[{"data":1,"prerenderedAt":632},["ShallowReactive",2],{"uc-supplier-invoice-processing":3,"uc-regulations":423},{"useCase":4,"evidence":195,"blitsAiDeployments":312,"benchmarks":313,"indicative":326,"related":329,"indexability":421,"includeUnpublished":201},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":22,"patterns":25,"channels":30,"audience":34,"autonomy":35,"adoptionStage":36,"segment":34,"problem":37,"problemStats":38,"howItWorks":44,"valueDrivers":45,"kpis":50,"indicativeValue":57,"macroEstimates":85,"feasibility":86,"implementation":100,"risk":139,"blitsAi":172,"faq":174,"related":184,"datePublished":190,"dateModified":190,"lastVerified":190,"changelog":191,"slug":194},"AI for supplier invoice processing in accounts payable","Supplier invoice processing","AI invoice processing for accounts payable","AI reads, matches and codes supplier invoices so AP staff only handle exceptions. Rossum reports 60% touchless extraction at Kingfisher and 8x faster work at Veolia.","published","AI that captures supplier invoices from any format, extracts header and line data, matches them to purchase orders and goods receipts, proposes tax and cost centre coding, flags duplicates and suspected fraud, and routes them for approval and posting, leaving only exceptions to accounts payable staff.",[12,13,14,15],"AI accounts payable automation","invoice data capture","intelligent invoice processing","purchase to pay automation",[17,18,19,20,21],"cross-industry","banking","government","retail-and-ecommerce","energy-and-utilities",[23,24],"finance-and-accounting","procurement",[26,27,28,29],"document-processing","agentic-workflow","anomaly-detection","classification-and-routing",[31,32,33],"email","internal-tools","api","back-office","supervised-agent","mainstream","Every organization pays suppliers, and in many the invoice still arrives as a PDF or paper that\nsomeone keys into the ERP. Staff then chase purchase orders and goods receipts, code the cost\ncentre and tax, and route the invoice to an approver who may take days to respond. Banks are no\nexception: their own procurement of technology, property and services runs through the same\naccounts payable process as any large company.\n\nThe costs are well documented. Manual handling is slow and expensive per invoice, late payment\nloses early payment discounts and damages supplier relationships, and duplicate payments and\ninvoice fraud (a changed bank account on a fake invoice) cause direct losses. Template based OCR\nhelped with the largest suppliers but breaks on the long tail of layouts.",[39],{"statement":40,"sourceTitle":41,"sourceUrl":42,"year":43},"Ardent Partners' State of ePayables 2025 report puts the average cost of processing an invoice at USD 9.84 and finds that the teams it ranks as Best in Class (the 20% with the lowest cost and shortest cycle time) process invoices at a 79% lower cost and 79% faster than their peers.","State of ePayables (Part Nine): AP Benchmarks and Best-in-Class Performance","https://payablesplace.ardentpartners.com/2026/01/state-of-epayables-part-nine-ap-benchmarks-and-best-in-class-performance/",2025,"1. **Capture.** Invoices arrive by email, supplier portal, electronic invoicing network or scanned post.\n   Structured electronic invoices are read directly; the rest go through AI extraction.\n2. **Extract and validate.** The AI extracts supplier, invoice number, dates, amounts, tax and\n   line items, and validates them against the vendor master (tax ID, bank account, currency).\n3. **Match.** It performs two or three way matching against purchase order and goods receipt,\n   within tolerances, and explains any mismatch in plain language.\n4. **Code and check.** For invoices without a purchase order it proposes the general ledger\n   account, cost centre and tax code from history and contract terms, and checks for duplicates\n   and fraud signals such as a changed bank account.\n5. **Route, approve and post.** Clean invoices post automatically within limits; others go to the\n   right approver with a summary. Approvers can ask questions in chat or email, and every step is\n   logged for audit.",[46,47,48,49],"cost-to-serve","speed","risk-reduction","employee-productivity",[51,52,53,54,55,56],"automation-rate","productivity-gain","handling-time-reduction","cost-reduction","processing-time-reduction","error-reduction",{"referenceOrg":58,"inputs":59,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A bank or company that processes 300,000 supplier invoices a year",[60,66,73],{"key":61,"label":62,"low":63,"high":63,"unit":64,"note":65},"invoices","Supplier invoices per year",300000,"invoices per year","The reference organization. Replace with your own invoice volume.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72,"sourceUrl":42},"costPerInvoice","Current fully loaded cost per invoice",6,10,"USD per invoice","Ardent Partners puts the average at USD 9.84 per invoice in its State of ePayables 2025 benchmarks; the low end is an editorial assumption for a team with some automation already in place.",{"key":74,"label":75,"low":76,"high":77,"unit":78,"note":79},"costReduction","Share of processing cost removed",0.3,0.6,"fraction of cost per invoice","Editorial assumption, well below the 79% cost gap between the teams Ardent Partners ranks as Best in Class and their peers.","invoices * costPerInvoice * costReduction","USD","per year","Accounts payable processing cost avoided","Processing cost only. It leaves out captured early payment discounts, avoided duplicate and fraudulent payments, and the cost of the platform, supplier onboarding and ERP integration.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"medium","Extraction and matching are mature, off the shelf capabilities. The effort is in vendor master data quality, purchase order discipline, ERP integration and the approval rules.",[90,91,92,93],"Clean vendor master with tax IDs and verified bank accounts","Purchase orders and goods receipts in the ERP for matched spend","Historical coded invoices to learn coding for non purchase order spend","Approval matrix and limits by entity, cost centre and amount",[95,96,97,98,99],"ERP (accounts payable, purchasing, general ledger)","Email inboxes, supplier portal and electronic invoicing networks","Vendor master and supplier onboarding tools","Payment and treasury systems","Approval workflow and collaboration tools",{"steps":101,"guardrails":117,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[102,105,108,111,114],{"title":103,"detail":104},"Measure the baseline","Record cost per invoice, touchless rate, cycle time and exception reasons by supplier and entity. Most of the value case depends on where exceptions come from.",{"title":106,"detail":107},"Fix the master data first","Duplicate suppliers, missing tax IDs and unverified bank accounts cause more exceptions than extraction errors. Clean them before tuning any model.",{"title":109,"detail":110},"Start with extraction and matching","Automate capture and purchase order matching for the largest suppliers, measure field level accuracy, and keep people validating low confidence fields.",{"title":112,"detail":113},"Add coding and fraud checks","Introduce proposed coding for non purchase order invoices and duplicate and bank account checks, with thresholds agreed with the controller.",{"title":115,"detail":116},"Automate posting within limits","Allow touchless posting only for matched invoices under a value threshold from suppliers with verified details, and widen gradually as quality holds.",[118,119,120,121],"Bank account changes are verified out of band before any payment, never from the invoice alone","Segregation of duties and approval limits are enforced by the ERP, not by the model","Duplicate checks run on every invoice before posting","Low confidence fields always go to a person for validation","Accounts payable staff validate low confidence extractions and resolve exceptions. Budget holders approve invoices according to the approval matrix, and the controller samples touchless postings every month.",[124,125,126,127,128],"Touchless rate (invoices posted without manual intervention)","Field level extraction accuracy on a weekly sample","Cost per invoice and cycle time from receipt to approval","Duplicate and fraudulent invoices caught before payment","Early payment discounts captured",[130,133,136],{"title":131,"detail":132},"Invoice fraud through changed bank details","A fake or altered invoice redirects payment. Verify bank account changes through a separate channel and flag them automatically.",{"title":134,"detail":135},"Wrong coding at scale","A learned coding pattern posts spend to the wrong cost centre for months. Sample coded invoices and review coding drift at every close.",{"title":137,"detail":138},"Automation that moves the queue","Extraction improves but approvals remain slow, so cycle time does not change. Measure end to end, including approval time.",{"euAiAct":140,"regulations":143,"guidance":148,"controls":166,"incidents":171},{"tier":141,"basis":142},"minimal","Processing supplier invoices is not an Annex III use case, is not a practice prohibited by Article 5 and does not involve decisions about natural persons, so it is minimal risk and the AI literacy duty of Article 4 applies. Approvers who ask questions in chat use an internal tool they know is AI; if that is not obvious to the people using it, the provider must also inform them that they are interacting with an AI system (Article 50(1)).",[144,145,146,147],"eu-ai-act","gdpr","iso-42001","dora",[149,155,161],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"VAT in the Digital Age (ViDA)","European Commission","europe","https://taxation-customs.ec.europa.eu/taxation/vat/vat-digital-age-vida_en","The EU VAT package that lets Member States mandate electronic invoicing and, from 1 July 2030, requires digital reporting based on electronic invoices for cross border B2B supplies, which changes the capture step.",{"title":156,"issuer":157,"region":158,"url":159,"note":160},"AS 2201: An Audit of Internal Control Over Financial Reporting That Is Integrated with An Audit of Financial Statements","Public Company Accounting Oversight Board","north-america","https://pcaobus.org/oversight/standards/auditing-standards/details/AS2201","Applies to integrated audits of US public companies (issuers), not to every organization. In those audits, the auditor tests the controls over automated invoice processing like any other control over financial reporting.",{"title":162,"issuer":163,"region":152,"url":164,"note":165},"Digital Operational Resilience Act (Regulation (EU) 2022/2554), Article 28","European Union","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Relevant only when the deployer is a financial entity such as a bank or insurer. Its invoice capture platform and AI vendors are then ICT third party service providers, managed under the ICT third party risk principles of Article 28.",[167,168,169,170],"Extraction and coding models inventoried with an owner and monitored for accuracy","Full trail of extraction, validation, match, approval and posting per invoice","Out of band verification of supplier bank account changes","Monthly sample of touchless postings reviewed by the controller",[],{"howToBuild":173},"On Blits.ai this is an **agentic workflow**. Invoices arrive through the **email channel**,\nwhere a dialog flow triggers the workflow, or directly through an API token. An\n**AI agent** with **structured output** returns the invoice fields, and **custom functions**\nlook up the vendor master, purchase orders and goods receipts in the ERP (for example through\nthe SAP or NetSuite connections in the integration catalog) and create the posting proposal.\nContract terms and coding rules sit in the **knowledge base** with hybrid retrieval.\n\n**Human in the loop confirmation** stops the posting action above a configurable threshold for a\nperson to approve or reject. Other routing rules, such as sending every invoice with a changed\nbank account to a person, are built with **custom functions** in the workflow, and the ready\nmade **Microsoft Teams** tool can\nnotify the approver. Every run keeps a **full audit trail**, **test suites** replay a labelled\nset of invoices before each change goes live, and **monitors** run scheduled checks against the\nagent and alert on failure.",[175,178,181],{"question":176,"answer":177},"What does it cost to process a supplier invoice?","Ardent Partners' State of ePayables 2025 report puts the average at USD 9.84 per invoice, and the accounts payable teams it ranks as Best in Class process invoices at a 79% lower cost and 79% faster than their peers. Your own figure depends on the share of paper, purchase order coverage and approval discipline.",{"question":179,"answer":180},"What share of invoices can go through without a person?","It varies with supplier mix, master data and purchase order coverage, and few sources report a true end to end touchless rate. In its Kingfisher customer story, Rossum reports that 60% of invoices pass data extraction with no manual intervention before they reach SAP. That covers the extraction step only: exceptions are still handled in SAP, and the story does not say what share of invoices is posted without a person.",{"question":182,"answer":183},"How do you stop AI from paying fraudulent invoices?","Never let the invoice change where money goes. Verify supplier bank account changes out of band, run duplicate checks on every invoice, and keep segregation of duties and approval limits in the ERP rather than in the model.",[185,186,187,188,189],"intelligent-document-processing","procurement-contract-review","ledger-and-payment-reconciliation","correspondence-triage-and-routing","vendor-due-diligence","2026-09-27",[192],{"date":190,"note":193},"First published","supplier-invoice-processing",[196,223,245,281],{"title":197,"useCases":198,"organization":199,"vendors":203,"summary":204,"stage":205,"year":206,"channels":207,"languages":208,"metrics":210,"outcomeDisclosed":201,"sources":211,"verification":217,"grade":220,"id":221,"organizationSlug":222},"FDIC: AI extraction of invoice and contract data for reconciliation",[194],{"name":200,"anonymized":201,"country":202,"region":158,"industry":19},"Federal Deposit Insurance Corporation",false,"US",[],"The FDIC, the US bank deposit insurer and supervisor, is developing AI that extracts data from invoice and contract PDFs and reconciles them, emailing oversight managers a spreadsheet of discrepancies and errors. A separate initiative in its Division of Finance plans AI monitoring of invoices for proper submission and duplicate payments. Both are listed as in development or initiated in the 2024 federal inventory; no results are published.","announced",2024,[31,32],[209],"en",[],[212],{"url":213,"title":214,"publisher":215,"date":216},"https://raw.githubusercontent.com/ombegov/2024-Federal-AI-Use-Case-Inventory/main/data/2024_consolidated_ai_inventory_raw_v2.csv","2024 consolidated AI use case inventory (raw data, version 2)","Office of Management and Budget (GitHub)","2025-01-23",{"level":218,"checkedAt":219},"source-verified","2026-09-26","B","fdic-invoice-and-contract-data-extraction","federal-deposit-insurance-corporation",{"title":224,"useCases":225,"organization":226,"vendors":228,"summary":234,"stage":235,"year":236,"channels":237,"languages":238,"metrics":239,"outcomeDisclosed":201,"sources":240,"verification":242,"grade":220,"id":243,"organizationSlug":244},"US Immigration and Customs Enforcement: intelligent document processing for invoices and forms",[194,185],{"name":227,"anonymized":201,"country":202,"region":158,"industry":19},"U.S. Immigration and Customs Enforcement",[229,232],{"name":230,"role":231},"UiPath","platform",{"name":233,"role":231},"Microsoft","Business units at ICE, part of the Department of Homeland Security, use an intelligent document processing platform (UiPath Suite and Azure AI Document Intelligence) with OCR and machine learning models to verify, extract and classify information from forms, automating repeatable work such as invoice processing and form entry validation. The agency lists it in operation since 2019 and says it saves staff significant time while improving data quality. No figures are published.","production",2019,[32],[209],[],[241],{"url":213,"title":214,"publisher":215,"date":216},{"level":218,"checkedAt":219},"us-immigration-and-customs-enforcement-intelligent-document-processing","u-s-immigration-and-customs-enforcement",{"title":246,"useCases":247,"organization":248,"vendors":251,"summary":258,"stage":259,"year":260,"channels":261,"languages":262,"metrics":263,"outcomeDisclosed":272,"sources":273,"verification":277,"grade":278,"id":279,"organizationSlug":280},"Veolia: AI invoice capture for a shared service centre serving 30 entities",[194],{"name":249,"anonymized":201,"country":250,"region":152,"industry":21},"Veolia","FR",[252,254,255],{"name":253,"role":231},"Rossum",{"name":230,"role":231},{"name":256,"role":257},"InnovationPath","integrator","A Veolia shared service centre that posts supplier invoices for 30 group entities rebuilt the process around central email inboxes, a UiPath robot built by InnovationPath and Rossum's AI data capture, while moving 60,000 suppliers to paperless invoicing. The most technically skilled of the former data entry clerks now review extractions and handle exceptions as \"AI Associates\"; output goes as a standard EDI message to each entity's ERP. The vendor reports an eightfold speed up in the AI Associates' processing.","scaled",2022,[31,32],[],[264],{"kpi":52,"value":265,"unit":266,"qualifier":267,"period":268,"claimant":269,"quote":270,"sourceUrl":271},87.5,"percent","up-to","processing time savings of the AI Associates","vendor","So far, the AI Associates were able to speed up their processing by 8x, with time-savings efficiency reaching up to 87.5%.","https://rossum.ai/customer-stories/veolia/",true,[274],{"url":271,"title":275,"publisher":253,"archivedUrl":276},"Customer Story - Veolia - Rossum.ai","https://web.archive.org/web/20220628152622/https://rossum.ai/customer-stories/veolia/",{"level":218,"checkedAt":219},"C","veolia-ssc-invoice-processing",null,{"title":282,"useCases":283,"organization":284,"vendors":287,"summary":291,"stage":259,"year":292,"channels":293,"languages":294,"metrics":295,"outcomeDisclosed":272,"sources":308,"verification":310,"grade":278,"id":311,"organizationSlug":280},"Kingfisher: AI invoice capture in the accounts payable shared service centre",[194],{"name":285,"anonymized":201,"country":286,"region":152,"industry":20},"Kingfisher","GB",[288,289],{"name":253,"role":231},{"name":290,"role":231},"SAP","Kingfisher's global business services centre in Poland, which handles accounts payable for six European countries (about 40,000 invoices a month), went live with Rossum's AI data capture in January 2021. Invoices arrive in central email inboxes; Rossum routes them by country and invoice type, extracts and validates the data, checks for duplicates and passes it to robots that index the invoices in SAP, where accountants still handle exceptions. The vendor reports that 60% of invoices now pass the data extraction step without any manual intervention before they reach SAP (it does not report an end to end touchless rate), that average indexing time fell from 5 minutes to 25 seconds, and that 14 full time employees moved to other invoice processing tasks.",2021,[31,32],[],[296,302],{"kpi":53,"value":297,"unit":266,"qualifier":298,"period":299,"claimant":269,"quote":300,"sourceUrl":301},90,"approximately","time to index an invoice into SAP (from about 5 minutes to 25 seconds on average)","Kingfisher's GBS cuts SAP invoice indexing time by 90% with end-to-end AP automation","https://rossum.ai/customer-stories/customer-story-kingfisher/",{"kpi":52,"value":303,"unit":266,"qualifier":304,"period":305,"claimant":306,"quote":307,"sourceUrl":301},80,"exact","manual data entry work of the accounts payable accountants, with 79% of invoice fields read automatically","organization","That means 80% less manual work for our accountants, so the team can focus on vendor queries, exceptions, and higher-value work instead of just typing data all day.",[309],{"url":301,"title":300,"publisher":253},{"level":218,"checkedAt":190},"kingfisher-ap-invoice-capture",1,[314,321],{"kpi":52,"label":315,"unit":266,"aggregate":272,"higherIsBetter":272,"n":312,"nUpTo":312,"median":303,"min":303,"max":303,"byClaimant":316,"vendorOnly":201,"points":318},"Productivity gain",{"organization":312,"vendor":317,"regulator":317,"independent":317},0,[319,320],{"evidenceId":279,"organization":249,"value":265,"qualifier":267,"claimant":269,"grade":278,"pooled":201},{"evidenceId":311,"organization":285,"value":303,"qualifier":304,"claimant":306,"grade":278,"pooled":272},{"kpi":53,"label":322,"unit":266,"aggregate":272,"higherIsBetter":272,"n":312,"nUpTo":317,"median":297,"min":297,"max":297,"byClaimant":323,"vendorOnly":272,"points":324},"Handling time reduction",{"organization":317,"vendor":312,"regulator":317,"independent":317},[325],{"evidenceId":311,"organization":285,"value":297,"qualifier":298,"claimant":269,"grade":278,"pooled":272},{"low":327,"high":328},540000,1800000,[330,354,372,388,407],{"slug":185,"title":331,"shortTitle":332,"definition":333,"status":9,"industries":334,"functions":337,"patterns":340,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":342,"publicEvidenceCount":343,"organizations":344,"bestGrade":220,"headline":349,"lastVerified":190,"indexable":272},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[17,19,335,336],"automotive","manufacturing",[338,339,23],"operations","case-management",[26,341,29],"computer-vision",7,5,[345,346,227,347,348],"Ancine","Pupuk Indonesia","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":350,"label":351,"unit":266,"n":312,"nUpTo":317,"kind":352,"value":297,"qualifier":353,"claimant":269,"organization":345,"vendorReported":272},"accuracy","Accuracy","reported","at-least",{"slug":186,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":359,"patterns":361,"audience":364,"autonomy":365,"adoptionStage":366,"evidenceCount":69,"publicEvidenceCount":343,"organizations":367,"bestGrade":220,"headline":280,"lastVerified":190,"indexable":272},"AI assistant for procurement and supplier contract review","Procurement and contract review","An assistant for procurement and vendor management that reads supplier contracts and proposals, extracts the key terms, flags deviations from the organization's standard positions, drafts requests for proposal and evaluation matrices, and prepares negotiation positions, with a procurement or legal owner approving every conclusion.",[17,18,19,20,336],[24,360,23],"legal",[26,362,363,27],"rag-knowledge-assistant","content-generation","employee-facing","copilot","early-adopters",[368,369,370,371],"General Services Administration","Administration for Children and Families","Internal Revenue Service","Walmart",{"slug":187,"title":373,"shortTitle":374,"definition":375,"status":9,"industries":376,"functions":380,"patterns":381,"audience":34,"autonomy":35,"adoptionStage":366,"segment":34,"evidenceCount":382,"publicEvidenceCount":382,"organizations":383,"bestGrade":220,"headline":280,"lastVerified":190,"indexable":272},"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.",[18,377,378,17,379,19],"payments","capital-markets","wealth-and-asset-management",[23,338],[27,28,26],4,[384,385,386,387],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme",{"slug":188,"title":389,"shortTitle":390,"definition":391,"status":9,"industries":392,"functions":394,"patterns":396,"audience":34,"autonomy":35,"adoptionStage":36,"segment":34,"evidenceCount":69,"publicEvidenceCount":69,"organizations":398,"bestGrade":220,"headline":405,"lastVerified":190,"indexable":272},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[17,18,393,19],"insurance",[338,395,339],"customer-service",[29,26,397],"summarization",[399,400,401,402,403,404],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":350,"label":351,"unit":266,"n":312,"nUpTo":317,"kind":352,"value":406,"qualifier":304,"claimant":269,"organization":403,"vendorReported":272},91,{"slug":189,"title":408,"shortTitle":409,"definition":410,"status":9,"industries":411,"functions":412,"patterns":415,"audience":364,"autonomy":365,"adoptionStage":366,"segment":416,"evidenceCount":382,"publicEvidenceCount":382,"organizations":417,"bestGrade":220,"headline":280,"lastVerified":190,"indexable":272},"AI for third party and vendor risk due diligence","Vendor due diligence","AI that reviews a vendor's security questionnaires, SOC and assurance reports, contracts and model documentation against the organization's control requirements, researches the vendor's ownership, sanctions, financial health and adverse media, drafts the risk assessment for a human to approve and keeps the register of material service providers current with ongoing monitoring.",[17,18,393,19,377],[24,413,414],"risk-management","regulatory-compliance",[26,362,27,397],"second-line",[418,370,419,420],"U.S. Department of Justice","U.S. Department of Agriculture","U.S. Trade and Development Agency",{"indexable":272,"reasons":422},[],[424,429,434,441,448,452,459,466,474,481,488,494,501,508,514,519,526,532,538,544,550,556,561,566,571,578,585,590,595,602,608,614,621,626],{"id":144,"label":425,"issuer":163,"region":152,"url":426,"description":427,"useCases":428,"indexable":272},"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":145,"label":430,"issuer":163,"region":152,"url":431,"description":432,"useCases":433,"indexable":272},"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":146,"label":435,"issuer":436,"region":437,"url":438,"description":439,"useCases":440,"indexable":272},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":442,"label":443,"issuer":444,"region":158,"url":445,"description":446,"useCases":447,"indexable":272},"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":147,"label":449,"issuer":163,"region":152,"url":164,"description":450,"useCases":451,"indexable":272},"DORA","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":453,"label":454,"issuer":455,"region":152,"url":456,"description":457,"useCases":458,"indexable":272},"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":460,"label":461,"issuer":462,"region":152,"url":463,"description":464,"useCases":465,"indexable":272},"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":467,"label":468,"issuer":469,"region":470,"url":471,"description":472,"useCases":473,"indexable":272},"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.",36,{"id":475,"label":476,"issuer":477,"region":470,"url":478,"description":479,"useCases":480,"indexable":272},"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":482,"label":483,"issuer":484,"region":437,"url":485,"description":486,"useCases":487,"indexable":272},"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":489,"label":490,"issuer":491,"region":158,"url":492,"description":493,"useCases":487,"indexable":272},"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":495,"label":496,"issuer":497,"region":152,"url":498,"description":499,"useCases":500,"indexable":272},"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":502,"label":503,"issuer":504,"region":437,"url":505,"description":506,"useCases":507,"indexable":272},"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":509,"label":510,"issuer":163,"region":152,"url":511,"description":512,"useCases":513,"indexable":272},"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":515,"label":516,"issuer":163,"region":152,"url":517,"description":518,"useCases":513,"indexable":272},"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":520,"label":521,"issuer":522,"region":158,"url":523,"description":524,"useCases":525,"indexable":272},"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":527,"label":528,"issuer":163,"region":152,"url":529,"description":530,"useCases":531,"indexable":272},"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":533,"label":534,"issuer":535,"region":158,"url":536,"description":537,"useCases":531,"indexable":272},"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":539,"label":540,"issuer":541,"region":437,"url":542,"description":543,"useCases":531,"indexable":272},"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":545,"label":546,"issuer":163,"region":152,"url":547,"description":548,"useCases":549,"indexable":272},"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":551,"label":552,"issuer":553,"region":158,"url":554,"description":555,"useCases":549,"indexable":272},"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":557,"label":558,"issuer":469,"region":470,"url":559,"description":560,"useCases":70,"indexable":272},"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":562,"label":563,"issuer":163,"region":152,"url":564,"description":565,"useCases":70,"indexable":272},"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":567,"label":568,"issuer":163,"region":152,"url":569,"description":570,"useCases":70,"indexable":272},"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":572,"label":573,"issuer":574,"region":152,"url":575,"description":576,"useCases":577,"indexable":272},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":579,"label":580,"issuer":581,"region":158,"url":582,"description":583,"useCases":584,"indexable":272},"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":586,"label":587,"issuer":163,"region":152,"url":588,"description":589,"useCases":584,"indexable":272},"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":591,"label":592,"issuer":163,"region":152,"url":593,"description":594,"useCases":69,"indexable":272},"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":596,"label":597,"issuer":598,"region":599,"url":600,"description":601,"useCases":343,"indexable":272},"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":603,"label":604,"issuer":605,"region":152,"url":606,"description":607,"useCases":382,"indexable":272},"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":609,"label":610,"issuer":611,"region":152,"url":612,"description":613,"useCases":382,"indexable":272},"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":615,"label":616,"issuer":617,"region":470,"url":618,"description":619,"useCases":620,"indexable":272},"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.",3,{"id":622,"label":623,"issuer":163,"region":152,"url":624,"description":625,"useCases":620,"indexable":272},"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":627,"label":628,"issuer":629,"region":158,"url":630,"description":631,"useCases":620,"indexable":272},"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.",1790598301824]