[{"data":1,"prerenderedAt":555},["ShallowReactive",2],{"uc-cash-application-and-remittance-matching":3,"uc-regulations":341},{"useCase":4,"evidence":185,"blitsAiDeployments":243,"benchmarks":244,"indicative":253,"related":256,"indexability":339,"includeUnpublished":191},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":22,"patterns":24,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"problem":36,"problemStats":37,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":91,"feasibility":92,"implementation":105,"risk":147,"blitsAi":161,"faq":163,"related":176,"datePublished":180,"dateModified":180,"lastVerified":180,"changelog":181,"slug":184},"AI for cash application and remittance matching","Cash application and remittance matching","AI cash application for accounts receivable","AI matches payments to invoices for accounts receivable teams. HighRadius reports 98% auto applied at Keurig Dr Pepper and 96% at ResMed as automation results.","published","AI that reads remittance advices in many formats, matches incoming customer payments to open receivable invoices, proposes deduction and short pay reason codes from prior resolutions, and routes only the genuine exceptions to a cash application analyst, so the accounts receivable sub ledger clears itself for the clean majority of payments.",[12,13,14,15],"cash application automation","AI accounts receivable matching","remittance processing","automated payment matching",[17,18,19,20,21],"cross-industry","manufacturing","retail-and-ecommerce","healthcare","logistics-and-transportation",[23],"finance-and-accounting",[25,26,27,28],"document-processing","agentic-workflow","anomaly-detection","classification-and-routing",[30,31,32],"email","internal-tools","api","back-office","supervised-agent","early-adopters","Every company that sells on credit has to turn an incoming payment into a cleared invoice, and\nthe payment rarely arrives with clean instructions. A wire lands with a reference number that\ndoes not match any invoice, a check comes with a remittance advice stapled to a delivery note, an\nERP portal payment bundles twelve invoices into one line, and a short paid invoice gives no reason\nat all. Rule based matching engines clear the exact, one to one payments; everything else becomes\na growing pile of unapplied cash that a credit or accounts receivable analyst has to open,\ninterpret and apply by hand, invoice by invoice.\n\nThe cost shows up twice. Analyst time goes into repetitive lookup and data entry instead of\ngenuine exceptions, and unapplied or misapplied cash distorts the accounts receivable ageing\nreport, triggers unnecessary collections calls to customers who already paid, and pushes up days\nsales outstanding, which is money the company has effectively already earned but cannot yet use.\nMachine learning changes what a matching engine can clear on its own: a learned matching layer can\npropose one to many and many to many matches, tolerate partial references and short pays within a\ntolerance, and suggest a deduction reason code from how similar cases were resolved before,\nleaving people to judge the cases that are genuinely new.",[],"1. **Capture every remittance.** Emails, customer portal downloads, EDI 820 files and scanned\n   lockbox images arrive in one pipeline; document AI reads the unstructured ones and extracts\n   payer, amount, currency and any invoice references.\n2. **Match beyond the rules.** Deterministic rules clear exact one to one matches first. A learned\n   matching layer then proposes one to many and many to many matches using fuzzy references,\n   amounts within tolerance and payment history, each with a confidence score.\n3. **Propose a reason for what does not match.** For short pays and deductions the agent suggests\n   a reason code and the likely open item, drawn from how the team resolved similar cases before,\n   instead of leaving a blank exception for someone to start from scratch.\n4. **Auto apply within limits.** Matches above the confidence and value threshold post\n   automatically to the sub ledger; everything else goes to an analyst queue with the payment,\n   remittance and candidate invoices shown together.\n5. **Feed collections and credit.** Genuine deductions and disputes that need a decision are\n   handed to the deductions or collections team with the evidence attached, so they do not sit\n   unresolved inside the cash application queue.\n6. **Learn under control.** Analyst decisions feed back as candidate matching rules or reason code\n   suggestions, which an accounts receivable manager approves before they change what posts\n   automatically.",[40,41,42,43],"cost-to-serve","speed","employee-productivity","risk-reduction",[45,46,47,48,49],"automation-rate","hours-saved","cost-savings","handling-time-reduction","cycle-time-days",{"referenceOrg":51,"inputs":52,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A manufacturer that processes 200,000 customer payments a year",[53,59,66,73,79],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"paymentsPerYear","Customer payments processed per year",200000,"payments per year","The reference company. Replace with your own payment volume.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"manualMinutes","Minutes an analyst spends manually applying a payment",6,12,"minutes per payment","Editorial assumption, replace with your own time study.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"autoApplyShare","Share of payments applied without a person once the AI is added",0.75,0.95,"fraction of payments","Total share including exact matches a rules engine already clears, conservative against the evidence on this page (HighRadius reported 98% of payments auto applied at Keurig Dr Pepper and a 96% cash posting hit rate at ResMed, both totals that include rules based matching, not the AI increment alone). Treat both vendor reported figures as an upper bound, not a typical first year result.",{"key":74,"label":75,"low":76,"high":77,"unit":71,"note":78},"ruleBaselineShare","Share of payments existing rules already clear before adding AI",0.3,0.5,"Editorial assumption, replace with your own baseline auto match rate. Deterministic one to one rules typically clear a meaningful share of exact matches on their own; the AI should be credited only for what it adds on top of this baseline.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"costPerHour","Fully loaded cost of an accounts receivable or credit analyst",30,50,"USD per hour","Editorial assumption for a blended onshore and offshore accounts receivable team.","paymentsPerYear * (autoApplyShare - ruleBaselineShare) * manualMinutes / 60 * costPerHour","USD","per year","Manual cash application effort avoided by the AI, over what rules already clear","Labour only, and only the increment over what existing rule based matching already clears before any AI is added. It leaves out the working capital value of a lower days sales outstanding, deductions recovered, fewer unnecessary collections calls to customers who already paid, and the cost of the platform and the integration work.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":99},"medium","The matching logic is well understood; the work is in the data. Remittance formats and channels vary by customer, references are inconsistent, and short pays need documented tolerances that the credit team agrees before any threshold is automated.",[96,97,98],"Twelve months of history of payments matched to invoices, with the resolution chosen","Clean customer and invoice master data, including known third party payers","Documented tolerances and write off thresholds for short pays and deductions",[100,101,102,103,104],"ERP or accounts receivable sub ledger (open invoices, customer master)","Bank statement and lockbox feeds (BAI2, MT940, camt.053)","Email and customer portal for remittance advices","Deductions and claims management system","Collections platform for unresolved items",{"steps":106,"guardrails":125,"humanInTheLoop":130,"kpisToInstrument":131,"failureModes":137},[107,110,113,116,119,122],{"title":108,"detail":109},"Start with your highest volume payment channel","Rank payment channels (ACH, wire, card, check lockbox) by manual cash application volume and pick the one with the most legible remittance data first, so the model has something to learn from quickly.",{"title":111,"detail":112},"Standardize remittance capture","Route every channel, including email attachments and portal downloads, into one pipeline and let document AI extract payer, amount and any invoice references before matching runs.",{"title":114,"detail":115},"Baseline what the current rules already clear","Measure the existing auto match rate so the AI is credited only for the increment, and fix obvious master data problems (duplicate customers, stale bank details) before tuning a model.",{"title":117,"detail":118},"Run in shadow mode","Let the AI propose matches and reason codes next to the analysts for several cycles, compare its proposals with what they actually did, and only then raise the auto apply threshold.",{"title":120,"detail":121},"Automate posting within limits","Auto apply only matches above an agreed confidence and value threshold; everything else, and anything that would change a customer's bank details, goes to a person.",{"title":123,"detail":124},"Close the loop into deductions and collections","Route unresolved short pays and disputes to the team that owns them with the evidence attached, so cash application does not become the place where deductions go to wait.",[126,127,128,129],"Auto apply only above an agreed confidence threshold and below an agreed value threshold; anything else goes to a person","Customer bank account and master data changes verified out of band, never inferred from a remittance","Write offs and reason code changes above an approval limit enforced by the ERP, not the model","Every automated posting keeps a link back to the source remittance and the matching logic used","Cash application analysts confirm or correct proposed matches below the confidence threshold and any deduction reason code. An accounts receivable or credit manager approves write offs above a set amount and reviews a sample of auto applied postings every month for drift.",[132,133,134,135,136],"Auto apply rate by payment channel","Unapplied cash balance and its age","Minutes per manually applied payment","Deduction and short pay resolution time","Days sales outstanding trend after go live",[138,141,144],{"title":139,"detail":140},"Confident but wrong postings","A matching model trained on a messy history applies a payment to the wrong invoice with high confidence. Set a hard confidence floor below which nothing posts automatically, and sample auto applied postings weekly.",{"title":142,"detail":143},"Auto apply that hides real deductions","Short pays get force matched to keep the ageing report clean instead of being flagged as pricing or delivery disputes. Track resolution reason and root cause, not only the match rate.",{"title":145,"detail":146},"Customer master drift","New subsidiaries, factoring arrangements or third party payers (where the payer is not the invoiced customer) block matching that used to work. Keep the customer master current, including known third party payers.",{"euAiAct":148,"regulations":151,"guidance":154,"controls":155,"incidents":160},{"tier":149,"basis":150},"minimal","Matching a company's own incoming payments to its own open invoices is a back office finance operation. It is not listed in Annex III and does not decide a natural person's creditworthiness or eligibility for a service, so it is minimal risk and the AI literacy duty of Article 4 applies. Using deduction or payment behaviour to score an individual sole trader's creditworthiness would need a fresh risk assessment.",[152,153],"eu-ai-act","gdpr",[],[156,157,158,159],"Auto apply confidence and value thresholds set and periodically reviewed by the controller","Full audit trail from remittance to posted journal entry, including the confidence score used","Out of band verification of any customer bank account or master data change","Monthly sample review of auto applied postings by the accounts receivable or credit manager",[],{"howToBuild":162},"On Blits.ai an inbound remittance email on the **email channel** starts a dialog flow whose\n**trigger workflow** block starts the **agentic workflow**; a bank or lockbox file can start\nthe same workflow through an **API token** instead. Inside the workflow an **AI agent** with\n**structured output** extracts payer, amount and invoice references from the remittance text,\nand **custom functions** look up open items in the ERP or accounts\nreceivable sub ledger (for example through the SAP, Oracle NetSuite or Microsoft Dynamics 365\nconnections in the integration catalog) to build the proposed match. Deduction reason codes,\ntolerances and write off policy sit in the **knowledge base** with hybrid retrieval, so the\nagent can explain why it proposed a match or a code.\n\nAn approval step, built with flow or custom function logic rather than a separate built in\nfeature, holds any match above an agreed value threshold, or below an agreed confidence\nthreshold, for a cash application analyst to approve or reject before a custom function posts\nthe approved match to the ledger. Every run keeps a\n**full audit trail**, **test suites** replay a labelled set of past remittances before any\nchange goes live, and **monitors**\nrun scheduled checks against the agent and alert on failure. The platform is **model agnostic**\nand can run in the **EU or UAE data residency** region a customer needs.",[164,167,170,173],{"question":165,"answer":166},"How accurate is AI cash application?","It depends heavily on how legible the remittance data is. HighRadius reported that Keurig Dr Pepper auto applied 98% of payments and that ResMed reached a 96% cash posting hit rate across its business units, but neither source describes machine learning or AI matching specifically: both are automation results that could include a large share of rule based matching. Treat both as an upper bound rather than a typical result: plan for a lower rate on a first deployment, especially on channels with heavy check or manual remittance volume.",{"question":168,"answer":169},"What is the difference between cash application and bank or ledger reconciliation?","Cash application matches a company's incoming customer payments to its own open receivable invoices, so the accounts receivable sub ledger clears. Bank and ledger reconciliation matches a bank's or fund's own statements, settlement files and general ledger to each other; see AI for ledger and payment reconciliation for that job.",{"question":171,"answer":172},"What should stay with a person?","Short pays and deductions that need a judgment call on the reason, any write off above the approval limit, and anything that touches a customer's bank details or master data.",{"question":174,"answer":175},"Does AI cash application reduce days sales outstanding?","Faster, more accurate application clears invoices sooner and stops collections calls to customers who already paid, both of which help days sales outstanding. HighRadius reported that ResMed's days sales outstanding fell by about 33 days within 10 months; that case study covers ResMed's broader Customer-to-Cash Receivables Management suite (deductions and payment options included), so it is unclear how much of the reduction is attributable to cash application alone, and it is not one of the benchmarked KPIs on this page. Treat a days sales outstanding improvement as a likely secondary effect to measure on your own data, not a guaranteed one.",[177,178,179],"ledger-and-payment-reconciliation","supplier-invoice-processing","treasury-cash-flow-forecasting","2026-09-28",[182],{"date":180,"note":183},"First published","cash-application-and-remittance-matching",[186,222],{"title":187,"useCases":188,"organization":189,"vendors":194,"summary":198,"stage":199,"year":200,"channels":201,"languages":202,"metrics":204,"outcomeDisclosed":212,"sources":213,"verification":217,"grade":219,"id":220,"organizationSlug":221},"Keurig Dr Pepper: automated cash application",[184],{"name":190,"anonymized":191,"country":192,"region":193,"industry":18},"Keurig Dr Pepper",false,"US","north-america",[195],{"name":196,"role":197},"HighRadius","platform","Keurig Dr Pepper, named on the source page by its then name Dr Pepper Snapple Group, brought payments processing in house and deployed HighRadius's cash application software to replace manual remittance aggregation and posting across its accounts receivable operation. The system gives real time visibility into payment statuses, automates invoice matching and deductions coding, and captures remittance information from multiple payment formats. The vendor also quotes Colleen Zdrojewski, then Vice President of Financial Services at Dr Pepper Snapple Group, saying financial services costs declined by $2.5 million while volume, quality and productivity increased; the page's own meta description and About text frame this as part of an annual run rate saving from bringing the previously outsourced payments processing in house together with the software, not a figure attributable to the matching software alone. The page calls the saving \"Saved in One Year with AI\" in a stat box caption, but nowhere describes machine learning or an AI matching method, so that label is the vendor's marketing framing, not a technical claim this record can verify.","production",2021,[],[203],"en",[205],{"kpi":45,"value":206,"unit":207,"qualifier":208,"claimant":209,"quote":210,"sourceUrl":211},98,"percent","exact","vendor","98% Payments Auto-Applied by the System","https://www.highradius.com/resources/case-studies/keurig-dr-pepper/",true,[214],{"url":211,"title":215,"publisher":196,"archivedUrl":216},"KDP | Saving $2.5M with Cash App Automation","https://web.archive.org/web/20230925105936/https://www.highradius.com/resources/case-studies/keurig-dr-pepper/",{"level":218,"checkedAt":180},"source-verified","C","keurig-dr-pepper-cash-application-automation",null,{"title":223,"useCases":224,"organization":225,"vendors":227,"summary":229,"stage":199,"year":200,"channels":230,"languages":231,"metrics":232,"outcomeDisclosed":212,"sources":237,"verification":241,"grade":219,"id":242,"organizationSlug":221},"ResMed: automated cash application across all business units",[184],{"name":226,"anonymized":191,"country":192,"region":193,"industry":20},"ResMed",[228],{"name":196,"role":197},"ResMed, a global connected care company, deployed HighRadius's Customer-to-Cash Receivables Management suite to standardize accounts receivable operations across business units, posting cash automatically and auto coding deductions. A ResMed manager reported that the solution saved 50% of an analyst's time specifically on data aggregation, one sub task of cash application, not an overall productivity gain. HighRadius also reported a reduction in days sales outstanding of about 33 days within 10 months; the case study covers ResMed's broader Customer-to-Cash Receivables Management suite, so it is unclear how much of that reduction is attributable to cash application alone.",[],[203],[233],{"kpi":45,"value":234,"unit":207,"qualifier":208,"claimant":209,"quote":235,"sourceUrl":236},96,"96% Cash Posting Hit-Rate","https://www.highradius.com/resources/case-studies/resmed/",[238],{"url":236,"title":239,"publisher":196,"archivedUrl":240},"ResMed | Customer-to-Cash Receivables Management","https://web.archive.org/web/20230925062621/https://www.highradius.com/resources/case-studies/resmed/",{"level":218,"checkedAt":180},"resmed-cash-application-automation",0,[245],{"kpi":45,"label":246,"unit":207,"aggregate":212,"higherIsBetter":212,"n":247,"nUpTo":243,"median":248,"min":234,"max":206,"byClaimant":249,"vendorOnly":212,"points":250},"Automation rate",2,97,{"organization":243,"vendor":247,"regulator":243,"independent":243},[251,252],{"evidenceId":220,"organization":190,"value":206,"qualifier":208,"claimant":209,"grade":219,"pooled":212},{"evidenceId":242,"organization":226,"value":234,"qualifier":208,"claimant":209,"grade":219,"pooled":212},{"low":254,"high":255},270000,899999.9999999998,[257,278,301,324],{"slug":177,"title":258,"shortTitle":259,"definition":260,"status":9,"industries":261,"functions":267,"patterns":269,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"evidenceCount":270,"publicEvidenceCount":270,"organizations":271,"bestGrade":276,"headline":221,"lastVerified":277,"indexable":212},"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.",[262,263,264,17,265,266],"banking","payments","capital-markets","wealth-and-asset-management","government",[23,268],"operations",[26,27,25],4,[272,273,274,275],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme","B","2026-09-27",{"slug":178,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":284,"patterns":286,"audience":33,"autonomy":34,"adoptionStage":287,"segment":33,"evidenceCount":288,"publicEvidenceCount":270,"organizations":289,"bestGrade":276,"headline":294,"lastVerified":277,"indexable":212},"AI for supplier invoice processing in accounts payable","Supplier invoice processing","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.",[17,262,266,19,283],"energy-and-utilities",[23,285],"procurement",[25,26,27,28],"mainstream",5,[290,291,292,293],"Federal Deposit Insurance Corporation","Kingfisher","U.S. Immigration and Customs Enforcement","Veolia",{"kpi":295,"label":296,"unit":207,"n":297,"nUpTo":297,"kind":298,"value":299,"qualifier":208,"claimant":300,"organization":291,"vendorReported":191},"productivity-gain","Productivity gain",1,"reported",80,"organization",{"slug":179,"title":302,"shortTitle":303,"definition":304,"status":9,"industries":305,"functions":306,"patterns":309,"audience":312,"autonomy":313,"adoptionStage":35,"segment":314,"evidenceCount":288,"publicEvidenceCount":288,"organizations":315,"bestGrade":276,"headline":321,"lastVerified":277,"indexable":212},"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.",[262,17,21,19,18],[307,23,308],"treasury","analytics-and-reporting",[310,28,311,26],"prediction-and-scoring","conversational-agent","employee-facing","assist","specialized-businesses",[316,317,318,319,320],"Amtrak","Bank of America","Domino's Pizza","JPMorgan Chase","Prysmian",{"kpi":295,"label":296,"unit":207,"n":247,"nUpTo":297,"kind":298,"value":322,"qualifier":323,"claimant":300,"organization":319,"vendorReported":191},90,"approximately",{"slug":325,"title":326,"shortTitle":327,"definition":328,"status":9,"industries":329,"functions":332,"patterns":333,"audience":33,"autonomy":34,"adoptionStage":35,"segment":33,"evidenceCount":247,"publicEvidenceCount":247,"organizations":334,"bestGrade":219,"headline":337,"lastVerified":180,"indexable":212},"travel-and-expense-audit-agent","AI agent for travel and expense report audit","Travel and expense audit","An AI agent that checks every travel and expense report line against policy, receipts and prior submissions instead of a small manual sample, flags duplicates, altered receipts and policy violations with the evidence attached, and auto approves the clean majority so auditors spend their time on the reports that are genuinely risky.",[17,330,331],"pharma-and-life-sciences","technology",[23],[25,27,28,26],[335,336],"Databricks","Takeda",{"kpi":45,"label":246,"unit":207,"n":247,"nUpTo":243,"kind":298,"value":338,"qualifier":208,"claimant":209,"organization":335,"vendorReported":212},72,{"indexable":212,"reasons":340},[],[342,349,354,362,369,375,382,389,397,404,411,417,424,431,437,442,449,454,460,466,472,478,484,489,494,501,508,513,518,525,531,537,544,549],{"id":152,"label":343,"issuer":344,"region":345,"url":346,"description":347,"useCases":348,"indexable":212},"EU AI Act","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":153,"label":350,"issuer":344,"region":345,"url":351,"description":352,"useCases":353,"indexable":212},"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":355,"label":356,"issuer":357,"region":358,"url":359,"description":360,"useCases":361,"indexable":212},"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":363,"label":364,"issuer":365,"region":193,"url":366,"description":367,"useCases":368,"indexable":212},"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":370,"label":371,"issuer":344,"region":345,"url":372,"description":373,"useCases":374,"indexable":212},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":376,"label":377,"issuer":378,"region":345,"url":379,"description":380,"useCases":381,"indexable":212},"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":383,"label":384,"issuer":385,"region":345,"url":386,"description":387,"useCases":388,"indexable":212},"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":390,"label":391,"issuer":392,"region":393,"url":394,"description":395,"useCases":396,"indexable":212},"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":398,"label":399,"issuer":400,"region":393,"url":401,"description":402,"useCases":403,"indexable":212},"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":405,"label":406,"issuer":407,"region":358,"url":408,"description":409,"useCases":410,"indexable":212},"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":412,"label":413,"issuer":414,"region":193,"url":415,"description":416,"useCases":410,"indexable":212},"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":418,"label":419,"issuer":420,"region":345,"url":421,"description":422,"useCases":423,"indexable":212},"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":425,"label":426,"issuer":427,"region":358,"url":428,"description":429,"useCases":430,"indexable":212},"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":432,"label":433,"issuer":344,"region":345,"url":434,"description":435,"useCases":436,"indexable":212},"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":438,"label":439,"issuer":344,"region":345,"url":440,"description":441,"useCases":436,"indexable":212},"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":443,"label":444,"issuer":445,"region":193,"url":446,"description":447,"useCases":448,"indexable":212},"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":450,"label":451,"issuer":344,"region":345,"url":452,"description":453,"useCases":63,"indexable":212},"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":455,"label":456,"issuer":457,"region":193,"url":458,"description":459,"useCases":63,"indexable":212},"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":461,"label":462,"issuer":463,"region":358,"url":464,"description":465,"useCases":63,"indexable":212},"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":467,"label":468,"issuer":344,"region":345,"url":469,"description":470,"useCases":471,"indexable":212},"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":473,"label":474,"issuer":475,"region":193,"url":476,"description":477,"useCases":471,"indexable":212},"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":479,"label":480,"issuer":392,"region":393,"url":481,"description":482,"useCases":483,"indexable":212},"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":485,"label":486,"issuer":344,"region":345,"url":487,"description":488,"useCases":483,"indexable":212},"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":490,"label":491,"issuer":344,"region":345,"url":492,"description":493,"useCases":483,"indexable":212},"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":495,"label":496,"issuer":497,"region":345,"url":498,"description":499,"useCases":500,"indexable":212},"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":502,"label":503,"issuer":504,"region":193,"url":505,"description":506,"useCases":507,"indexable":212},"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":509,"label":510,"issuer":344,"region":345,"url":511,"description":512,"useCases":507,"indexable":212},"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":514,"label":515,"issuer":344,"region":345,"url":516,"description":517,"useCases":62,"indexable":212},"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":519,"label":520,"issuer":521,"region":522,"url":523,"description":524,"useCases":288,"indexable":212},"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":526,"label":527,"issuer":528,"region":345,"url":529,"description":530,"useCases":270,"indexable":212},"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":532,"label":533,"issuer":534,"region":345,"url":535,"description":536,"useCases":270,"indexable":212},"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":538,"label":539,"issuer":540,"region":393,"url":541,"description":542,"useCases":543,"indexable":212},"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":545,"label":546,"issuer":344,"region":345,"url":547,"description":548,"useCases":543,"indexable":212},"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":550,"label":551,"issuer":552,"region":193,"url":553,"description":554,"useCases":543,"indexable":212},"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.",1790598299332]