[{"data":1,"prerenderedAt":603},["ShallowReactive",2],{"uc-statement-and-tax-document-generation":3,"uc-regulations":383},{"useCase":4,"evidence":188,"blitsAiDeployments":247,"benchmarks":248,"indicative":255,"related":258,"indexability":381,"includeUnpublished":194},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"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":84,"feasibility":85,"implementation":97,"risk":136,"blitsAi":164,"faq":166,"related":176,"datePublished":183,"dateModified":183,"lastVerified":183,"changelog":184,"slug":187},"AI for statement, tax certificate and confirmation letter generation","Statement and tax documents","AI for statement and tax document generation","Statements, tax certificates and audit confirmations built from system data in approved templates, with AI on intake, checks and free text. Covers PCAOB AS 2310.","published","AI assisted assembly of on request bank documents, such as statements, tax certificates and audit confirmations, where figures come from the system of record and layout from approved templates. AI handles request intake, validation and the free text parts, for documents such as account and card statements, interest certificates, balance and closure letters.",[12,13,14,15],"audit confirmation automation","tax certificate generation","balance confirmation letters","on request document generation",[17,18,19],"banking","wealth-and-asset-management","insurance",[21,22,23],"operations","customer-service","finance-and-accounting",[25,26,27,28],"document-processing","agentic-workflow","classification-and-routing","translation",[30,31,32],"email","api","internal-tools","back-office","supervised-agent","emerging","This job is about the documents a bank produces on request, not the scheduled bulk run of periodic\nstatements. The manual effort sits in the long tail: a balance confirmation for a\ncustomer's auditor, an interest certificate for a tax return, a closure letter for an estate, a\nstatement reprint for a visa application, a certificate in another language. These requests\narrive by email, letter, branch and portal, and each needs data from several systems and a\nspecific layout. Where the process is not automated, staff assemble each document by hand and\nrekey the figures.\n\nAuditor confirmation requests need extra care. Under the PCAOB standard on confirmation (AS 2310),\nthe auditor seeks evidence from a knowledgeable external source, such as the bank that holds the\ncompany's cash, and should consider asking that source about other financial relationships with\nthe company, such as lines of credit, other indebtedness and guarantees. The standard puts the\nduty to keep control of the process on the auditor, so that requests and responses are not\nintercepted or altered. For the bank this is a design consequence rather than a requirement of\nthe standard: its confirmation flow should verify where a request comes from and answer only\nthrough channels the auditor controls. A wrong or incomplete answer weakens the audit evidence it\nwas meant to provide.",[],"1. **Take in the request.** Requests from email, portals, branch systems and confirmation\n   platforms are classified by document type, and the requester's authority is checked (customer\n   consent for an auditor, executor documents for an estate).\n2. **Gather the data.** The workflow retrieves balances, interest, tax withheld, facilities,\n   guarantees and other relationships from the systems of record for the requested date.\n3. **Assemble from the template.** Figures go into the approved template deterministically; the\n   AI only drafts free text parts (for example a note on a closed account) and translations,\n   which are checked.\n4. **Validate against source.** Automated checks reconcile every figure in the document with the\n   source data, and document AI compares the output with what the request asked for.\n5. **Approve, send and retain.** Legal and regulated documents, such as audit confirmations, get a\n   human check on a sample or in full; documents go out through secure channels and are retained\n   with their data for the statutory period.",[40,41,42,43],"cost-to-serve","speed","risk-reduction","customer-experience",[45,46,47,48,49],"automation-rate","processing-time-reduction","error-reduction","hours-saved","time-saved-per-task",{"referenceOrg":51,"inputs":52,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A bank that produces 100,000 on request statements, certificates and confirmations a year",[53,59,66,73],{"key":54,"label":55,"low":56,"high":56,"unit":57,"note":58},"documents","On request documents per year",100000,"documents per year","The reference bank. Count only documents assembled by staff, not bulk statements.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"minutesPerDocument","Staff minutes per document today",10,30,"minutes per document","Editorial assumption; audit confirmations take longer than statement reprints. Replace with your own time study.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"automatedShare","Share of that effort removed",0.4,0.7,"fraction of effort","Editorial assumption. A vendor reports 62 percent of certificate applications at one bank handled solely by an AI agent (see the evidence records), but no public source measures effort removed per document.",{"key":74,"label":75,"low":63,"high":76,"unit":77,"note":78},"costPerHour","Fully loaded operations cost per hour",55,"USD per hour","Editorial assumption, replace with your own.","documents * minutesPerDocument \u002F 60 * automatedShare * costPerHour","USD","per year","Document assembly effort avoided","Labour only. It leaves out the value of fewer errors in legal documents, faster turnaround for customers and auditors, and the cost of the platform and template work.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":92},"medium","Filling approved templates from structured data is a well understood pattern. Teams spend their time reaching every system of record for a given date, the authority checks, and keeping many templates and languages approved and current.",[89,90,91],"Approved templates per document type, language and jurisdiction","Access to balances, interest, tax and relationship data by date","Rules for who may request which document, with the evidence required",[93,94,95,96],"Core banking, card, loan, deposit and custody systems","Customer communications management platform","Email, customer portal and audit confirmation platforms","Document archive for retention",{"steps":98,"guardrails":114,"humanInTheLoop":119,"kpisToInstrument":120,"failureModes":126},[99,102,105,108,111],{"title":100,"detail":101},"Inventory the long tail","List the on request document types, volumes, turnaround times and error rates. Pick the three with the most manual effort, often audit confirmations, interest certificates and balance letters.",{"title":103,"detail":104},"Separate figures from words","Design each template so that every number comes from a system query and only clearly marked free text is drafted. This is the core safety property.",{"title":106,"detail":107},"Automate intake and authority checks","Classify incoming requests and check authority and consent before any data is gathered, so documents are never produced for the wrong requester.",{"title":109,"detail":110},"Validate every document against source","Add an automated reconciliation of each document's figures with the source data, and block sending when it fails.",{"title":112,"detail":113},"Sample, then widen","Start with full human review, move to sampling for document types with a clean record, and keep full review for legal documents.",[115,116,117,118],"Every figure comes from the system of record; the model never generates or edits numbers","Authority and consent checks before any document is produced for a third party","Automated reconciliation of each document with source data before sending","Audit confirmations are sent only to verified auditor addresses or confirmation platforms","Operations staff review exceptions, failed validations and a sample of documents. Legal and audit confirmation documents keep a human check, and the template owner approves every change to templates and wording.",[121,122,123,124,125],"Turnaround time per document type","Documents produced without manual intervention","Errors found in sampling and reported by customers or auditors","Requests rejected for failed authority checks","Staff minutes per document",[127,130,133],{"title":128,"detail":129},"A figure that does not match the books","A document shows a balance for the wrong date or omits a facility. Reconcile every figure and check completeness of relationships.",{"title":131,"detail":132},"Documents sent to impostors","A fraudster poses as an auditor or executor to obtain account information. Verify requester authority and use trusted confirmation channels.",{"title":134,"detail":135},"Template sprawl","Local copies of templates drift from approved wording. Keep one managed template library with owners.",{"euAiAct":137,"regulations":140,"guidance":145,"controls":158,"incidents":163},{"tier":138,"basis":139},"limited","Generating statements and confirmations from system data is not listed in Annex III and takes no decision about credit, insurance or access to essential services. If a customer facing agent takes the requests, Article 50(1) requires telling people they are interacting with an AI system. Under Article 50(2), providers of systems that generate text must mark the output as AI generated, unless the system only performs an assistive function for standard editing, or does not substantially alter the input data provided by the deployer or the semantics thereof; the free text the model drafts here is checked against source, but marking still applies where those exemptions do not.",[141,142,143,144],"eu-ai-act","gdpr","dora","uk-consumer-duty",[146,152],{"title":147,"issuer":148,"region":149,"url":150,"note":151},"SYSC 9 Record keeping","Financial Conduct Authority","europe","https:\u002F\u002Fwww.handbook.fca.org.uk\u002Fhandbook\u002FSYSC\u002F9\u002F","SYSC 9 requires firms to keep records of their services and transactions that let the FCA check compliance, including obligations to clients; common platform firms must retain records of their MiFID business for at least five years. Generated statements and confirmations, with the data behind them, belong in that record.",{"title":153,"issuer":154,"region":155,"url":156,"note":157},"AS 2310: The Auditor's Use of Confirmation","Public Company Accounting Oversight Board","north-america","https:\u002F\u002Fpcaobus.org\u002Foversight\u002Fstandards\u002Fauditing-standards\u002Fdetails\u002FAS2310","Applies to audits of fiscal years ending on or after 15 June 2025. Auditors must direct requests to knowledgeable confirming parties and keep control of the process so that requests and responses are not intercepted or altered, which sets what a bank's confirmation flow has to support.",[159,160,161,162],"Template library under change control with owners and approval dates","Reconciliation evidence stored with every generated document","Access logging for every document produced for a third party","Retention of each document and its data for the statutory period",[],{"howToBuild":165},"On Blits.ai requests arrive through the **email channel**, the REST API or a customer facing\nagent, and an **agentic workflow** handles them. An **AI agent** with **structured output**\nclassifies the request and extracts what is asked, and **custom functions** check authority, fetch\nthe figures from the systems of record and pass them to the bank's existing document or\ncommunications platform, which renders the approved template, so numbers never pass through a\nmodel.\n\n**Human in the loop approval** holds legal documents and failed validations for a person, every\nrun keeps a **full audit trail**, **PII masking** keeps account data out of model prompts, and\n**multi language** support with machine translation covers documents in the customer's\nlanguage. **Test suites** replay real requests before template or prompt changes go live.",[167,170,173],{"question":168,"answer":169},"Should a language model generate the figures on a statement or certificate?","No. Every figure must come from the system of record and be placed into an approved template deterministically. AI helps with request intake, authority checks, free text parts, translation and validating the finished document against source.",{"question":171,"answer":172},"Why are audit confirmations a good first candidate?","They ask about a known set of items (cash balances and, where the auditor chooses to ask, other financial relationships such as lines of credit, other indebtedness and guarantees) and draw on data the bank already holds, so much of the assembly can be automated. They also need strict control: PCAOB AS 2310 makes the auditor keep control of the process so that requests and responses are not intercepted or altered, and a bank flow that verifies requesters and answers through trusted channels supports that.",{"question":174,"answer":175},"Is any bank using AI on these documents in production?","Two named banks use AI on the request side. Federal Bank in India describes its Feddy assistant as AI powered and lists account statements, loan certificates, deposit interest certificates, balance confirmation certificates and TDS certificates among the documents customers can request in a WhatsApp chat. Deviniti, the integrator for Crédit Agricole Bank Polska, reports that 62 percent of certificate applications there are handled solely by an AI agent. Neither source says the model writes the figures, and we found no public report of AI producing bank audit confirmations. AI written spending reviews that explain a customer's year are a different job, covered on the financial wellbeing coach page.",[177,178,179,180,181,182],"outbound-notice-drafting","account-servicing-execution","portfolio-reporting-and-commentary","fee-and-interest-leakage-detection","correspondence-triage-and-routing","financial-wellbeing-coach","2026-10-13",[185],{"date":183,"note":186},"First published","statement-and-tax-document-generation",[189,216],{"title":190,"useCases":191,"organization":192,"vendors":197,"summary":198,"stage":199,"year":200,"channels":201,"languages":204,"metrics":206,"outcomeDisclosed":194,"sources":207,"verification":211,"grade":213,"id":214,"organizationSlug":215},"Federal Bank: Feddy AI assistant issues certificates and statements over WhatsApp",[187],{"name":193,"anonymized":194,"country":195,"region":196,"industry":17},"Federal Bank",false,"IN","asia-pacific",[],"Federal Bank, an Indian private sector bank, describes Feddy as its AI powered virtual assistant. On WhatsApp, customers can ask Feddy in chat for an account statement or a mini statement, and can generate and download a loan certificate, a deposit interest certificate, a balance confirmation certificate and a TDS certificate. The bank says these functions need no prior registration on the WhatsApp channel, and that Feddy recognises natural requests such as \"I need my account statement\" before authorization and authentication run and the document is returned in the chat. The bank's page gives no volumes, accuracy or time figures, and does not say how the documents are rendered.","production",2026,[202,203],"whatsapp","web-chat",[205],"en",[],[208],{"url":209,"title":210,"publisher":193},"https:\u002F\u002Fwww.federalbank.co.in\u002Ffeddy","Feddy",{"level":212,"checkedAt":183},"source-verified","B","federal-bank-feddy-whatsapp-certificates",null,{"title":217,"useCases":218,"organization":219,"vendors":222,"summary":226,"stage":199,"year":227,"channels":228,"languages":229,"metrics":231,"outcomeDisclosed":239,"sources":240,"verification":244,"grade":245,"id":246,"organizationSlug":215},"Crédit Agricole Bank Polska: AI agent that handles certificate applications",[187],{"name":220,"anonymized":194,"country":221,"region":149,"industry":17},"Crédit Agricole Bank Polska","PL",[223],{"name":224,"role":225},"Deviniti","integrator","Crédit Agricole Bank Polska runs an AI agent, built by the integrator Deviniti, inside its after sales service system. The agent reads incoming letters and forms, classifies them, registers them and routes them to the right team, and carries out simple standard requests on its own. Certificate applications are one of the request types it handles. Deviniti reports that 62 percent of certificate applications are handled solely by the AI agent. The case study does not say which certificates these are (for example interest, tax or balance certificates), how the certificate itself is produced, or how many applications there are.",2024,[32,30],[230],"pl",[232],{"kpi":45,"value":233,"unit":234,"qualifier":235,"claimant":236,"quote":237,"sourceUrl":238},62,"percent","exact","vendor","62% of certificate applications are handled solely by AI Agent","https:\u002F\u002Fdeviniti.com\u002Fsoftware-development-case-studies\u002Fcase-study-ai-agent-at-credit-agricole\u002F",true,[241],{"url":238,"title":242,"publisher":224,"date":243},"Case Study: AI Agent at Credit Agricole","2024-10-01",{"level":212,"checkedAt":183},"C","credit-agricole-bank-polska-certificate-applications",0,[249],{"kpi":45,"label":250,"unit":234,"aggregate":239,"higherIsBetter":239,"n":251,"nUpTo":247,"median":233,"min":233,"max":233,"byClaimant":252,"vendorOnly":239,"points":253},"Automation rate",1,{"organization":247,"vendor":251,"regulator":247,"independent":247},[254],{"evidenceId":246,"organization":220,"value":233,"qualifier":235,"claimant":236,"grade":245,"pooled":239},{"low":256,"high":257},200000.00000000003,1925000,[259,290,307,321,334,356],{"slug":177,"title":260,"shortTitle":261,"definition":262,"status":9,"industries":263,"functions":267,"patterns":271,"audience":274,"autonomy":275,"adoptionStage":276,"segment":33,"evidenceCount":277,"publicEvidenceCount":277,"organizations":278,"bestGrade":213,"headline":284,"lastVerified":289,"indexable":239},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI for outbound notice drafting writes the letters and notices operations must send at scale, such as arrears notices or decline letters. It also covers complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[264,17,19,265,266,18],"cross-industry","government","healthcare",[21,22,268,269,270],"collections-and-recovery","regulatory-compliance","claims",[272,273,28],"content-generation","rag-knowledge-assistant","employee-facing","copilot","early-adopters",6,[279,280,281,282,283],"Acentra Health","Hiscox","Health Resources and Services Administration","Nationwide Building Society","SS&C Technologies",{"kpi":46,"label":285,"unit":234,"n":251,"nUpTo":247,"kind":286,"value":287,"qualifier":235,"claimant":236,"organization":288,"vendorReported":239},"Cycle time reduction","reported",25,"SS&C GIDS and RS","2026-09-26",{"slug":178,"title":291,"shortTitle":292,"definition":293,"status":9,"industries":294,"functions":295,"patterns":297,"audience":33,"autonomy":34,"adoptionStage":276,"segment":33,"evidenceCount":298,"publicEvidenceCount":299,"organizations":300,"bestGrade":213,"headline":303,"lastVerified":306,"indexable":239},"AI for back office account servicing execution","Account servicing execution","AI account servicing execution is AI that reads and acts on servicing requests landing in operations queues, such as address or beneficiary changes, for back office staff. It checks each request against policy and entitlements, then prepares or makes the change, such as mandate changes, standing instructions, reissues, payoff and reference letters and loan maintenance, in core systems under dual control.",[17,19,18],[21,296],"lending-and-credit",[26,25,27],4,3,[301,283,302],"Banco Supervielle","Transamerica",{"kpi":46,"label":285,"unit":234,"n":304,"nUpTo":247,"kind":286,"value":305,"qualifier":235,"claimant":236,"organization":283,"vendorReported":239},2,95,"2026-09-27",{"slug":179,"title":308,"shortTitle":309,"definition":310,"status":9,"industries":311,"functions":312,"patterns":314,"audience":33,"autonomy":275,"adoptionStage":276,"segment":316,"evidenceCount":298,"publicEvidenceCount":299,"organizations":317,"bestGrade":213,"headline":215,"lastVerified":306,"indexable":239},"AI generated client portfolio reports and commentary","Portfolio commentary","Portfolio reporting and commentary is AI that drafts each client's periodic portfolio report and commentary in plain language, for wealth and asset managers. The narrative covers performance, attribution, what drove returns, positioning and outlook, written in the client's language, where every figure comes from the portfolio system of record and a reviewer approves the text before delivery.",[18,17],[313,22,21],"analytics-and-reporting",[272,315,28],"summarization","middle-office",[318,319,320],"AssetMark","Morgan Stanley","Quilter",{"slug":180,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":327,"patterns":329,"audience":33,"autonomy":275,"adoptionStage":35,"segment":33,"evidenceCount":251,"publicEvidenceCount":251,"organizations":331,"bestGrade":213,"headline":215,"lastVerified":333,"indexable":239},"AI for fee and interest leakage detection","Fee and interest leakage","Fee and interest leakage detection is an independent verification layer that checks whether each fee, FX margin, spread and interest charge matches what the contract and pricing tables specify. It recomputes what each charge should have been, compares it with what was actually billed, and surfaces overcharges and undercharges account by account for correction, customer remediation and revenue recovery.",[17,326,264],"payments",[23,328,269,21],"product-and-pricing",[330,26,273],"anomaly-detection",[332],"State Bank of India","2026-09-28",{"slug":181,"title":335,"shortTitle":336,"definition":337,"status":9,"industries":338,"functions":339,"patterns":341,"audience":33,"autonomy":34,"adoptionStage":342,"segment":33,"evidenceCount":343,"publicEvidenceCount":343,"organizations":344,"bestGrade":213,"headline":353,"lastVerified":306,"indexable":239},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI correspondence triage and routing is AI that sorts inbound letters, emails, uploads and secure messages before anyone answers them, so each one reaches the right team. It brings everything into one intake, identifies what each item is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right workflow, replacing the manual sorting desk.",[264,17,19,265],[21,22,340],"case-management",[27,25,315],"mainstream",8,[345,346,347,348,349,350,351,352],"Ecclesia Group","Encova Insurance","Irish Life","Loadsure","The Master Trust Bank of Japan","PKO Bank Polski","Travelers","U.S. Department of Veterans Affairs",{"kpi":354,"label":355,"unit":234,"n":304,"nUpTo":247,"kind":286,"value":305,"qualifier":235,"claimant":236,"organization":347,"vendorReported":239},"accuracy","Accuracy",{"slug":182,"title":357,"shortTitle":358,"definition":359,"status":9,"industries":360,"functions":361,"patterns":363,"audience":367,"autonomy":34,"adoptionStage":276,"segment":368,"evidenceCount":369,"publicEvidenceCount":62,"organizations":370,"bestGrade":213,"headline":215,"lastVerified":306,"indexable":239},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","A financial wellbeing coach is an in app AI assistant that customers open to understand their own money. It uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[17],[22,362],"marketing",[364,365,366,26],"conversational-agent","recommendation-and-personalization","prediction-and-scoring","customer-facing","front-office",12,[371,372,373,374,375,376,377,378,379,380],"Bank of America","Commonwealth Bank of Australia","HSBC","Hyundai Card","KBC","Royal Bank of Canada","Starling Bank","Truist Financial Corporation","Wells Fargo","Westpac",{"indexable":239,"reasons":382},[],[384,390,395,403,410,417,422,427,434,441,448,454,460,467,474,481,487,493,500,505,510,516,522,529,534,539,544,549,555,561,569,574,580,586,592,597],{"id":141,"label":385,"issuer":386,"region":149,"url":387,"description":388,"useCases":389,"indexable":239},"EU AI Act","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":142,"label":391,"issuer":386,"region":149,"url":392,"description":393,"useCases":394,"indexable":239},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":396,"label":397,"issuer":398,"region":399,"url":400,"description":401,"useCases":402,"indexable":239},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":404,"label":405,"issuer":406,"region":155,"url":407,"description":408,"useCases":409,"indexable":239},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":411,"label":412,"issuer":413,"region":149,"url":414,"description":415,"useCases":416,"indexable":239},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":143,"label":418,"issuer":386,"region":149,"url":419,"description":420,"useCases":421,"indexable":239},"DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":144,"label":423,"issuer":148,"region":149,"url":424,"description":425,"useCases":426,"indexable":239},"FCA Consumer Duty","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":428,"label":429,"issuer":430,"region":196,"url":431,"description":432,"useCases":433,"indexable":239},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":435,"label":436,"issuer":437,"region":196,"url":438,"description":439,"useCases":440,"indexable":239},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":442,"label":443,"issuer":444,"region":155,"url":445,"description":446,"useCases":447,"indexable":239},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":449,"label":450,"issuer":386,"region":149,"url":451,"description":452,"useCases":453,"indexable":239},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":455,"label":456,"issuer":457,"region":399,"url":458,"description":459,"useCases":453,"indexable":239},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":461,"label":462,"issuer":463,"region":149,"url":464,"description":465,"useCases":466,"indexable":239},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":468,"label":469,"issuer":470,"region":155,"url":471,"description":472,"useCases":473,"indexable":239},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":475,"label":476,"issuer":477,"region":399,"url":478,"description":479,"useCases":480,"indexable":239},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":482,"label":483,"issuer":386,"region":149,"url":484,"description":485,"useCases":486,"indexable":239},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":488,"label":489,"issuer":490,"region":155,"url":491,"description":492,"useCases":486,"indexable":239},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":494,"label":495,"issuer":496,"region":155,"url":497,"description":498,"useCases":499,"indexable":239},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":501,"label":502,"issuer":386,"region":149,"url":503,"description":504,"useCases":499,"indexable":239},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":506,"label":507,"issuer":386,"region":149,"url":508,"description":509,"useCases":499,"indexable":239},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":511,"label":512,"issuer":513,"region":155,"url":514,"description":515,"useCases":369,"indexable":239},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":517,"label":518,"issuer":519,"region":399,"url":520,"description":521,"useCases":369,"indexable":239},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":523,"label":524,"issuer":525,"region":149,"url":526,"description":527,"useCases":528,"indexable":239},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":530,"label":531,"issuer":386,"region":149,"url":532,"description":533,"useCases":528,"indexable":239},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":535,"label":536,"issuer":386,"region":149,"url":537,"description":538,"useCases":528,"indexable":239},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":540,"label":541,"issuer":430,"region":196,"url":542,"description":543,"useCases":62,"indexable":239},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":545,"label":546,"issuer":386,"region":149,"url":547,"description":548,"useCases":62,"indexable":239},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":550,"label":551,"issuer":552,"region":155,"url":553,"description":554,"useCases":343,"indexable":239},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":556,"label":557,"issuer":386,"region":149,"url":558,"description":559,"useCases":560,"indexable":239},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",7,{"id":562,"label":563,"issuer":564,"region":565,"url":566,"description":567,"useCases":568,"indexable":239},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":570,"label":571,"issuer":386,"region":149,"url":572,"description":573,"useCases":298,"indexable":239},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":575,"label":576,"issuer":577,"region":149,"url":578,"description":579,"useCases":298,"indexable":239},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":581,"label":582,"issuer":583,"region":149,"url":584,"description":585,"useCases":298,"indexable":239},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":587,"label":588,"issuer":589,"region":196,"url":590,"description":591,"useCases":299,"indexable":239},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":593,"label":594,"issuer":386,"region":149,"url":595,"description":596,"useCases":299,"indexable":239},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":598,"label":599,"issuer":600,"region":155,"url":601,"description":602,"useCases":299,"indexable":239},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904359227]