[{"data":1,"prerenderedAt":560},["ShallowReactive",2],{"uc-credit-early-warning-monitoring":3,"uc-regulations":351},{"useCase":4,"evidence":185,"blitsAiDeployments":253,"benchmarks":254,"indicative":255,"related":258,"indexability":349,"includeUnpublished":191},{"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":35,"valueDrivers":36,"kpis":40,"indicativeValue":44,"macroEstimates":78,"feasibility":79,"implementation":93,"risk":134,"blitsAi":162,"faq":164,"related":174,"datePublished":180,"dateModified":180,"lastVerified":180,"changelog":181,"slug":184},"AI early warning and covenant monitoring for loan portfolios","Credit early warning and covenants","AI credit early warning and covenant monitoring","AI early warning flags weakening borrowers from covenant tests, account flows and peer data. PNC took OakNorth's monitoring software in 2020; SMBC announced a deal.","published","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.",[12,13,14,15],"early warning system for credit","covenant monitoring","watchlist monitoring","credit portfolio monitoring",[17],"banking",[19,20],"risk-management","lending-and-credit",[22,23,24,25],"anomaly-detection","document-processing","agentic-workflow","summarization",[27,28],"internal-tools","email","employee-facing","assist","early-adopters","lending","Many commercial loan books are still monitored on a calendar. Borrowers send financial statements\nand compliance certificates on fixed dates, often months apart, and analysts rekey them, test\ncovenants in spreadsheets and update the watchlist. By the time a breach shows up in audited numbers\nthe problem can be months old, and the options for the bank and the borrower have narrowed.\n\nMeanwhile the signals were visible elsewhere: falling inflows in the operating account, late\nsupplier payments, a lost customer in the news, a sector downturn, a director resigning. They sit\nin different systems and no one has time to watch them for every borrower. The pandemic made the\ngap obvious, when historic financials said little about which businesses would survive.",[],"1. **Build the obligation calendar.** Covenants, reporting duties and test dates are extracted from\n   facility agreements and kept per borrower.\n2. **Ingest and spread.** Incoming financial statements and compliance certificates are read with\n   document AI, spread into the bank's template and covenants recalculated.\n3. **Watch continuous signals.** Account flows, utilisation, days past due, bureau and registry\n   changes, filings, news and sector indicators are monitored for each borrower and compared with\n   peers.\n4. **Score and explain.** Signals are combined into an early warning score; every alert lists the\n   signals that drove it and links to the evidence.\n5. **Propose an action.** The system drafts a short note with suggested next steps, such as a\n   client call, a covenant waiver discussion or a watchlist review, for the relationship manager.\n6. **Record the outcome.** The relationship manager accepts, changes or dismisses the alert, and\n   the reason is kept for the audit trail and to tune thresholds.",[37,38,39],"risk-reduction","employee-productivity","compliance",[41,42,43],"detection-rate-improvement","false-positive-reduction","productivity-gain",{"referenceOrg":45,"inputs":46,"formula":74,"currency":51,"period":75,"resultLabel":76,"caveat":77},"A bank with a USD 5 billion commercial loan book",[47,53,60,67],{"key":48,"label":49,"low":50,"high":50,"unit":51,"note":52},"book","Commercial loan book",5000000000,"USD","The reference bank.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"defaultRate","Annual default rate",0.01,0.02,"fraction of the book per year","Editorial assumption for a commercial book through the cycle. Replace with your own.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"lossGivenDefault","Loss given default",0.3,0.45,"fraction of exposure","Editorial assumption. Replace with your own workout data.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"lossAvoided","Share of default losses avoided through earlier action",0.03,0.08,"fraction of losses","Editorial assumption. No public deployment on this page discloses a measured loss effect, so the range is deliberately small.","book * defaultRate * lossGivenDefault * lossAvoided","per year","Credit losses avoided","Leaves out analyst time saved on spreading and covenant testing, and the cost of data feeds and implementation. Loss avoidance depends on what the bank actually does with an alert; without a disciplined response process the value is close to zero.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":88},"high","Needs data from core banking, payments, loan administration, documents and external sources, a covenant model per facility, and a workflow that relationship managers actually use. Scores that influence credit decisions need model validation.",[83,84,85,86,87],"Facility agreements with covenant definitions, or a structured covenant register","Borrower financial statements and compliance certificates in digital form","Account, payment and utilisation data per borrower","External data such as filings, news, bureau, registry and sector indicators","History of past defaults and watchlist moves to calibrate thresholds",[89,90,91,92],"Loan administration and core banking systems","Document management for borrower reporting","External data providers (news, filings, bureau, registries)","Credit workflow or CRM for alerts and actions",{"steps":94,"guardrails":110,"humanInTheLoop":115,"kpisToInstrument":116,"failureModes":121},[95,98,101,104,107],{"title":96,"detail":97},"Start with covenant testing","Automate extraction of covenants and recalculation from submitted financials first. It saves analyst time immediately and creates the data backbone for everything else.",{"title":99,"detail":100},"Add internal behaviour signals","The bank already owns account inflows, utilisation and payment delays, and they update far more often than borrower financials. Calibrate thresholds on past defaults.",{"title":102,"detail":103},"Layer external signals carefully","Add news, filings and sector data per segment, and measure whether each source improves detection or only adds noise.",{"title":105,"detail":106},"Design the alert to be actionable","One page per alert: what changed, the evidence, peer context and a proposed next step. Make dismissal require a reason.",{"title":108,"detail":109},"Close the loop","Track what happened to every alert and every default that was not alerted, and review thresholds and signals each quarter with credit risk.",[111,112,113,114],"An alert prompts a human review; it never triggers a downgrade, limit cut or exit on its own","Every alert shows the signals behind it and links to the source evidence","Signal logic and thresholds are documented, versioned and in the model inventory","Relationship manager actions and dismissal reasons are recorded","Relationship managers and credit officers decide what to do with every alert. Credit committee owns watchlist changes, rating changes and restructuring decisions. Credit risk reviews alert quality and missed defaults every quarter.",[117,118,119,120],"Share of defaults that had an alert at least 90 days earlier","Alert precision (alerts that led to an action or a watchlist move)","Time from signal to relationship manager review","Analyst hours spent on spreading and covenant testing",[122,125,128,131],{"title":123,"detail":124},"Alert fatigue","Too many weak signals and relationship managers stop reading. Measure precision per signal and cut the ones that do not help.",{"title":126,"detail":127},"Automatic consequences","An alert that silently lowers a limit or rating creates conduct and legal risk. Keep every consequence behind a human decision.",{"title":129,"detail":130},"Spreading errors","A misread line item produces a false covenant breach or hides a real one. Show the source page next to every extracted figure.",{"title":132,"detail":133},"Watching without acting","Early warning only pays if the bank has a response playbook for each alert type. Define it before launch.",{"euAiAct":135,"regulations":138,"guidance":144,"controls":156,"incidents":161},{"tier":136,"basis":137},"context-dependent","Monitoring the credit of companies is not listed in Annex III. Where the same system evaluates the creditworthiness of natural persons, such as sole traders or personal guarantors, it falls under Annex III point 5(b) and is high risk; because that evaluation profiles natural persons, the Article 6(3) exemption does not apply.",[139,140,141,142,143],"eba-loan-origination","us-sr-11-7","eu-ai-act","mas-ai-risk-management","gdpr",[145,151],{"title":146,"issuer":147,"region":148,"url":149,"note":150},"Guidance to banks on non-performing loans","European Central Bank","europe","https://www.bankingsupervision.europa.eu/ecb/pub/pdf/guidance_on_npl.en.pdf","Sets supervisory expectations for early warning indicators, watchlists and early intervention on deteriorating exposures.",{"title":152,"issuer":153,"region":148,"url":154,"note":155},"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","Includes expectations for ongoing credit monitoring and early warning indicators.",[157,158,159,160],"Documented signal catalogue with owners, thresholds and validation evidence","Model inventory entry for any score that influences credit decisions","Audit trail of alerts, reviews, actions and dismissal reasons","Quarterly back testing against defaults and watchlist moves",[],{"howToBuild":163},"On Blits.ai the monitoring runs as **agentic tasks** and **agentic workflows**: scheduled checks\nper borrower call **custom functions** that read account and utilisation data from the bank's\nsystems, **SQL knowledge bases** hold the covenant register, and the **knowledge base** ingests\nfinancial statements and compliance certificates (PDF, XLSX) for extraction. An **agent** with\n**structured output** recalculates covenants from the extracted figures and drafts the alert note\nwith the evidence attached, and the **web search** tool can bring in recent news for a named\nborrower.\n\nAlerts go to the relationship manager by email or in **Microsoft Teams**, and any proposed action\nwaits for **human in the loop approval**. The **run history and audit trail** record every alert\nand decision, **test suites** check extraction and alert logic on known cases, and **monitors**\nrun scheduled health checks on the agents involved. Deployments can stay in the EU or UAE region.",[165,168,171],{"question":166,"answer":167},"How much earlier can AI flag a deteriorating borrower?","It depends on the signals. OakNorth's CIO described audited financials as lagging during the pandemic and pointed to alternative data that updates much more frequently; the bank's own account and payment data also changes far more often than financial statements. OakNorth said its monitoring often reveals a subset of loans where borrowers who are still current might well be heading for difficulty, and that PNC took it to understand the pandemic's impact across its loan portfolios. No bank on this page has published a measured lead time yet.",{"question":169,"answer":170},"Should an early warning alert change a credit limit automatically?","No. Treat an alert as a prompt for review. Automatic limit cuts or downgrades create conduct and legal risk and remove the judgment that restructuring decisions need.",{"question":172,"answer":173},"Where should a bank start?","With covenant extraction and testing from submitted financials, because it saves analyst time at once, then with the bank's own account and payment signals, which it already holds and which update far more often than financial statements.",[175,176,177,178,179],"credit-memo-drafting-agent","loan-restructuring-recommendations","sme-cash-flow-underwriting","collections-and-hardship-agent","client-briefing-and-call-report-copilot","2026-09-27",[182],{"date":180,"note":183},"First published","credit-early-warning-monitoring",[186,216,235],{"title":187,"useCases":188,"organization":189,"vendors":193,"summary":197,"stage":198,"year":199,"channels":200,"languages":201,"metrics":203,"outcomeDisclosed":191,"sources":204,"verification":210,"grade":213,"id":214,"organizationSlug":215},"OakNorth Bank: data driven SME underwriting and continuous borrower monitoring",[177,184],{"name":190,"anonymized":191,"country":192,"region":148,"industry":17},"OakNorth Bank",false,"GB",[194],{"name":195,"role":196},"OakNorth","in-house","OakNorth Bank, a UK lender to small and mid sized businesses, underwrites with human credit officers supported by systems that pull in and analyse public and alternative data, and monitors each borrower continuously against a peer group in the same sector and geography rather than waiting for audited financials every six months. By late 2020 it had lent GBP 4.6 billion to 750 businesses since 2016 and sold the same software to other banks. The published figures describe the lending book, not a measured effect of the AI.","scaled",2020,[27],[202],"en",[],[205],{"url":206,"title":207,"publisher":208,"date":209},"https://www.euromoney.com/article/27sic7y97uvu96j2fuc5c/fintech/smbc-uses-oaknorths-credit-intelligence-software-to-grow-lending/","SMBC uses OakNorth's credit intelligence software to grow lending","Euromoney","2020-11-23",{"level":211,"checkedAt":212},"source-verified","2026-09-26","C","oaknorth-bank-continuous-credit-monitoring",null,{"title":217,"useCases":218,"organization":219,"vendors":223,"summary":226,"stage":227,"year":199,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":191,"sources":231,"verification":233,"grade":213,"id":234,"organizationSlug":215},"PNC: OakNorth credit monitoring to read pandemic impact across the loan book",[184],{"name":220,"anonymized":191,"country":221,"region":222,"industry":17},"PNC Financial Services","US","north-america",[224],{"name":195,"role":225},"platform","In mid 2020 PNC, a large US regional bank, took OakNorth's credit monitoring system to understand the impact of the pandemic across its loan portfolios. The system models each borrower against sector and local peers with frequently updated data, such as reviews, footfall and pricing, instead of relying on lagging audited financials. OakNorth said it delivered the system within a week of the first conversation and that such monitoring often reveals a subset of loans where borrowers who are still current might well be heading for difficulty. PNC published no outcome figures.","production",[27],[202],[],[232],{"url":206,"title":207,"publisher":208,"date":209},{"level":211,"checkedAt":180},"pnc-oaknorth-portfolio-monitoring",{"title":236,"useCases":237,"organization":238,"vendors":242,"summary":244,"stage":245,"year":199,"channels":246,"languages":247,"metrics":248,"outcomeDisclosed":191,"sources":249,"verification":251,"grade":213,"id":252,"organizationSlug":215},"SMBC: licensing OakNorth's credit intelligence software for lending and monitoring",[177,184],{"name":239,"anonymized":191,"country":240,"region":241,"industry":17},"Sumitomo Mitsui Banking Corporation","JP","asia-pacific",[243],{"name":195,"role":225},"In November 2020 Sumitomo Mitsui Banking Corporation licensed the credit underwriting and monitoring software built by OakNorth, a UK SME lender, and invested USD 30 million in OakNorth equity. The software pulls in public and alternative data and compares each borrower with sector and local peers, so lenders can underwrite businesses and watch them continuously rather than waiting for periodic audited financials. SMBC's group CFO said the alliance would bring more sophistication to its corporate lending platforms and that the group was harnessing AI through big data and machine learning across its strategic markets in Southeast Asia, such as Indonesia. No outcome figures were published.","announced",[27],[202],[],[250],{"url":206,"title":207,"publisher":208,"date":209},{"level":211,"checkedAt":212},"sumitomo-mitsui-banking-corporation-oaknorth-credit-intelligence",0,[],{"low":256,"high":257},450000,3600000,[259,277,290,305,334],{"slug":175,"title":260,"shortTitle":261,"definition":262,"status":9,"industries":263,"functions":264,"patterns":266,"audience":29,"autonomy":269,"adoptionStage":31,"segment":270,"evidenceCount":271,"publicEvidenceCount":271,"organizations":272,"bestGrade":275,"headline":215,"lastVerified":180,"indexable":276},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.",[17],[20,265,19],"underwriting",[23,24,267,268],"rag-knowledge-assistant","content-generation","copilot","specialized-businesses",2,[273,274],"Banestes","DBS Bank","B",true,{"slug":176,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":282,"patterns":284,"audience":29,"autonomy":269,"adoptionStage":286,"segment":32,"evidenceCount":271,"publicEvidenceCount":287,"organizations":288,"bestGrade":275,"headline":215,"lastVerified":180,"indexable":276},"AI recommendations for loan restructuring and hardship arrangements","Restructuring recommendations","An assistant that assembles a stressed borrower's position, tests restructuring options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, and recommends the best fit with a written rationale for a person to approve.",[17],[283,20,19],"collections-and-recovery",[24,267,23,285],"recommendation-and-personalization","emerging",1,[289],"Commonwealth Bank of Australia",{"slug":177,"title":291,"shortTitle":292,"definition":293,"status":9,"industries":294,"functions":295,"patterns":296,"audience":299,"autonomy":300,"adoptionStage":31,"segment":32,"evidenceCount":301,"publicEvidenceCount":301,"organizations":302,"bestGrade":275,"headline":215,"lastVerified":212,"indexable":276},"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],[20,265,19],[297,23,24,298],"prediction-and-scoring","conversational-agent","back-office","supervised-agent",4,[303,304,190,239],"MYbank","National Australia Bank",{"slug":178,"title":306,"shortTitle":307,"definition":308,"status":9,"industries":309,"functions":316,"patterns":318,"audience":321,"autonomy":300,"adoptionStage":31,"segment":32,"evidenceCount":322,"publicEvidenceCount":271,"organizations":323,"bestGrade":213,"headline":326,"lastVerified":180,"indexable":276},"AI agent for early collections and hardship support","Collections and hardship agent","A voice and messaging agent that contacts customers in early arrears and answers their inbound calls, takes payments and sets up payment arrangements within preapproved rules, and recognises signs of hardship or vulnerability so those customers go straight to a trained person.",[310,17,311,312,313,314,315],"cross-industry","payments","telecommunications","energy-and-utilities","automotive","professional-services",[283,317],"customer-service",[319,298,24,320],"voice-agent","classification-and-routing","customer-facing",3,[324,325],"Day Knight & Associates","SameDay Auto Finance",{"kpi":327,"label":328,"unit":329,"n":271,"nUpTo":253,"kind":330,"value":331,"qualifier":332,"claimant":333,"organization":325,"vendorReported":276},"cost-reduction","Cost reduction","percent","reported",75,"exact","vendor",{"slug":179,"title":335,"shortTitle":336,"definition":337,"status":9,"industries":338,"functions":341,"patterns":344,"audience":29,"autonomy":269,"adoptionStage":31,"segment":270,"evidenceCount":322,"publicEvidenceCount":322,"organizations":345,"bestGrade":275,"headline":215,"lastVerified":180,"indexable":276},"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.",[17,339,340],"wealth-and-asset-management","capital-markets",[342,343],"sales","knowledge-management",[267,25,268,24],[346,347,348],"Bank of America","Scotiabank","Standard Chartered",{"indexable":276,"reasons":350},[],[352,358,363,371,378,384,391,398,404,411,418,423,430,437,443,448,455,461,467,473,479,485,491,496,501,505,512,517,523,531,537,543,549,554],{"id":141,"label":353,"issuer":354,"region":148,"url":355,"description":356,"useCases":357,"indexable":276},"EU AI Act","European Union","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":143,"label":359,"issuer":354,"region":148,"url":360,"description":361,"useCases":362,"indexable":276},"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":364,"label":365,"issuer":366,"region":367,"url":368,"description":369,"useCases":370,"indexable":276},"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":372,"label":373,"issuer":374,"region":222,"url":375,"description":376,"useCases":377,"indexable":276},"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":379,"label":380,"issuer":354,"region":148,"url":381,"description":382,"useCases":383,"indexable":276},"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":385,"label":386,"issuer":387,"region":148,"url":388,"description":389,"useCases":390,"indexable":276},"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":392,"label":393,"issuer":394,"region":148,"url":395,"description":396,"useCases":397,"indexable":276},"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":142,"label":399,"issuer":400,"region":241,"url":401,"description":402,"useCases":403,"indexable":276},"MAS AI risk management guidelines","Monetary Authority of Singapore","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":405,"label":406,"issuer":407,"region":241,"url":408,"description":409,"useCases":410,"indexable":276},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":412,"label":413,"issuer":414,"region":367,"url":415,"description":416,"useCases":417,"indexable":276},"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":140,"label":419,"issuer":420,"region":222,"url":421,"description":422,"useCases":417,"indexable":276},"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":424,"label":425,"issuer":426,"region":148,"url":427,"description":428,"useCases":429,"indexable":276},"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":431,"label":432,"issuer":433,"region":367,"url":434,"description":435,"useCases":436,"indexable":276},"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":438,"label":439,"issuer":354,"region":148,"url":440,"description":441,"useCases":442,"indexable":276},"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":444,"label":445,"issuer":354,"region":148,"url":446,"description":447,"useCases":442,"indexable":276},"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":449,"label":450,"issuer":451,"region":222,"url":452,"description":453,"useCases":454,"indexable":276},"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":456,"label":457,"issuer":354,"region":148,"url":458,"description":459,"useCases":460,"indexable":276},"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":462,"label":463,"issuer":464,"region":222,"url":465,"description":466,"useCases":460,"indexable":276},"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":468,"label":469,"issuer":470,"region":367,"url":471,"description":472,"useCases":460,"indexable":276},"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":474,"label":475,"issuer":354,"region":148,"url":476,"description":477,"useCases":478,"indexable":276},"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":480,"label":481,"issuer":482,"region":222,"url":483,"description":484,"useCases":478,"indexable":276},"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":486,"label":487,"issuer":400,"region":241,"url":488,"description":489,"useCases":490,"indexable":276},"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":492,"label":493,"issuer":354,"region":148,"url":494,"description":495,"useCases":490,"indexable":276},"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":497,"label":498,"issuer":354,"region":148,"url":499,"description":500,"useCases":490,"indexable":276},"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":139,"label":502,"issuer":153,"region":148,"url":154,"description":503,"useCases":504,"indexable":276},"EBA Guidelines on loan origination and monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":506,"label":507,"issuer":508,"region":222,"url":509,"description":510,"useCases":511,"indexable":276},"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":513,"label":514,"issuer":354,"region":148,"url":515,"description":516,"useCases":511,"indexable":276},"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":518,"label":519,"issuer":354,"region":148,"url":520,"description":521,"useCases":522,"indexable":276},"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":524,"label":525,"issuer":526,"region":527,"url":528,"description":529,"useCases":530,"indexable":276},"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":532,"label":533,"issuer":534,"region":148,"url":535,"description":536,"useCases":301,"indexable":276},"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":538,"label":539,"issuer":540,"region":148,"url":541,"description":542,"useCases":301,"indexable":276},"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":544,"label":545,"issuer":546,"region":241,"url":547,"description":548,"useCases":322,"indexable":276},"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":550,"label":551,"issuer":354,"region":148,"url":552,"description":553,"useCases":322,"indexable":276},"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":555,"label":556,"issuer":557,"region":222,"url":558,"description":559,"useCases":322,"indexable":276},"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.",1790598298803]