[{"data":1,"prerenderedAt":613},["ShallowReactive",2],{"uc-sme-cash-flow-underwriting":3,"uc-regulations":409},{"useCase":4,"evidence":207,"blitsAiDeployments":311,"benchmarks":312,"indicative":319,"related":322,"indexability":407,"includeUnpublished":213},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":21,"channels":26,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":44,"indicativeValue":49,"macroEstimates":85,"feasibility":86,"implementation":100,"risk":146,"blitsAi":183,"faq":185,"related":195,"datePublished":201,"dateModified":201,"lastVerified":202,"changelog":203,"slug":206},"AI cash flow underwriting for small business loans","SME cash flow underwriting","AI cash flow underwriting for SME lending","Cash flow underwriting decides small business loans from bank and accounting data. MYbank approves in under a second; NAB opened 45% of SME loan accounts this way.","published","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.",[12,13,14],"small business cash flow lending","MSME credit decisioning","SME lending automation",[16],"banking",[18,19,20],"lending-and-credit","underwriting","risk-management",[22,23,24,25],"prediction-and-scoring","document-processing","agentic-workflow","conversational-agent",[27,28,29,30],"api","mobile-app","web-chat","internal-tools","back-office","supervised-agent","early-adopters","lending","Many small businesses struggle to get bank credit. Traditional underwriting asks for audited\nfinancial statements, tax returns and collateral, which many micro and small firms do not have, and\nrelies on manual spreading and credit memos for loans that are small relative to the effort. The\nresult is a high cost to serve, slow decisions and owners who turn to more expensive finance or go\nwithout. At MYbank, which lends on its own data and models, over 72 percent of the 3 million\nborrowers it added in 2023 had never had a business loan from a bank before.\n\nMost of these businesses do have a detailed financial record: their bank account, card acquiring,\nmarketplace or accounting software. Cash flow underwriting reads that record directly. It can decide\nsimple, small facilities in minutes and give credit officers a much better picture for larger ones,\nbut only if the data connections, the model and the credit policy are designed together.",[],"1. **Connect the data.** With the owner's consent the engine pulls bank transactions, acquiring or\n   marketplace sales, and accounting data through APIs, or reads uploaded statements with document\n   AI.\n2. **Build the cash flow picture.** Transactions are categorised into revenue, payroll, suppliers,\n   taxes and existing debt service; seasonality, volatility and concentration are measured.\n3. **Score and size.** A model estimates default risk, and policy rules translate free cash flow\n   into an affordable limit and tenor.\n4. **Decide or refer.** Small, clean applications within policy are approved automatically; the\n   rest go to a credit officer with a prepared summary, the key ratios and the reasons.\n5. **Keep the owner informed.** Status updates and requests for missing documents go out on the\n   owner's channel of choice.\n6. **Keep watching.** The same data feeds monitor the borrower after the loan is drawn.",[39,40,41,42,43],"speed","inclusion-and-access","cost-to-serve","revenue-growth","risk-reduction",[45,46,47,48],"automation-rate","processing-time-reduction","cycle-time-days","users-served",{"referenceOrg":50,"inputs":51,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A bank receiving 20,000 small business loan applications a year",[52,58,66,73],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"applications","Small business loan applications per year",20000,"applications per year","The reference bank.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64,"sourceUrl":65},"automatedShare","Share of applications decided through the automated cash flow path",0.2,0.33,"fraction of applications","iTnews reported that NAB's QuickBiz platform had been held up for deciding one in every three small business loans, and a NAB executive later said 45 percent of small business lending accounts were opened through it. Not every application on such a platform is decided without an underwriter, so the high bound stays at one in three. The low bound of 0.2 is an editorial floor, not a reported figure: replace it with your own measured automation rate.","https://www.itnews.com.au/news/nab-watches-cloud-based-quickbiz-lending-process-gain-traction-530744",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"hoursSaved","Underwriter hours saved per application on that path",3,6,"hours per application","Editorial assumption for spreading, analysis and memo writing on a small facility. Replace with your own time study.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"hourlyCost","Fully loaded cost of an underwriter hour",50,80,"USD per hour","Editorial assumption. Replace with your own cost.","applications * automatedShare * hoursSaved * hourlyCost","USD","per year","Underwriting effort released","Counts underwriting effort only. It leaves out additional lending volume from faster decisions, changes in credit losses, data access fees, and the cost of building and validating the model.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"high","Data connections, categorisation quality and credit policy design carry most of the effort. Decisions are regulated in many markets, the model needs validation, and the automated path must be tightly bounded by product, size and risk grade.",[90,91,92,93],"Historical small business applications with repayment outcomes","Consent based access to bank transaction, acquiring or accounting data","A written credit policy for the automated path (eligible products, maximum amounts, exclusions)","Reason codes that credit and compliance have approved",[95,96,97,98,99],"Open banking or account aggregation provider","Accounting software connectors (for example Xero, MYOB, QuickBooks)","Acquiring, marketplace or point of sale data where the bank has it","Loan origination system and decision engine","Credit bureau and business registry",{"steps":101,"guardrails":120,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":133},[102,105,108,111,114,117],{"title":103,"detail":104},"Define the automated lane","Write down which products, amounts, industries and risk grades may be decided without a person. Start narrow, such as unsecured facilities up to a set limit for existing customers.",{"title":106,"detail":107},"Get the categorisation right","Test transaction categorisation on a few hundred real businesses per sector. Revenue, owner drawings and transfers between own accounts are where errors hide.",{"title":109,"detail":110},"Backtest and validate","Score past applicants and compare with outcomes, then take the model through independent validation and add it to the model inventory before any live decision.",{"title":112,"detail":113},"Prepare the referral pack","For cases outside the lane, generate a summary with cash flow charts, key ratios and the reasons for referral, so credit officers start from analysis instead of raw statements.",{"title":115,"detail":116},"Launch with existing customers","The bank already holds their transaction history, which removes the consent step and gives a cleaner first measurement of approval, speed and loss rates.",{"title":118,"detail":119},"Extend to connected new customers","Add accounting and acquiring connections for new to bank businesses once the lane performs as expected.",[121,122,123,124,125],"Automatic approvals only inside the documented lane; everything else goes to a credit officer","Specific, recorded reasons for every decline, reduced limit or referral","Consent recorded for every external data source used in a decision","Deterministic affordability and exposure limits outside the model","Monitoring that compares automated approvals with manually underwritten ones","Credit officers own every decision outside the automated lane, every appeal and every exception to policy. Credit risk reviews the performance of automated approvals each month and can close the lane for a segment at any time.",[128,129,130,131,132],"Share of applications decided in the automated lane","Median time from application to decision, by lane","Default and arrears rates of automated versus manual approvals","Consent and data connection completion rate","Decline reasons distribution and appeal overturn rate",[134,137,140,143],{"title":135,"detail":136},"Misread cash flow","Transfers between the owner's own accounts or a one off asset sale look like revenue. Categorisation must be tested per sector and suspicious patterns flagged.",{"title":138,"detail":139},"A lane that creeps wider","Pressure to grow volume pushes larger or riskier loans into automatic decisions. Change the lane only through credit committee with fresh backtests.",{"title":141,"detail":142},"Declines without a real reason","A small business told only that it \"did not meet criteria\" cannot act and may complain. Give specific reasons tied to the data.",{"title":144,"detail":145},"Blind spots after drawdown","Underwriting uses live data but monitoring still waits for annual accounts. Connect the same feeds to early warning.",{"euAiAct":147,"regulations":150,"guidance":158,"controls":176,"incidents":182},{"tier":148,"basis":149},"context-dependent","Annex III point 5(b) makes AI systems that evaluate the creditworthiness of natural persons or establish their credit score high risk. Scoring a company is outside that point, but a sole trader is a natural person, and a model that also assesses the personal credit of owners, partners or guarantors evaluates natural persons. The tier therefore depends on who the borrower is and whose creditworthiness the model assesses.",[151,152,153,154,155,156,157],"eu-ai-act","gdpr","eba-loan-origination","us-sr-11-7","mas-ai-risk-management","dora","us-ecoa-reg-b",[159,165,170],{"title":160,"issuer":161,"region":162,"url":163,"note":164},"Guidelines on loan origination and monitoring","European Banking Authority","europe","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Covers creditworthiness assessment for micro and small enterprises and the governance of automated models used in credit decisions.",{"title":166,"issuer":167,"region":162,"url":168,"note":169},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https://artificialintelligenceact.eu/annex/3/","Point 5(b) on creditworthiness of natural persons decides whether a given small business model is high risk.",{"title":171,"issuer":172,"region":173,"url":174,"note":175},"MAS Guidelines for Artificial Intelligence 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 financial institutions in Singapore on AI inventories, risk materiality, fairness, explainability and human oversight, which apply to credit models.",[177,178,179,180,181],"Model inventory entry, independent validation and an approved scope for automatic decisions","Written automated lane policy approved by credit committee","Consent and data lineage records for every decision","Reason code library reviewed by compliance","Monthly performance review of automated approvals against manual ones",[],{"howToBuild":184},"The scoring model and the decision engine stay in the bank's credit stack. Blits.ai carries the\napplication and the conversation around it: an **AI agent** on web chat, WhatsApp or, through the API\nchannel, the bank's own app guides the owner through eligibility, asks for consent to connect accounts, collects statements\nthrough **receive attachment** blocks and answers product questions from a **knowledge base** of\napproved terms. **Custom functions** call the bank's aggregation, origination and decision APIs,\nand the regulated steps (consent, declarations, acceptance) run as deterministic **flows**.\n\nFor referred cases an **agentic workflow** assembles the credit officer's pack from the decision\nengine output and the documents, and waits for **human in the loop approval** before anything is\nsent to the customer. **SQL knowledge bases** let credit staff ask questions about the pipeline in\nplain language, **PII masking** protects owner data at the gateway, and **test suites** replay\napplications on every change. EU and UAE hosting supports data residency.",[186,189,192],{"question":187,"answer":188},"How fast can a small business loan be decided with cash flow data?","For small, simple facilities, very fast. MYbank describes a loan that takes under three minutes to apply for and under one second to approve with no human involved, and a NAB executive said QuickBiz credit decisions often came the same day as the conversation with the banker. Larger facilities still need a credit officer, but with a prepared analysis instead of raw statements.",{"question":190,"answer":191},"Does cash flow underwriting replace financial statements?","For micro and small loans it often can, because transaction data shows revenue, costs and debt service more currently than annual accounts. For larger facilities it complements statements and supports continuous monitoring after drawdown, as OakNorth Bank does by comparing each borrower with peers in the same sector and location.",{"question":193,"answer":194},"Is SME credit scoring high risk under the EU AI Act?","Annex III point 5(b) covers creditworthiness of natural persons, so scoring a company is not listed. Sole traders are natural persons, and models that assess owners or guarantors personally also fall inside it, so classify each product by whose creditworthiness is assessed.",[196,197,198,199,200],"alternative-data-credit-scoring","credit-memo-drafting-agent","credit-early-warning-monitoring","adverse-action-explanations","treasury-cash-flow-forecasting","2026-09-27","2026-09-26",[204],{"date":201,"note":205},"First published","sme-cash-flow-underwriting",[208,244,268,286],{"title":209,"useCases":210,"organization":211,"vendors":215,"summary":218,"stage":219,"year":220,"channels":221,"languages":222,"metrics":224,"outcomeDisclosed":233,"sources":234,"verification":239,"grade":241,"id":242,"organizationSlug":243},"MYbank: the 310 model for collateral free SME loans",[206],{"name":212,"anonymized":213,"country":214,"region":173,"industry":16},"MYbank",false,"CN",[216],{"name":212,"role":217},"in-house","MYbank, the Chinese digital bank associated with Ant Group, lends to small and micro businesses with its \"310 model\": a collateral free business loan that takes under three minutes to apply for on a phone, under one second to approve and no human interaction. The bank says AI, including Ant Group's Bailing foundation model and a supply chain knowledge graph, informs its lending decisions. By the end of 2023 it had served over 53 million small and micro businesses, and over 72% of the 3 million new borrowers it added in 2023 had obtained a business loan from a bank for the first time. It also uses satellite imagery to estimate farm output and an AI conversational system to manage credit lines.","scaled",2024,[28,27],[223],"zh",[225],{"kpi":48,"value":226,"unit":227,"qualifier":228,"period":229,"claimant":230,"quote":231,"sourceUrl":232},53000000,"count","at-least","small and micro businesses served, cumulative to the end of 2023","organization","the bank has cumulatively served over 53 million small and micro-sized enterprises (SMEs) as of the end of 2023","https://aijourn.com/leveraging-ai-mybank-enables-financing-services-for-53-million-smes/",true,[235],{"url":232,"title":236,"publisher":237,"date":238},"Leveraging AI, MYbank Enables Financing Services for 53 Million SMEs","MYbank press release via Business Wire, republished by The AI Journal","2024-04-30",{"level":240,"checkedAt":202},"source-verified","B","mybank-310-sme-lending",null,{"title":245,"useCases":246,"organization":247,"vendors":250,"summary":253,"stage":219,"year":254,"channels":255,"languages":256,"metrics":258,"outcomeDisclosed":213,"sources":259,"verification":265,"grade":266,"id":267,"organizationSlug":243},"OakNorth Bank: data driven SME underwriting and continuous borrower monitoring",[206,198],{"name":248,"anonymized":213,"country":249,"region":162,"industry":16},"OakNorth Bank","GB",[251],{"name":252,"role":217},"OakNorth","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.",2020,[30],[257],"en",[],[260],{"url":261,"title":262,"publisher":263,"date":264},"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":240,"checkedAt":202},"C","oaknorth-bank-continuous-credit-monitoring",{"title":269,"useCases":270,"organization":271,"vendors":274,"summary":277,"stage":278,"year":254,"channels":279,"languages":280,"metrics":281,"outcomeDisclosed":213,"sources":282,"verification":284,"grade":266,"id":285,"organizationSlug":243},"SMBC: licensing OakNorth's credit intelligence software for lending and monitoring",[206,198],{"name":272,"anonymized":213,"country":273,"region":173,"industry":16},"Sumitomo Mitsui Banking Corporation","JP",[275],{"name":252,"role":276},"platform","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",[30],[257],[],[283],{"url":261,"title":262,"publisher":263,"date":264},{"level":240,"checkedAt":202},"sumitomo-mitsui-banking-corporation-oaknorth-credit-intelligence",{"title":287,"useCases":288,"organization":289,"vendors":292,"summary":295,"stage":219,"year":296,"channels":297,"languages":298,"metrics":299,"outcomeDisclosed":233,"sources":300,"verification":309,"grade":266,"id":310,"organizationSlug":243},"NAB: QuickBiz automated unsecured small business lending",[206],{"name":290,"anonymized":213,"country":291,"region":173,"industry":16},"National Australia Bank","AU",[293],{"name":294,"role":276},"Amazon Web Services","NAB's QuickBiz platform decides unsecured small business loans and overdrafts online. In 2021 its product page said NAB reviews the applicant's cash flow, credit score and time in business, and that businesses using Xero, MYOB or QuickBooks can link their accounting data. iTnews reported that the platform uses machine learning to make decisions faster. In 2019 the general manager of digital and sales transformation in NAB's business and private bank said that 45 percent of NAB's small business lending accounts were being opened this way, and that credit decisions often came the same day.",2019,[27],[257],[],[301,305],{"url":65,"title":302,"publisher":303,"date":304},"NAB watches cloud-based QuickBiz lending process gain traction","iTnews","2019-09-09",{"url":306,"title":307,"publisher":290,"archivedUrl":308},"https://www.nab.com.au/business/loans-and-finance/business-loans/nab-quickbiz-loan","NAB QuickBiz unsecured business loan","https://web.archive.org/web/20210119020618/https://www.nab.com.au/business/loans-and-finance/business-loans/nab-quickbiz-loan",{"level":240,"checkedAt":202},"nab-quickbiz-automated-sme-lending",0,[313],{"kpi":48,"label":314,"unit":227,"aggregate":213,"higherIsBetter":233,"n":315,"nUpTo":311,"median":226,"min":226,"max":226,"byClaimant":316,"vendorOnly":213,"points":317},"Users served",1,{"organization":315,"vendor":311,"regulator":311,"independent":311},[318],{"evidenceId":242,"organization":212,"value":226,"qualifier":228,"claimant":230,"grade":241,"pooled":233},{"low":320,"high":321},600000,3168000,[323,338,354,366,379],{"slug":196,"title":324,"shortTitle":325,"definition":326,"status":9,"industries":327,"functions":329,"patterns":330,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":331,"publicEvidenceCount":331,"organizations":332,"bestGrade":241,"headline":243,"lastVerified":202,"indexable":233},"AI credit scoring with alternative data for thin file applicants","Alternative data credit scoring","A machine learning credit model that adds consumer permissioned alternative data, such as bank account cash flow, rent, utility and telco payments or ecosystem data, to credit bureau data, so a lender can assess applicants with thin or no credit files and return a decision with specific reasons.",[16,328],"payments",[18,19,20],[22,23,25],5,[333,334,335,336,337],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network",{"slug":197,"title":339,"shortTitle":340,"definition":341,"status":9,"industries":342,"functions":343,"patterns":344,"audience":347,"autonomy":348,"adoptionStage":33,"segment":349,"evidenceCount":350,"publicEvidenceCount":350,"organizations":351,"bestGrade":241,"headline":243,"lastVerified":201,"indexable":233},"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.",[16],[18,19,20],[23,24,345,346],"rag-knowledge-assistant","content-generation","employee-facing","copilot","specialized-businesses",2,[352,353],"Banestes","DBS Bank",{"slug":198,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":359,"patterns":360,"audience":347,"autonomy":363,"adoptionStage":33,"segment":34,"evidenceCount":69,"publicEvidenceCount":69,"organizations":364,"bestGrade":266,"headline":243,"lastVerified":201,"indexable":233},"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.",[16],[20,18],[361,23,24,362],"anomaly-detection","summarization","assist",[248,365,272],"PNC Financial Services",{"slug":199,"title":367,"shortTitle":368,"definition":369,"status":9,"industries":370,"functions":371,"patterns":374,"audience":347,"autonomy":348,"adoptionStage":375,"segment":34,"evidenceCount":350,"publicEvidenceCount":350,"organizations":376,"bestGrade":241,"headline":243,"lastVerified":201,"indexable":233},"AI drafted explanations for credit declines and adverse actions","Adverse action explanations","An assistant that turns the reason codes of a credit model into an accurate, specific and readable explanation of a decline, reduced limit or repricing for the customer, and a matching internal rationale for the file, without adding any reason the model did not produce.",[16,328],[18,372,373],"regulatory-compliance","customer-service",[346,345,25],"emerging",[377,378],"Discover Financial Services","Wells Fargo",{"slug":200,"title":380,"shortTitle":381,"definition":382,"status":9,"industries":383,"functions":388,"patterns":392,"audience":347,"autonomy":363,"adoptionStage":33,"segment":349,"evidenceCount":331,"publicEvidenceCount":331,"organizations":394,"bestGrade":241,"headline":400,"lastVerified":201,"indexable":233},"AI cash flow forecasting for corporate treasury","Treasury cash forecasting","Machine learning and conversational analytics, offered by some banks inside their cash management platforms, that categorise a company's cash flows, forecast positions across accounts and currencies, and answer treasurers' questions in plain language, so the treasury team decides on funding and idle balances with better information and less spreadsheet work.",[16,384,385,386,387],"cross-industry","logistics-and-transportation","retail-and-ecommerce","manufacturing",[389,390,391],"treasury","finance-and-accounting","analytics-and-reporting",[22,393,25,24],"classification-and-routing",[395,396,397,398,399],"Amtrak","Bank of America","Domino's Pizza","JPMorgan Chase","Prysmian",{"kpi":401,"label":402,"unit":403,"n":350,"nUpTo":315,"kind":404,"value":405,"qualifier":406,"claimant":230,"organization":398,"vendorReported":213},"productivity-gain","Productivity gain","percent","reported",90,"approximately",{"indexable":233,"reasons":408},[],[410,415,420,428,436,441,448,455,459,466,473,478,485,492,498,503,510,516,522,528,534,540,546,551,556,560,566,571,576,583,590,596,602,607],{"id":151,"label":411,"issuer":167,"region":162,"url":412,"description":413,"useCases":414,"indexable":233},"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":416,"issuer":167,"region":162,"url":417,"description":418,"useCases":419,"indexable":233},"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":421,"label":422,"issuer":423,"region":424,"url":425,"description":426,"useCases":427,"indexable":233},"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":429,"label":430,"issuer":431,"region":432,"url":433,"description":434,"useCases":435,"indexable":233},"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":156,"label":437,"issuer":167,"region":162,"url":438,"description":439,"useCases":440,"indexable":233},"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":442,"label":443,"issuer":444,"region":162,"url":445,"description":446,"useCases":447,"indexable":233},"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":449,"label":450,"issuer":451,"region":162,"url":452,"description":453,"useCases":454,"indexable":233},"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":155,"label":456,"issuer":172,"region":173,"url":174,"description":457,"useCases":458,"indexable":233},"MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":460,"label":461,"issuer":462,"region":173,"url":463,"description":464,"useCases":465,"indexable":233},"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":467,"label":468,"issuer":469,"region":424,"url":470,"description":471,"useCases":472,"indexable":233},"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":154,"label":474,"issuer":475,"region":432,"url":476,"description":477,"useCases":472,"indexable":233},"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":479,"label":480,"issuer":481,"region":162,"url":482,"description":483,"useCases":484,"indexable":233},"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":486,"label":487,"issuer":488,"region":424,"url":489,"description":490,"useCases":491,"indexable":233},"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":493,"label":494,"issuer":167,"region":162,"url":495,"description":496,"useCases":497,"indexable":233},"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":499,"label":500,"issuer":167,"region":162,"url":501,"description":502,"useCases":497,"indexable":233},"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":504,"label":505,"issuer":506,"region":432,"url":507,"description":508,"useCases":509,"indexable":233},"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":511,"label":512,"issuer":167,"region":162,"url":513,"description":514,"useCases":515,"indexable":233},"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":517,"label":518,"issuer":519,"region":432,"url":520,"description":521,"useCases":515,"indexable":233},"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":523,"label":524,"issuer":525,"region":424,"url":526,"description":527,"useCases":515,"indexable":233},"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":529,"label":530,"issuer":167,"region":162,"url":531,"description":532,"useCases":533,"indexable":233},"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":535,"label":536,"issuer":537,"region":432,"url":538,"description":539,"useCases":533,"indexable":233},"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":541,"label":542,"issuer":172,"region":173,"url":543,"description":544,"useCases":545,"indexable":233},"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":547,"label":548,"issuer":167,"region":162,"url":549,"description":550,"useCases":545,"indexable":233},"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":552,"label":553,"issuer":167,"region":162,"url":554,"description":555,"useCases":545,"indexable":233},"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":153,"label":557,"issuer":161,"region":162,"url":163,"description":558,"useCases":559,"indexable":233},"EBA Guidelines on loan origination and monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":157,"label":561,"issuer":562,"region":432,"url":563,"description":564,"useCases":565,"indexable":233},"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":567,"label":568,"issuer":167,"region":162,"url":569,"description":570,"useCases":565,"indexable":233},"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":572,"label":573,"issuer":167,"region":162,"url":574,"description":575,"useCases":70,"indexable":233},"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":577,"label":578,"issuer":579,"region":580,"url":581,"description":582,"useCases":331,"indexable":233},"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":584,"label":585,"issuer":586,"region":162,"url":587,"description":588,"useCases":589,"indexable":233},"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":591,"label":592,"issuer":593,"region":162,"url":594,"description":595,"useCases":589,"indexable":233},"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":597,"label":598,"issuer":599,"region":173,"url":600,"description":601,"useCases":69,"indexable":233},"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":603,"label":604,"issuer":167,"region":162,"url":605,"description":606,"useCases":69,"indexable":233},"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":608,"label":609,"issuer":610,"region":432,"url":611,"description":612,"useCases":69,"indexable":233},"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.",1790598297603]