance sheet reviews",[201],{"name":240,"anonymized":208,"country":241,"region":242,"industry":17},"Banestes","BR","latin-america",[244],{"name":245,"role":246},"Google Cloud","platform","Banestes, a Brazilian bank, used Gemini in Google Workspace to accelerate credit analysis by simplifying balance sheet reviews. Banestes CTO Vicente Lopes Duarte said the generative AI tool has made it easier to read balance sheet documents, helping credit analysis teams work faster. No outcome figures for credit analysis are published; the story's only figure, a 70% ticket reduction, is about AppSheet, a separate Workspace tool, not the credit analysis use.",2025,[28],[251],"pt",[],[254,258],{"url":255,"title":256,"publisher":257},"https://workspace.google.com/intl/pt-BR/customers/banestes/","Banestes: soluções do Google Workspace","Google",{"url":259,"title":260,"publisher":245,"date":261},"https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders","Real-world gen AI use cases from the world's leading organizations","2026-04-22",{"level":232,"checkedAt":197},"C","banestes-gemini-credit-analysis",null,0,[268],{"kpi":50,"label":269,"unit":221,"aggregate":208,"higherIsBetter":226,"n":270,"nUpTo":266,"median":220,"min":220,"max":220,"byClaimant":271,"vendorOnly":208,"points":272},"Users served",1,{"organization":270,"vendor":266,"regulator":266,"independent":266},[273],{"evidenceId":234,"organization":207,"value":220,"qualifier":222,"claimant":224,"grade":233,"pooled":226},{"low":275,"high":276},540000,3360000,[278,297,310,325,348],{"slug":192,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":283,"patterns":284,"audience":287,"autonomy":288,"adoptionStage":31,"segment":289,"evidenceCount":290,"publicEvidenceCount":290,"organizations":291,"bestGrade":233,"headline":265,"lastVerified":296,"indexable":226},"AI cash flow underwriting for small business loans","SME cash flow underwriting","An underwriting engine that assesses a small business's repayment capacity from live bank transactions, point of sale and payment flows, receivables and accounting data instead of audited accounts, and returns a decision recommendation with the evidence and reasons behind it.",[17],[19,20,21],[285,23,24,286],"prediction-and-scoring","conversational-agent","back-office","supervised-agent","lending",4,[292,293,294,295],"MYbank","National Australia Bank","OakNorth Bank","Sumitomo Mitsui Banking Corporation","2026-09-26",{"slug":193,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":302,"patterns":303,"audience":29,"autonomy":306,"adoptionStage":31,"segment":289,"evidenceCount":307,"publicEvidenceCount":307,"organizations":308,"bestGrade":263,"headline":265,"lastVerified":197,"indexable":226},"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],[21,19],[304,23,24,305],"anomaly-detection","summarization","assist",3,[294,309,295],"PNC Financial Services",{"slug":194,"title":311,"shortTitle":312,"definition":313,"status":9,"industries":314,"functions":317,"patterns":320,"audience":29,"autonomy":30,"adoptionStage":31,"segment":32,"evidenceCount":307,"publicEvidenceCount":307,"organizations":321,"bestGrade":233,"headline":265,"lastVerified":197,"indexable":226},"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.",[17,315,316],"wealth-and-asset-management","capital-markets",[318,319],"sales","knowledge-management",[25,305,26,24],[322,323,324],"Bank of America","Scotiabank","Standard Chartered",{"slug":195,"title":326,"shortTitle":327,"definition":328,"status":9,"industries":329,"functions":331,"patterns":334,"audience":287,"autonomy":288,"adoptionStage":336,"segment":32,"evidenceCount":307,"publicEvidenceCount":307,"organizations":337,"bestGrade":263,"headline":341,"lastVerified":197,"indexable":226},"AI for business onboarding (KYB) and beneficial ownership discovery","Business onboarding and UBO","An AI agent that builds the know your business (KYB) due diligence file for a new or reviewed corporate client, before any account is opened: it collects registry, incorporation and ownership documents, resolves the entity across sources, maps the ownership chain through holding companies, nominees and trusts to the ultimate beneficial owners, screens the entity and its owners, and presents a risk scored case for a compliance analyst to decide.",[17,330,316],"payments",[332,333],"onboarding-and-kyc","financial-crime-compliance",[23,24,335,305],"classification-and-routing","emerging",[338,339,340],"BNY","Incore Bank","M-DAQ Global",{"kpi":342,"label":343,"unit":344,"n":270,"nUpTo":266,"kind":345,"value":346,"qualifier":347,"claimant":224,"organization":338,"vendorReported":208},"automation-rate","Automation rate","percent","reported",25,"exact",{"slug":196,"title":349,"shortTitle":350,"definition":351,"status":9,"industries":352,"functions":354,"patterns":355,"audience":29,"autonomy":30,"adoptionStage":31,"segment":20,"evidenceCount":356,"publicEvidenceCount":356,"organizations":357,"bestGrade":233,"headline":366,"lastVerified":296,"indexable":226},"AI copilot for underwriting risk assessment","Underwriting risk assessment copilot","A copilot that assembles everything relevant to a risk (the submission, loss history, internal guidelines, third party data and public information), highlights exposures and gaps against the insurer's underwriting guidelines and drafts the underwriting narrative or referral note, while the underwriter makes and signs every decision.",[353],"insurance",[20,21],[25,305,26,24],8,[358,359,360,361,362,363,364,365],"Accelerant Holdings","American International Group","Arch Capital Group","Bowhead Specialty","Generali Global Corporate & Commercial","Hiscox","Skyward Specialty Insurance Group","Zurich North America",{"kpi":48,"label":367,"unit":344,"n":270,"nUpTo":266,"kind":345,"value":368,"qualifier":347,"claimant":369,"organization":362,"vendorReported":226},"Cycle time reduction",50,"vendor",{"indexable":226,"reasons":371},[],[373,378,383,391,399,405,412,419,424,429,435,441,448,455,461,466,473,479,485,491,497,503,509,514,519,523,529,534,540,548,554,560,566,571],{"id":150,"label":374,"issuer":157,"region":158,"url":375,"description":376,"useCases":377,"indexable":226},"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":152,"label":379,"issuer":157,"region":158,"url":380,"description":381,"useCases":382,"indexable":226},"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":384,"label":385,"issuer":386,"region":387,"url":388,"description":389,"useCases":390,"indexable":226},"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":392,"label":393,"issuer":394,"region":395,"url":396,"description":397,"useCases":398,"indexable":226},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":400,"label":401,"issuer":157,"region":158,"url":402,"description":403,"useCases":404,"indexable":226},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":406,"label":407,"issuer":408,"region":158,"url":409,"description":410,"useCases":411,"indexable":226},"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":413,"label":414,"issuer":415,"region":158,"url":416,"description":417,"useCases":418,"indexable":226},"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":420,"label":421,"issuer":168,"region":169,"url":170,"description":422,"useCases":423,"indexable":226},"mas-ai-risk-management","MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":153,"label":425,"issuer":426,"region":169,"url":427,"description":428,"useCases":346,"indexable":226},"APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":430,"label":431,"issuer":432,"region":387,"url":433,"description":434,"useCases":64,"indexable":226},"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":436,"label":437,"issuer":438,"region":395,"url":439,"description":440,"useCases":64,"indexable":226},"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":442,"label":443,"issuer":444,"region":158,"url":445,"description":446,"useCases":447,"indexable":226},"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":449,"label":450,"issuer":451,"region":387,"url":452,"description":453,"useCases":454,"indexable":226},"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":456,"label":457,"issuer":157,"region":158,"url":458,"description":459,"useCases":460,"indexable":226},"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":462,"label":463,"issuer":157,"region":158,"url":464,"description":465,"useCases":460,"indexable":226},"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":467,"label":468,"issuer":469,"region":395,"url":470,"description":471,"useCases":472,"indexable":226},"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":474,"label":475,"issuer":157,"region":158,"url":476,"description":477,"useCases":478,"indexable":226},"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":480,"label":481,"issuer":482,"region":395,"url":483,"description":484,"useCases":478,"indexable":226},"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":486,"label":487,"issuer":488,"region":387,"url":489,"description":490,"useCases":478,"indexable":226},"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":492,"label":493,"issuer":157,"region":158,"url":494,"description":495,"useCases":496,"indexable":226},"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":498,"label":499,"issuer":500,"region":395,"url":501,"description":502,"useCases":496,"indexable":226},"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":504,"label":505,"issuer":168,"region":169,"url":506,"description":507,"useCases":508,"indexable":226},"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":510,"label":511,"issuer":157,"region":158,"url":512,"description":513,"useCases":508,"indexable":226},"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":515,"label":516,"issuer":157,"region":158,"url":517,"description":518,"useCases":508,"indexable":226},"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":151,"label":162,"issuer":163,"region":158,"url":520,"description":521,"useCases":522,"indexable":226},"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":524,"label":525,"issuer":526,"region":395,"url":527,"description":528,"useCases":356,"indexable":226},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":530,"label":531,"issuer":157,"region":158,"url":532,"description":533,"useCases":356,"indexable":226},"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":535,"label":536,"issuer":157,"region":158,"url":537,"description":538,"useCases":539,"indexable":226},"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":541,"label":542,"issuer":543,"region":544,"url":545,"description":546,"useCases":547,"indexable":226},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":549,"label":550,"issuer":551,"region":158,"url":552,"description":553,"useCases":290,"indexable":226},"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":555,"label":556,"issuer":557,"region":158,"url":558,"description":559,"useCases":290,"indexable":226},"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":561,"label":562,"issuer":563,"region":169,"url":564,"description":565,"useCases":307,"indexable":226},"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":567,"label":568,"issuer":157,"region":158,"url":569,"description":570,"useCases":307,"indexable":226},"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":572,"label":573,"issuer":574,"region":395,"url":575,"description":576,"useCases":307,"indexable":226},"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.",1790598294478]