[{"data":1,"prerenderedAt":581},["ShallowReactive",2],{"uc-sme-financial-admin-agent":3,"uc-regulations":366},{"useCase":4,"evidence":187,"blitsAiDeployments":255,"benchmarks":256,"indicative":263,"related":266,"indexability":364,"includeUnpublished":193},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":41,"indicativeValue":46,"macroEstimates":87,"feasibility":88,"implementation":100,"risk":138,"blitsAi":163,"faq":165,"related":178,"datePublished":182,"dateModified":182,"lastVerified":182,"changelog":183,"slug":186},"AI agent for SME financial admin and cash management","SME financial admin agent","AI agent for small business cash management","An AI agent drafts payroll, invoices and transfers for owners to approve. Anthropic reports Qonto sends transfers twice as fast, invoices in a third of the time.","published","An AI agent built into a small business account that turns a natural language request or an uploaded document into a prepared financial action, such as a bank transfer, a client invoice, a payroll run or an answer about cash position, and leaves the business owner to review and approve before anything is sent or money moves.",[12,13,14,15],"SME financial admin AI","business banking AI assistant","small business cash management agent","AI bookkeeping and payments assistant",[17,18],"banking","payments",[20,21,22],"treasury","finance-and-accounting","operations",[24,25,26],"agentic-workflow","conversational-agent","document-processing",[28,29],"web-chat","mobile-app","customer-facing","copilot","emerging","front-office","Few small and medium businesses have a finance team behind them. The owner or a single\nbookkeeper does the financial admin between customer work: chasing which invoices went out,\nrunning payroll by hand or through a separate tool, filling in a transfer from an uploaded\nsupplier invoice, and checking the balance across tabs to see whether there is room to pay\neveryone this week. None of it is hard, but it is constant, and it happens in the business\naccount's own web and mobile app, where switching to a separate finance tool costs time the\nowner does not have.\n\nThe AI agent moves into that gap without asking the owner to leave their bank account: it reads\nthe request or the document, prepares the transfer, invoice or payroll batch using the account's\nown data, and gets out of the way of the one decision that still needs a human, which is whether\nto send it.",[],"1. **Understand the request.** The owner asks in plain language (\"pay everyone's April invoices\",\n   \"how did our cash position change this month\") or uploads a document such as a supplier\n   invoice, and the agent works out the intent and the fields it needs.\n2. **Ground it in the account's own data.** The agent pulls the account's live transactions,\n   payees, employee list and prior invoices, so a transfer or invoice draft uses the same\n   beneficiary and reference details the owner would have typed by hand.\n3. **Prepare, never send.** The agent builds the transfer, invoice or payroll batch and puts it in\n   front of the owner for review; nothing executes without an explicit approval.\n4. **Reuse the account's existing controls.** Payment approval rules, per transaction and daily\n   limits, dual admin approval and spend controls that already apply to the account apply the\n   same way when the agent prepares the action.\n5. **Log everything.** Every prepared and approved action is recorded and traceable, so the owner\n   and the bank can both see what happened and why.",[38,39,40],"speed","customer-experience","employee-productivity",[42,43,44,45],"processing-time-reduction","time-saved-per-task","hours-saved","users-served",{"referenceOrg":47,"inputs":48,"formula":82,"currency":83,"period":84,"resultLabel":85,"caveat":86},"An SME banking platform with 300,000 active business customers",[49,54,61,68,75],{"key":50,"label":51,"low":52,"high":52,"unit":50,"note":53},"customers","Active business customers",300000,"The reference organization.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"adminTasksPerCustomer","Transfers, invoices and payroll runs per customer per year",40,80,"tasks per customer per year","Editorial assumption for an SME doing financial admin about weekly to twice weekly. Replace with your own task volume.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"adoptionShare","Share of customers who delegate a task to the agent",0.01,0.1,"fraction of customers","Editorial assumption. Anthropic's Qonto case study says the project started in November 2025 and the first agent reached several thousand beta customers six weeks later, against a base of more than 600,000 businesses, roughly 1 percent or less at that early pilot stage. This range assumes materially higher adoption once the agents are out of beta; replace with your own adoption data.",{"key":69,"label":70,"low":71,"high":72,"unit":73,"note":74},"minutesSavedPerTask","Minutes saved per delegated task",3,6,"minutes per task","Editorial assumption, replace with your own time study. The only task level time baseline in the evidence is Sophie Cornay's \"it's a few minutes\" for a single manual transfer; the speed multipliers in Anthropic's Qonto case study (2x transfers, 3x invoices, 5x payroll) are not stated in minutes, so this range is not derived from them.",{"key":76,"label":77,"low":78,"high":79,"unit":80,"note":81},"hourlyValue","Value of the business owner's or bookkeeper's time",25,60,"USD per hour","Editorial assumption, replace with your own blended rate.","customers * adminTasksPerCustomer * adoptionShare * minutesSavedPerTask / 60 * hourlyValue","USD","per year","Annual value of owner or bookkeeper time saved on financial admin","Time saved only, valued at an assumed hourly rate; it leaves out the bank's cost of running the agent, any change in error rates, and the revenue or retention effect of a faster banking experience.",[],{"complexity":89,"complexityNote":90,"dataPrerequisites":91,"integrations":95},"medium","Answering cash position questions is straightforward with read access to the account's own ledger. The work is in safely preparing actions that move money or send a document: reusing the account's existing approval rules and limits, resolving beneficiaries and payees unambiguously, and building a review step the owner actually reads rather than rubber stamps.",[92,93,94],"The customer's own linked account, transaction and payee data, reachable through APIs","A payroll and invoicing data model with consistent employee and client identifiers","A record of prepared, approved and rejected actions to tune when the agent should ask instead of prepare",[96,97,98,99],"Core ledger and payment rail for transfer execution (for example SEPA or ACH)","Payroll processing module","Invoicing and accounts receivable module","Existing payment approval rules, limits and dual admin approval",{"steps":101,"guardrails":117,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":128},[102,105,108,111,114],{"title":103,"detail":104},"Start with one task, the highest volume and lowest risk","Pick a single admin task with clear, structured inputs, such as transfers to an existing, previously used beneficiary, before extending to invoices or payroll.",{"title":106,"detail":107},"Reuse the account's controls, do not build new ones","Route every prepared action through the same approval rules, per transaction and daily limits and dual admin approval the account already has, so the agent cannot do anything a human user of that account could not already do.",{"title":109,"detail":110},"Make the review step worth reading","Show the beneficiary, amount and reference the agent resolved, not just a summary sentence, and flag when a detail (a new beneficiary, an unusually large amount) is not one the customer has approved before.",{"title":112,"detail":113},"Ground drafts in the account's own data","Pull payees, employees and prior invoices from the account rather than asking the model to infer them, so a draft uses the same beneficiary and reference details the owner would use.",{"title":115,"detail":116},"Log and sample","Keep an immutable, explorable log of every prepared and approved action, and review a sample of approved actions regularly to catch mistakes that a rushed owner approved anyway.",[118,119,120,121],"Every prepared transfer, invoice or payroll run requires the customer's explicit approval before it executes","Per action and per day monetary limits and dual admin approval carry over unchanged from the account's existing controls","Card numbers, national identifiers and credentials are kept out of the model and out of its context","A full, traceable log of what was prepared, by whom it was approved, and when","The business owner (or whoever holds approval rights on the account) reviews and approves every prepared transfer, invoice or payroll batch before it executes; the agent never has standing authority to move money or send a document on its own.",[124,125,126,127],"Share of prepared actions approved without edits, per task type","Time from request to an approved action, per task type","Value and count of transfers, invoices and payroll runs delegated to the agent","Rejected or edited actions, to find where the agent gets details wrong",[129,132,135],{"title":130,"detail":131},"Wrong beneficiary or amount prepared","A misread document or an ambiguous instruction produces a transfer to the wrong payee or for the wrong amount. Mitigate with a review screen that shows the resolved beneficiary name against prior transfers, and a confidence threshold below which the agent asks a clarifying question instead of preparing the action.",{"title":133,"detail":134},"Payroll or invoice run includes a stale record","A payroll batch includes a terminated employee, or an invoice uses an outdated client reference. Mitigate by cross checking against the payroll or CRM system of record before including a line in a prepared batch.",{"title":136,"detail":137},"Approval fatigue","Once customers trust the agent, they stop reading the review screen carefully, which removes the human check the design relies on. Mitigate with extra friction on larger or unusual amounts and periodic re verification, not just a single approval click.",{"euAiAct":139,"regulations":142,"guidance":147,"controls":158,"incidents":162},{"tier":140,"basis":141},"limited","Article 50(1): the business owner must be informed they are interacting with an AI system, unless that is obvious from the context. Preparing routine transfers, invoices and payroll for an existing business account is not an Annex III use. It would become high risk under Annex III point 5(b) if the agent itself assessed a natural person's creditworthiness, for example to decide whether to extend the account an overdraft or credit line.",[143,144,145,146],"eu-ai-act","gdpr","dora","eu-psd2",[148,154],{"title":149,"issuer":150,"region":151,"url":152,"note":153},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":155,"issuer":150,"region":151,"url":156,"note":157},"Directive (EU) 2015/2366 on payment services in the internal market (PSD2)","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Article 97 requires strong customer authentication when a payer initiates an electronic payment, subject to the exemptions in the RTS on strong customer authentication (Delegated Regulation (EU) 2018/389), such as trusted beneficiaries, recurring transactions and secure corporate payment processes. This applies to Qonto's EU deployment; Mercury is a US provider outside PSD2's scope.",[118,159,160,161],"Per action and daily monetary limits and dual admin approval carry over unchanged from the account's existing controls","Card numbers, national identifiers and credentials are kept out of the model's context","Full, traceable log of every prepared and approved action, including who approved it",[],{"howToBuild":164},"On Blits.ai this is an **AI agent** with **custom functions** that call the bank's own transfer,\npayroll and invoicing APIs as scoped REST calls, one action per function, so the agent can only\never prepare the specific actions it has been given. Cash flow and transaction questions are\nanswered through a **SQL knowledge base** registered against the account's own transaction data,\nso answers are grounded in real numbers rather than the model's own arithmetic. Anything that\nmoves money or sends a document runs as an **agentic workflow with human in the loop\nconfirmation**, with the confirmation threshold set to zero so every prepared transfer, invoice\nand payroll run needs the customer's explicit approval before it executes, with a full run\nhistory and audit trail kept for every action.\n\nThe agent is reachable through the tenant's own **web chat and REST or WebSocket API channels**\ninside the bank's app, with **guardrails** and **PII masking** at the gateway keeping card\nnumbers and national identifiers out of the model, and **test suites** running the approval flow\non every change so a change to one action cannot silently loosen what the agent is allowed to\nprepare. The platform is **model agnostic**, so the bank can choose or switch the underlying\nmodel, and **EU data residency** is available for banks that need to keep account data in region.",[166,169,172,175],{"question":167,"answer":168},"What financial admin tasks can an AI agent handle inside a small business bank account?","In current deployments: preparing bank transfers from a request or an uploaded invoice, drafting client invoices, preparing payroll runs as a single batch for approval, categorizing transactions, and answering questions about cash position. Anthropic reports Qonto's agents handling transfers, invoices and payroll; Mercury's Command assistant adds transaction categorization, routing number lookups and moving money between business and personal accounts.",{"question":170,"answer":171},"Can the agent move money without the business owner's approval?","No, in both deployments we found. Qonto's agents prepare a transfer, invoice or payroll batch and wait for the customer's final approval; Mercury states that actions are \"prepared for your review\" and governed by the account's existing permissions, and that nothing happens without approval.",{"question":173,"answer":174},"How is this different from an AI agent that pays on a customer's behalf?","A delegated payment agent (see AI agent for payment initiation within a customer mandate) acts within spending limits the customer set in advance, often for recurring purchases or checkout, and only asks for confirmation past a threshold. The agent described here sits inside the bank account itself and asks for approval on every prepared transfer, invoice or payroll run; it has no standing mandate to act on its own.",{"question":176,"answer":177},"Is this high risk under the EU AI Act?","Usually not. Preparing transfers, invoices and payroll for an existing business account falls under the Article 50 transparency duty, not Annex III. It would become high risk under Annex III point 5(b) if the agent itself assessed the creditworthiness of a natural person, such as a sole trader or a guarantor, which is a separate decision that should stay in the bank's existing credit process.",[179,180,181],"agentic-payment-initiation","treasury-cash-flow-forecasting","corporate-client-servicing-assistant","2026-09-29",[184],{"date":182,"note":185},"First published","sme-financial-admin-agent",[188,214],{"title":189,"useCases":190,"organization":191,"vendors":196,"summary":197,"stage":198,"year":199,"channels":200,"languages":201,"metrics":203,"outcomeDisclosed":193,"sources":204,"verification":209,"grade":211,"id":212,"organizationSlug":213},"Mercury: Command AI financial assistant",[186],{"name":192,"anonymized":193,"country":194,"region":195,"industry":17},"Mercury",false,"US","north-america",[],"Mercury, a US business banking platform, launched Mercury Command, an AI assistant built into its web and mobile banking product that turns a natural language request into finished financial work: categorizing transactions, answering questions about cash position, preparing a payment to a contractor or an invoice to a customer, and moving money between a business and a personal account. Every action is prepared for the customer's review and stays inside the account's existing payment approval rules, daily limits, dual admin approval and spend controls, and card numbers, Social Security numbers and credentials are kept out of the underlying AI model. Mercury states Command is available to every Mercury customer; the announcement discloses no adoption or performance metrics.","production",2026,[28,29],[202],"en",[],[205],{"url":206,"title":207,"publisher":192,"date":208},"https://mercury.com/blog/introducing-mercury-command","Introducing Mercury Command","2026-06-16",{"level":210,"checkedAt":182},"source-verified","B","mercury-command-ai-financial-assistant",null,{"title":215,"useCases":216,"organization":217,"vendors":220,"summary":227,"stage":228,"year":229,"channels":230,"languages":231,"metrics":232,"outcomeDisclosed":248,"sources":249,"verification":252,"grade":253,"id":254,"organizationSlug":213},"Qonto: AI agents for SME financial admin",[186],{"name":218,"anonymized":193,"country":219,"region":151,"industry":17},"Qonto","FR",[221,224],{"name":222,"role":223},"Anthropic","model-provider",{"name":225,"role":226},"Amazon Bedrock","platform","Qonto, a business banking platform for small and medium businesses operating in eight European markets, built a set of AI agents inside its own product that prepare routine financial admin for the account holder: an operator agent fills in bank transfers from an uploaded invoice or a request, drafts client invoices and prepares payroll batches, and an analyst agent answers questions about transactions and cash flow. Every prepared action still needs the customer's final approval before it executes. The agents started development in November 2025, and the first one reached several thousand beta customers six weeks later, running on Claude Opus and Sonnet through Amazon Bedrock.","pilot",2025,[29],[],[233,241,244],{"kpi":42,"value":234,"unit":235,"qualifier":236,"baseline":237,"claimant":238,"quote":239,"sourceUrl":240},2,"multiplier","exact","manual bank transfer entry (beneficiary search, amount entry, reference copying)","vendor","Transfers now get sent 2x faster: the user uploads an invoice and the operator agent does the rest, with no beneficiary search, no amount entry, no reference copying.","https://claude.com/customers/qonto",{"kpi":42,"value":71,"unit":235,"qualifier":236,"baseline":242,"claimant":238,"quote":243,"sourceUrl":240},"manual client invoice creation","Client invoices take 3x less time to create, drafted by the agents and confirmed by the customer before sending.",{"kpi":42,"value":245,"unit":235,"qualifier":236,"baseline":246,"claimant":238,"quote":247,"sourceUrl":240},5,"manual payroll run, slip by slip","Runs payroll 5X quicker: agents prepare every pay slip, the customer approves before anything sends, and a 15-to-20-slip run becomes one drag-and-drop bulk transfer",true,[250],{"url":240,"title":251,"publisher":222},"How Qonto delegates financial admin for small businesses with Claude on Amazon Bedrock",{"level":210,"checkedAt":182},"C","qonto-ai-financial-admin-agents",0,[257],{"kpi":42,"label":258,"unit":235,"aggregate":248,"higherIsBetter":248,"n":259,"nUpTo":255,"median":234,"min":234,"max":234,"byClaimant":260,"vendorOnly":248,"points":261},"Cycle time reduction",1,{"organization":255,"vendor":259,"regulator":255,"independent":255},[262],{"evidenceId":254,"organization":218,"value":234,"qualifier":236,"claimant":238,"grade":253,"pooled":248},{"low":264,"high":265},150000,14400000,[267,289,322,339],{"slug":179,"title":268,"shortTitle":269,"definition":270,"status":9,"industries":271,"functions":273,"patterns":276,"audience":30,"autonomy":277,"adoptionStage":32,"segment":33,"evidenceCount":278,"publicEvidenceCount":279,"organizations":280,"bestGrade":211,"headline":213,"lastVerified":288,"indexable":248},"AI agent for payment initiation within a customer mandate","Agentic payment initiation","An AI agent that initiates and completes payments or purchases on a customer's behalf, within a mandate the customer set in advance (spending caps, allowed merchants or categories, a tokenized credential and rules for when to ask for confirmation), and then confirms and reconciles every transaction it made.",[18,17,272],"retail-and-ecommerce",[274,275,22],"customer-service","sales",[24,25],"supervised-agent",8,7,[281,282,283,284,285,286,287],"DBS Bank","ING","Majid Al Futtaim","PayPal","Banco Santander","Ulta Beauty","Visa","2026-09-27",{"slug":180,"title":290,"shortTitle":291,"definition":292,"status":9,"industries":293,"functions":297,"patterns":299,"audience":302,"autonomy":303,"adoptionStage":304,"segment":305,"evidenceCount":279,"publicEvidenceCount":279,"organizations":306,"bestGrade":211,"headline":314,"lastVerified":288,"indexable":248},"AI cash flow forecasting for corporate treasury","Treasury cash forecasting","Machine learning and conversational analytics, offered by some banks inside their cash management platforms, that categorise a company's cash flows, forecast positions across accounts and currencies, and answer treasurers' questions in plain language, so the treasury team decides on funding and idle balances with better information and less spreadsheet work.",[17,294,295,272,296],"cross-industry","logistics-and-transportation","manufacturing",[20,21,298],"analytics-and-reporting",[300,301,25,24],"prediction-and-scoring","classification-and-routing","employee-facing","assist","early-adopters","specialized-businesses",[307,308,309,310,311,312,313],"Amtrak","Ant International","Bank of America","Capital A Berhad (AirAsia Group)","Domino's Pizza","JPMorgan Chase","Prysmian",{"kpi":315,"label":316,"unit":317,"n":234,"nUpTo":259,"kind":318,"value":319,"qualifier":320,"claimant":321,"organization":312,"vendorReported":193},"productivity-gain","Productivity gain","percent","reported",90,"approximately","organization",{"slug":181,"title":323,"shortTitle":324,"definition":325,"status":9,"industries":326,"functions":327,"patterns":328,"audience":30,"autonomy":277,"adoptionStage":304,"segment":305,"evidenceCount":279,"publicEvidenceCount":331,"organizations":332,"bestGrade":211,"headline":335,"lastVerified":288,"indexable":248},"AI assistant for corporate and commercial client servicing","Corporate client servicing","A conversational assistant inside the corporate banking portal, app and messaging channels that answers finance and treasury teams' servicing questions, such as payment status, balances, cut off times, fees and how to submit an instruction, resolves routine requests end to end and hands the rest to a service specialist who has an AI copilot.",[17,18],[274,22],[25,329,24,330],"rag-knowledge-assistant","summarization",4,[309,333,281,334],"Citi","HSBC",{"kpi":336,"label":337,"unit":317,"n":259,"nUpTo":255,"kind":318,"value":338,"qualifier":236,"claimant":321,"organization":309,"vendorReported":193},"contact-deflection","Contact deflection",16,{"slug":340,"title":341,"shortTitle":342,"definition":343,"status":9,"industries":344,"functions":348,"patterns":349,"audience":351,"autonomy":277,"adoptionStage":304,"segment":351,"evidenceCount":279,"publicEvidenceCount":279,"organizations":352,"bestGrade":211,"headline":360,"lastVerified":288,"indexable":248},"ledger-and-payment-reconciliation","AI for ledger and payment reconciliation","Ledger and payment reconciliation","AI that matches entries across nostro and vostro statements, card and scheme settlement files, the general ledger and suspense accounts, proposes matches and clearing journals, and routes only the genuine breaks to an operator with a plain language explanation.",[17,18,345,294,346,347],"capital-markets","wealth-and-asset-management","government",[21,22],[24,350,26],"anomaly-detection","back-office",[353,354,355,356,357,358,359],"Brex","Capital Area Food Bank","Comrade Trustee Services","Doximity","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme",{"kpi":361,"label":362,"unit":317,"n":234,"nUpTo":255,"kind":318,"value":363,"qualifier":320,"claimant":238,"organization":354,"vendorReported":248},"automation-rate","Automation rate",95,{"indexable":248,"reasons":365},[],[367,371,376,384,390,397,402,409,417,423,430,436,442,448,454,461,467,474,479,485,492,499,504,507,512,519,524,529,535,540,547,553,559,565,570,575],{"id":143,"label":368,"issuer":150,"region":151,"url":152,"description":369,"useCases":370,"indexable":248},"EU AI Act","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.",250,{"id":144,"label":372,"issuer":150,"region":151,"url":373,"description":374,"useCases":375,"indexable":248},"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.",223,{"id":377,"label":378,"issuer":379,"region":380,"url":381,"description":382,"useCases":383,"indexable":248},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":385,"label":386,"issuer":387,"region":195,"url":388,"description":389,"useCases":363,"indexable":248},"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.",{"id":391,"label":392,"issuer":393,"region":151,"url":394,"description":395,"useCases":396,"indexable":248},"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.",73,{"id":145,"label":398,"issuer":150,"region":151,"url":399,"description":400,"useCases":401,"indexable":248},"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.",67,{"id":403,"label":404,"issuer":405,"region":151,"url":406,"description":407,"useCases":408,"indexable":248},"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.",50,{"id":410,"label":411,"issuer":412,"region":413,"url":414,"description":415,"useCases":416,"indexable":248},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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.",37,{"id":418,"label":419,"issuer":420,"region":413,"url":421,"description":422,"useCases":78,"indexable":248},"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.",{"id":424,"label":425,"issuer":426,"region":380,"url":427,"description":428,"useCases":429,"indexable":248},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":431,"label":432,"issuer":433,"region":195,"url":434,"description":435,"useCases":429,"indexable":248},"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":437,"label":438,"issuer":150,"region":151,"url":439,"description":440,"useCases":441,"indexable":248},"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.",17,{"id":443,"label":444,"issuer":445,"region":151,"url":446,"description":447,"useCases":441,"indexable":248},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":449,"label":450,"issuer":451,"region":195,"url":452,"description":453,"useCases":338,"indexable":248},"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":455,"label":456,"issuer":457,"region":380,"url":458,"description":459,"useCases":460,"indexable":248},"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":462,"label":463,"issuer":150,"region":151,"url":464,"description":465,"useCases":466,"indexable":248},"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":468,"label":469,"issuer":470,"region":195,"url":471,"description":472,"useCases":473,"indexable":248},"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":475,"label":476,"issuer":150,"region":151,"url":477,"description":478,"useCases":473,"indexable":248},"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.",{"id":480,"label":481,"issuer":482,"region":195,"url":483,"description":484,"useCases":473,"indexable":248},"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":488,"region":380,"url":489,"description":490,"useCases":491,"indexable":248},"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.",12,{"id":493,"label":494,"issuer":495,"region":195,"url":496,"description":497,"useCases":498,"indexable":248},"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.",11,{"id":500,"label":501,"issuer":150,"region":151,"url":502,"description":503,"useCases":498,"indexable":248},"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.",{"id":146,"label":505,"issuer":150,"region":151,"url":156,"description":506,"useCases":498,"indexable":248},"PSD2","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":508,"label":509,"issuer":150,"region":151,"url":510,"description":511,"useCases":498,"indexable":248},"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":513,"label":514,"issuer":515,"region":151,"url":516,"description":517,"useCases":518,"indexable":248},"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.",10,{"id":520,"label":521,"issuer":412,"region":413,"url":522,"description":523,"useCases":518,"indexable":248},"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":525,"label":526,"issuer":150,"region":151,"url":527,"description":528,"useCases":518,"indexable":248},"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":530,"label":531,"issuer":532,"region":195,"url":533,"description":534,"useCases":279,"indexable":248},"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.",{"id":536,"label":537,"issuer":150,"region":151,"url":538,"description":539,"useCases":279,"indexable":248},"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":541,"label":542,"issuer":543,"region":544,"url":545,"description":546,"useCases":245,"indexable":248},"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":548,"label":549,"issuer":550,"region":151,"url":551,"description":552,"useCases":331,"indexable":248},"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":554,"label":555,"issuer":556,"region":151,"url":557,"description":558,"useCases":331,"indexable":248},"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":560,"label":561,"issuer":562,"region":413,"url":563,"description":564,"useCases":71,"indexable":248},"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":566,"label":567,"issuer":150,"region":151,"url":568,"description":569,"useCases":71,"indexable":248},"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":571,"label":572,"issuer":150,"region":151,"url":573,"description":574,"useCases":71,"indexable":248},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":576,"label":577,"issuer":578,"region":195,"url":579,"description":580,"useCases":71,"indexable":248},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790783072982]