[{"data":1,"prerenderedAt":563},["ShallowReactive",2],{"uc-trade-document-examination":3,"uc-regulations":355},{"useCase":4,"evidence":190,"blitsAiDeployments":267,"benchmarks":268,"indicative":269,"related":272,"indexability":353,"includeUnpublished":196},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":26,"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":100,"risk":139,"blitsAi":168,"faq":170,"related":180,"datePublished":185,"dateModified":185,"lastVerified":185,"changelog":186,"slug":189},"AI examination of trade documents under letters of credit and collections","Trade document examination","AI for letter of credit document checking","AI reads letter of credit presentations, cross checks them against the credit, UCP 600 and ISBP, and lists discrepancies. RMB went live with Traydstream in 2022.","published","AI that reads the full document presentation under a letter of credit or collection (bill of lading, commercial invoice, packing list, certificates), extracts and cross checks the data, tests it against the instructions and the ICC rules (for letters of credit, the credit terms, UCP 600 and ISBP), and lists discrepancies by severity with the rule cited, so qualified examiners focus on the genuine exceptions.",[12,13,14,15],"letter of credit document checking","automated LC examination","trade document discrepancy checking","documentary credit automation",[17],"banking",[19,20],"operations","regulatory-compliance",[22,23,24,25],"document-processing","classification-and-routing","agentic-workflow","summarization",[27,28],"internal-tools","api","back-office","supervised-agent","early-adopters","specialized-businesses","Documentary trade still runs on paper. For every presentation under a letter of credit, a trained\nexaminer reads each document, compares names, dates, quantities, amounts, ports and goods\ndescriptions across them and against the credit, and applies a large body of international\npractice to decide whether the presentation complies. A missed discrepancy can leave the bank\npaying against documents its client may refuse to reimburse; an unnecessary one delays the\nclient's money.\n\nRMB's head of trade describes the checking of numerous unstructured trade documents as manual\nand extremely time consuming, and Standard Bank Group's head of trade presents automation as a way\nto minimise repetitive tasks. Microsoft notes that traditional OCR and template based systems can\nstruggle when layouts change or data is missing. Modern document AI plus a rules engine can do the extraction and the mechanical\ncross checks. In our view the main gain is examiner time freed for the genuinely ambiguous cases,\nas long as the contractual judgement on those stays with a qualified examiner.",[35],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"Microsoft, citing ICC United Kingdom, states that an average international trade shipment can involve up to 50 separate documents exchanged between as many as 30 different stakeholders.","Reimagining trade finance with AI: A collaborative proof of concept from Microsoft, ANZ, HSBC, and Lloyds","https://www.microsoft.com/en-us/microsoft-cloud/blog/financial-services/2026/04/20/reimagining-trade-finance-with-ai-a-collaborative-proof-of-concept-from-microsoft-anz-hsbc-and-lloyds/",2026,"1. **Ingest the presentation.** Scanned and digital documents are classified by type and read with\n   OCR and document AI, whatever their layout.\n2. **Extract and normalise.** Parties, amounts, currencies, dates, ports, goods descriptions,\n   marks and quantities are extracted and normalised.\n3. **Cross check.** Data is compared across documents and against the credit terms (the MT700\n   fields), for example amount and currency, shipment dates, and consistency of goods descriptions.\n4. **Apply the rules.** A rules engine mapped to UCP 600 and ISBP tests each finding, and a\n   language model helps with free text comparisons such as whether two goods descriptions conflict.\n5. **Report discrepancies.** Findings are listed by severity with the rule or credit clause cited;\n   clean presentations go to a lighter review, exceptions to a qualified examiner who decides.",[42,43,44,45],"speed","employee-productivity","risk-reduction","cost-to-serve",[47,48,49,50,51],"processing-time-reduction","handling-time-reduction","accuracy","automation-rate","error-reduction",{"referenceOrg":53,"inputs":54,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"A bank examining 20,000 documentary credit presentations a year",[55,61,68,75],{"key":56,"label":57,"low":58,"high":58,"unit":59,"note":60},"presentations","Presentations examined per year",20000,"presentations per year","The reference bank.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"hoursPerPresentation","Examiner hours per presentation",1.5,3,"hours per presentation","Editorial assumption, replace with your own time study. Complex presentations take much longer.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"reduction","Share of examiner time saved",0.3,0.5,"fraction of time","Editorial assumption, replace with your own pilot results; banks on this page have not published measured figures.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"hourlyCost","Loaded cost of an examiner hour",60,90,"USD per hour","Editorial assumption, replace with your own loaded cost.","presentations * hoursPerPresentation * reduction * hourlyCost","USD","per year","Examiner time released, valued at loaded cost","Values examiner time only. It leaves out the cost of the platform and integration, faster payment for clients, fewer missed discrepancies and the value of scaling without hiring scarce specialists.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":95},"high","Document variety and quality are the main difficulty, followed by encoding UCP and ISBP practice as testable rules and integrating with the trade processing system and SWIFT messages.",[92,93,94],"A library of past presentations with the examiners' decisions, for testing","The bank's discrepancy taxonomy and severity rules","Current credit terms from the trade system in structured form",[96,97,98,99],"Trade finance processing system","SWIFT messaging (MT700 and related)","Document scanning and management","Trade screening, so compliance checks run on the same extracted data",{"steps":101,"guardrails":117,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[102,105,108,111,114],{"title":103,"detail":104},"Build a gold standard set","Collect past presentations with the examiners' final findings, including disputed ones, to measure extraction accuracy and discrepancy recall before go live.",{"title":106,"detail":107},"Separate mechanical from judgement checks","Automate the mechanical checks (amounts, dates, names, consistency) first and route judgement calls, such as whether a variation is a discrepancy, to examiners with the evidence.",{"title":109,"detail":110},"Version the rules","Keep every rule mapped to its UCP, ISBP or credit clause source, versioned, so a past decision can be reproduced with the rules in force at the time.",{"title":112,"detail":113},"Run in shadow mode","Let the system check live presentations in parallel with examiners for a period and compare findings before changing the workflow.",{"title":115,"detail":116},"Share the extraction with compliance","Feed the extracted data to trade screening so compliance and examination work from one version of the facts.",[118,119,120,121],"A qualified examiner decides on every discrepancy and signs off every refusal notice","Every finding cites the document, field and rule or credit clause behind it","Rule versions and model versions are logged per presentation","Low confidence extraction is shown as such and routed to manual review","Examiners review every exception and decide on ambiguous discrepancies. Clean presentations get a lighter human review until error rates are proven low, and a sample of automatically cleared presentations is re examined every month.",[124,125,126,127,128],"Examination time per presentation, clean and with discrepancies","Discrepancy recall and precision against examiner findings","Extraction accuracy per field and document type","Share of presentations cleared with lighter review","Refusals later disputed or overturned",[130,133,136],{"title":131,"detail":132},"Missed discrepancy on a clean looking presentation","The system misses a subtle inconsistency and the presentation is waved through. Sample cleared presentations and track recall on the gold standard set.",{"title":134,"detail":135},"Discrepancy noise","Too many trivial findings and examiners start ignoring them. Tune severity and suppress findings that practice treats as non discrepant.",{"title":137,"detail":138},"Poor scans","Low quality images break extraction. Detect image quality and route poor scans to manual review.",{"euAiAct":140,"regulations":143,"guidance":149,"controls":162,"incidents":167},{"tier":141,"basis":142},"minimal","Checking trade documents for compliance with credit terms is not listed in Annex III and does not decide about natural persons. AI literacy duties under Article 4 apply, and the process falls under the bank's operational resilience and model governance.",[144,145,146,147,148],"eu-ai-act","dora","mas-ai-risk-management","apra-cps-230","iso-42001",[150,156],{"title":151,"issuer":152,"region":153,"url":154,"note":155},"ICC trade finance rules and standards","International Chamber of Commerce","global","https://iccwbo.org/business-solutions/trade-finance/","The ICC publishes UCP 600, ISBP and URC 522 (collections), the rule base the checks are mapped to; the examiner's judgement under them stays with people.",{"title":157,"issuer":158,"region":159,"url":160,"note":161},"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 November 2025 proposing supervisory expectations for all financial institutions on AI inventories, risk materiality, evaluation and testing, human oversight and monitoring.",[163,164,165,166],"Model and rule inventory with owners, versions and validation results","Examiner sign off recorded per presentation","Monthly sampling of automatically cleared presentations","Retention of documents, findings and rule versions for the statutory period",[],{"howToBuild":169},"On Blits.ai this is an **agentic workflow** that the trade system triggers through an API token.\nA **custom function** fetches the presentation and the text from the bank's scanning and OCR\nservice through a REST call, an agent extracts the fields with **structured output**, and the\ndeterministic cross checks run as **custom functions** in the isolated code sandbox. A **knowledge base** with hybrid\nretrieval holds the bank's examination guidelines and discrepancy examples, which the agent uses\nfor free text comparisons and to cite the rule behind each finding.\n\nFindings go back to the examiner through the trade system via REST calls, and that action can be\nset to require **human in the loop confirmation** (approve and reject controls); the examiner\nstill decides every discrepancy. The per run **audit trail** keeps inputs, outputs and tool calls,\n**test suites** can replay the gold standard set against the workflow on each change, and\n**monitors** can run scheduled checks with known inputs against the extraction agent to catch drift. The platform is model agnostic, so each agent\ncan use a different model, and data can stay in the EU or UAE region.",[171,174,177],{"question":172,"answer":173},"Which banks use AI to check trade documents?","RMB (Rand Merchant Bank) announced in September 2022 that it had gone live on Traydstream's AI enabled trade finance platform, which automates trade document checking, and Stanbic Bank Uganda (Standard Bank Group) signed an agreement in 2021 to implement the same platform after months of trade document processing on it. On the corporate side, ANZ, HSBC and Lloyds built a proof of concept with Microsoft in which an AI agent in the company's ERP cross checks a letter of credit against invoice and shipping data before sending structured data to the bank.",{"question":175,"answer":176},"Does AI decide whether a presentation complies?","It should not decide ambiguous cases. It extracts, cross checks and lists discrepancies with the rule cited; a qualified examiner decides and signs off, because the bank stays responsible for honouring or refusing the presentation whatever tool it uses.",{"question":178,"answer":179},"How fast is automated checking?","The banks on this page have not published measured figures. The RMB and Traydstream announcements speak of faster processing and improved turnaround times without numbers, so measure examination time on your own presentations in shadow mode.",[181,182,183,184],"trade-finance-crime-screening","intelligent-document-processing","correspondence-triage-and-routing","payment-investigations-and-exceptions","2026-09-27",[187],{"date":185,"note":188},"First published","trade-document-examination",[191,226,246],{"title":192,"useCases":193,"organization":194,"vendors":199,"summary":203,"stage":204,"year":205,"channels":206,"languages":207,"metrics":209,"outcomeDisclosed":196,"sources":210,"verification":221,"grade":223,"id":224,"organizationSlug":225},"RMB: AI powered trade document checking on the Traydstream platform",[189],{"name":195,"anonymized":196,"country":197,"region":198,"industry":17},"Rand Merchant Bank",false,"ZA","africa",[200],{"name":201,"role":202},"Traydstream","platform","RMB (Rand Merchant Bank), which describes itself as a leading African corporate and investment bank, announced that it had gone live on Traydstream's AI enabled trade finance platform. The platform can digitise documents related to letters of credit, collections and open account transactions for automated document checking, clause matching and rules validation with machine learning and OCR; the releases do not say which of these RMB uses it for. RMB's head of trade described the checking of numerous unstructured trade documents as manual and extremely time consuming. No outcome figures are published.","production",2022,[27],[208],"en",[],[211,217],{"url":212,"title":213,"publisher":214,"date":215,"archivedUrl":216},"https://www.rmb.co.za/news/rmb-streamline-and-digitise-its-trade-finance-process","RMB streamline and digitise its trade finance process","RMB","2022-09-20","https://web.archive.org/web/20220920143316/https://www.rmb.co.za/news/rmb-streamline-and-digitise-its-trade-finance-process",{"url":218,"title":219,"publisher":201,"date":220},"https://traydstream.com/news/ai-powered-platform-for-trade","RMB goes live with Traydstream's AI powered platform for trade","2022-09-21",{"level":222,"checkedAt":185},"source-verified","B","rmb-automated-trade-document-checking",null,{"title":227,"useCases":228,"organization":229,"vendors":231,"summary":234,"stage":235,"year":236,"channels":237,"languages":238,"metrics":239,"outcomeDisclosed":196,"sources":240,"verification":243,"grade":244,"id":245,"organizationSlug":225},"ANZ, HSBC and Lloyds with Microsoft: AI agent proof of concept for letter of credit data",[189,181],{"name":230,"anonymized":196,"region":153,"industry":17},"ANZ, HSBC and Lloyds Banking Group",[232],{"name":233,"role":202},"Microsoft","Microsoft built a proof of concept with ANZ, HSBC and Lloyds, shown at Sibos 2025, in which an AI agent embedded in a corporate's ERP parses an incoming MT700 letter of credit, cross checks it against invoice and shipping data, flags discrepancies such as currency and amount, and sends structured data aligned to the ICC Key Trade Documents and Data Elements to the bank. The same agent answers treasury questions about compliance with the credit terms, and Microsoft says such agents can help flag references to sanctioned entities or ambiguous dual use goods descriptions. It is a demonstration, not a live service.","announced",2025,[27,28],[208],[],[241],{"url":38,"title":37,"publisher":233,"date":242},"2026-04-20",{"level":222,"checkedAt":185},"C","anz-hsbc-lloyds-trade-finance-agent-proof-of-concept",{"title":247,"useCases":248,"organization":249,"vendors":252,"summary":254,"stage":255,"year":256,"channels":257,"languages":258,"metrics":259,"outcomeDisclosed":196,"sources":260,"verification":265,"grade":244,"id":266,"organizationSlug":225},"Standard Bank Group: automated letter of credit document checking at Stanbic Bank Uganda",[189,181],{"name":250,"anonymized":196,"country":251,"region":198,"industry":17},"Stanbic Bank Uganda","UG",[253],{"name":201,"role":202},"Stanbic Bank Uganda, part of Standard Bank Group, signed an agreement to implement Traydstream's platform to digitise the manual vetting of letters of credit for discrepancies, after trade document processing on it over the previous few months. The platform digitises the documents, checks them against trade rules and adds an aggregated compliance module; the group presented it as faster processing with more thorough trade checks and more transparent transactions. No figures are published.","pilot",2021,[27],[208],[],[261],{"url":262,"title":263,"publisher":201,"date":264},"https://traydstream.com/traydstream-and-standard-bank-group-are-pleased-to-announce-achieving-a-major-milestone-with-its-automated-trade-document-checking-solution-roll-out/","Traydstream and Standard Bank Group are pleased to announce achieving a major milestone with its automated trade document checking solution roll out","2021-04-06",{"level":222,"checkedAt":185},"standard-bank-automated-trade-document-checking",0,[],{"low":270,"high":271},540000,2700000,[273,286,316,336],{"slug":181,"title":274,"shortTitle":275,"definition":276,"status":9,"industries":277,"functions":278,"patterns":280,"audience":29,"autonomy":30,"adoptionStage":282,"segment":32,"evidenceCount":65,"publicEvidenceCount":65,"organizations":283,"bestGrade":244,"headline":225,"lastVerified":185,"indexable":285},"AI screening of trade finance transactions for trade based money laundering","Trade crime screening","AI that screens every trade finance transaction for financial crime risk: it checks parties, vessels and ports against sanctions and watchlists, tests goods descriptions against dual use and controlled goods lists, compares unit prices with benchmarks for over or under invoicing, and reads trade documents and messages for laundering red flags, then prepares a case narrative for a human investigator.",[17],[279,19],"financial-crime-compliance",[22,281,23,25],"anomaly-detection","emerging",[230,250,284],"United Bank Limited",true,{"slug":182,"title":287,"shortTitle":288,"definition":289,"status":9,"industries":290,"functions":295,"patterns":298,"audience":29,"autonomy":30,"adoptionStage":300,"evidenceCount":301,"publicEvidenceCount":302,"organizations":303,"bestGrade":223,"headline":309,"lastVerified":185,"indexable":285},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[291,292,293,294],"cross-industry","government","automotive","manufacturing",[19,296,297],"case-management","finance-and-accounting",[22,299,23],"computer-vision","mainstream",7,5,[304,305,306,307,308],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":49,"label":310,"unit":311,"n":312,"nUpTo":267,"kind":313,"value":79,"qualifier":314,"claimant":315,"organization":304,"vendorReported":285},"Accuracy","percent",1,"reported","at-least","vendor",{"slug":183,"title":317,"shortTitle":318,"definition":319,"status":9,"industries":320,"functions":322,"patterns":324,"audience":29,"autonomy":30,"adoptionStage":300,"segment":29,"evidenceCount":325,"publicEvidenceCount":325,"organizations":326,"bestGrade":223,"headline":333,"lastVerified":185,"indexable":285},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[291,17,321,292],"insurance",[19,323,296],"customer-service",[23,22,25],6,[327,328,329,330,331,332],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":49,"label":310,"unit":311,"n":312,"nUpTo":267,"kind":313,"value":334,"qualifier":335,"claimant":315,"organization":331,"vendorReported":285},91,"exact",{"slug":184,"title":337,"shortTitle":338,"definition":339,"status":9,"industries":340,"functions":342,"patterns":343,"audience":29,"autonomy":30,"adoptionStage":282,"segment":29,"evidenceCount":345,"publicEvidenceCount":345,"organizations":346,"bestGrade":223,"headline":349,"lastVerified":185,"indexable":285},"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.",[17,341],"payments",[19,323],[24,22,23,344],"content-generation",2,[347,348],"BNY","JPMorgan Chase",{"kpi":50,"label":350,"unit":311,"n":312,"nUpTo":267,"kind":313,"value":351,"qualifier":314,"claimant":352,"organization":347,"vendorReported":196},"Automation rate",10,"organization",{"indexable":285,"reasons":354},[],[356,363,369,375,383,388,395,402,406,412,419,425,432,439,445,450,457,463,469,475,481,487,492,497,502,509,516,521,526,533,540,546,552,557],{"id":144,"label":357,"issuer":358,"region":359,"url":360,"description":361,"useCases":362,"indexable":285},"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":364,"label":365,"issuer":358,"region":359,"url":366,"description":367,"useCases":368,"indexable":285},"gdpr","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":148,"label":370,"issuer":371,"region":153,"url":372,"description":373,"useCases":374,"indexable":285},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":376,"label":377,"issuer":378,"region":379,"url":380,"description":381,"useCases":382,"indexable":285},"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":145,"label":384,"issuer":358,"region":359,"url":385,"description":386,"useCases":387,"indexable":285},"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":389,"label":390,"issuer":391,"region":359,"url":392,"description":393,"useCases":394,"indexable":285},"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":396,"label":397,"issuer":398,"region":359,"url":399,"description":400,"useCases":401,"indexable":285},"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":146,"label":403,"issuer":158,"region":159,"url":160,"description":404,"useCases":405,"indexable":285},"MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":147,"label":407,"issuer":408,"region":159,"url":409,"description":410,"useCases":411,"indexable":285},"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":413,"label":414,"issuer":415,"region":153,"url":416,"description":417,"useCases":418,"indexable":285},"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":420,"label":421,"issuer":422,"region":379,"url":423,"description":424,"useCases":418,"indexable":285},"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":426,"label":427,"issuer":428,"region":359,"url":429,"description":430,"useCases":431,"indexable":285},"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":433,"label":434,"issuer":435,"region":153,"url":436,"description":437,"useCases":438,"indexable":285},"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":440,"label":441,"issuer":358,"region":359,"url":442,"description":443,"useCases":444,"indexable":285},"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":446,"label":447,"issuer":358,"region":359,"url":448,"description":449,"useCases":444,"indexable":285},"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":451,"label":452,"issuer":453,"region":379,"url":454,"description":455,"useCases":456,"indexable":285},"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":458,"label":459,"issuer":358,"region":359,"url":460,"description":461,"useCases":462,"indexable":285},"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":464,"label":465,"issuer":466,"region":379,"url":467,"description":468,"useCases":462,"indexable":285},"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":470,"label":471,"issuer":472,"region":153,"url":473,"description":474,"useCases":462,"indexable":285},"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":476,"label":477,"issuer":358,"region":359,"url":478,"description":479,"useCases":480,"indexable":285},"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":482,"label":483,"issuer":484,"region":379,"url":485,"description":486,"useCases":480,"indexable":285},"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":488,"label":489,"issuer":158,"region":159,"url":490,"description":491,"useCases":351,"indexable":285},"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":493,"label":494,"issuer":358,"region":359,"url":495,"description":496,"useCases":351,"indexable":285},"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":498,"label":499,"issuer":358,"region":359,"url":500,"description":501,"useCases":351,"indexable":285},"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":503,"label":504,"issuer":505,"region":359,"url":506,"description":507,"useCases":508,"indexable":285},"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":510,"label":511,"issuer":512,"region":379,"url":513,"description":514,"useCases":515,"indexable":285},"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":517,"label":518,"issuer":358,"region":359,"url":519,"description":520,"useCases":515,"indexable":285},"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":522,"label":523,"issuer":358,"region":359,"url":524,"description":525,"useCases":325,"indexable":285},"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":527,"label":528,"issuer":529,"region":530,"url":531,"description":532,"useCases":302,"indexable":285},"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":534,"label":535,"issuer":536,"region":359,"url":537,"description":538,"useCases":539,"indexable":285},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",4,{"id":541,"label":542,"issuer":543,"region":359,"url":544,"description":545,"useCases":539,"indexable":285},"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":547,"label":548,"issuer":549,"region":159,"url":550,"description":551,"useCases":65,"indexable":285},"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":553,"label":554,"issuer":358,"region":359,"url":555,"description":556,"useCases":65,"indexable":285},"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":558,"label":559,"issuer":560,"region":379,"url":561,"description":562,"useCases":65,"indexable":285},"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.",1790598298947]