[{"data":1,"prerenderedAt":580},["ShallowReactive",2],{"uc-account-servicing-execution":3,"uc-regulations":372},{"useCase":4,"evidence":182,"blitsAiDeployments":249,"benchmarks":250,"indicative":260,"related":263,"indexability":370,"includeUnpublished":188},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":23,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":30,"problem":33,"problemStats":34,"howItWorks":35,"valueDrivers":36,"kpis":41,"indicativeValue":47,"macroEstimates":82,"feasibility":83,"implementation":96,"risk":135,"blitsAi":159,"faq":161,"related":171,"datePublished":177,"dateModified":177,"lastVerified":177,"changelog":178,"slug":181},"AI for back office account servicing execution","Account servicing execution","AI for back office servicing requests","AI agents read servicing requests, check policy and entitlements, and prepare or make changes under dual control, with a value model for the effort saved.","published","AI that executes the servicing requests that land in operations queues, such as address and mandate changes, standing instructions, beneficiary updates, reissues, payoff and reference letters and loan maintenance, by reading the request, checking it against policy and entitlements, and preparing or making the change in core systems under dual control.",[12,13,14,15],"servicing operations automation","back office servicing agent","service request fulfilment AI","operations queue automation",[17,18,19],"banking","insurance","wealth-and-asset-management",[21,22],"operations","lending-and-credit",[24,25,26],"agentic-workflow","document-processing","classification-and-routing",[28,29],"internal-tools","api","back-office","supervised-agent","early-adopters","Behind every servicing channel sits an operations team that does the actual change. A customer\nasks in the app, a branch fills in a form, a letter arrives, or a relationship manager emails a\nrequest: in each case a person in operations keys the new address, updates the direct debit\nmandate, sets up the standing order, orders the replacement cheque book, calculates the payoff\nfigure or changes the repayment date on a loan. The work is repetitive, spread across many\nscreens and subject to dual control, so queues build up when volumes peak.\n\nThis page is about that execution layer. The customer facing conversation (the chatbot or voice\nagent that takes the request and resolves simple ones instantly) is covered on the related page\n\"AI agent for account and card servicing\". Execution is what happens when the request needs documents, checks, calculations or changes in\nsystems that the front end cannot touch directly, whichever channel it came from.",[],"1. **Receive the request from any source.** A handover from the servicing agent, a branch or\n   portal form, a scanned letter or an email becomes one structured service request.\n2. **Read and complete it.** Document AI extracts the fields from attached forms and evidence\n   (proof of address, signed mandate, power of attorney) and flags what is missing.\n3. **Check policy and entitlement.** The agent retrieves the servicing procedure for the request\n   type and checks the requester's authority, signatures, cut off times and limits.\n4. **Prepare or execute.** For low risk changes inside set limits it makes the change through the\n   core system APIs; for changes with financial consequence it prepares the change and the\n   evidence for a second person to approve.\n5. **Confirm and close.** It generates the confirmation or letter (such as a payoff statement),\n   updates the case and tells the originating channel, so the front end can inform the customer.",[37,38,39,40],"cost-to-serve","speed","employee-productivity","risk-reduction",[42,43,44,45,46],"automation-rate","handling-time-reduction","processing-time-reduction","error-reduction","hours-saved",{"referenceOrg":48,"inputs":49,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A retail bank whose operations teams complete 500,000 servicing requests a year",[50,56,63,70],{"key":51,"label":52,"low":53,"high":53,"unit":54,"note":55},"requests","Servicing requests completed by operations per year",500000,"requests per year","The reference bank. Replace with the volume from your operations workflow tool.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"minutesPerRequest","Operator minutes per request today",8,15,"minutes per request","Editorial assumption including checks and dual control. Replace with your own time study.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"effortReduction","Share of operator time the AI removes",0.3,0.6,"fraction of time per request","Editorial assumption; checkers still approve changes with financial consequence.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"costPerHour","Fully loaded operations cost per hour",30,55,"USD per hour","Editorial assumption, replace with your own.","requests * minutesPerRequest / 60 * effortReduction * costPerHour","USD","per year","Operations effort avoided","Labour only. It leaves out faster turnaround for customers, fewer errors and rework, lower complaint volumes and the cost of the platform and core system integration.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":90},"high","Each request type touches different core systems, many of them without clean APIs, and every change with financial consequence needs entitlement checks and dual control that auditors accept. Start with a handful of request types and widen over time.",[87,88,89],"Procedures per request type, with required documents, checks and limits","Request volumes and handling times per type from the operations workflow tool","Signature and mandate records reachable for entitlement checks",[91,92,93,94,95],"Core banking, card and loan servicing systems","Operations workflow or case management tool","Document capture for forms and evidence","Customer communications for confirmations and letters","The front end servicing agent and contact centre for handover and status",{"steps":97,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[98,101,104,107,110],{"title":99,"detail":100},"Pick request types by volume and reversibility","Start with high volume changes that are easy to reverse and have no direct financial effect, such as contact detail updates and statement preferences. Leave payment instructions and loan changes for a later wave.",{"title":102,"detail":103},"Write the procedure as rules the agent can check","For each request type list the required evidence, the entitlement check, the limits and the systems touched. Automation stalls when procedures are unclear.",{"title":105,"detail":106},"Run as a preparer first","Let the AI prepare every change with its evidence while operators still execute. Measure how often the preparation is right before letting it execute anything.",{"title":108,"detail":109},"Separate execute from approve","Allow the agent to execute within limits only for low risk types, and keep a second person on anything that changes where money goes or how much is owed.",{"title":111,"detail":112},"Connect the front end","Feed status back to the customer facing channels so customers and agents stop chasing operations for updates.",[114,115,116,117],"Entitlement and signature checks before any change, with the evidence stored on the case","Dual control on changes to payees, standing instructions, mandates and loan terms","Execution only through an allow list of core system actions with limits per request type","Changes to contact details trigger a notification to the old and the new contact, as a fraud control","Checkers approve every change with financial consequence and every exception the agent flags. Operations leads decide which request types the agent may execute on its own, and a quality team samples completed requests weekly.",[120,121,122,123,124],"Share of requests completed without manual keying, per request type","Median turnaround from receipt to completion","Errors found in quality sampling and by customers","Share of prepared changes that checkers reject","Operator minutes per request",[126,129,132],{"title":127,"detail":128},"Account takeover through a servicing change","A fraudster changes contact details or a payee through a convincing request. Keep strong identity checks and notify both old and new contact details.",{"title":130,"detail":131},"Automation without clean procedures","The agent follows an outdated or ambiguous procedure consistently and at scale. Assign owners and review dates to every procedure it uses.",{"title":133,"detail":134},"Screen based integrations that break","Changes made through fragile screen automation fail silently after a system update. Prefer APIs and monitor completion against the core record.",{"euAiAct":136,"regulations":139,"guidance":146,"controls":153,"incidents":158},{"tier":137,"basis":138},"context-dependent","The tier depends on how the system is built. It stays minimal when the agent only executes changes approved by a person and any letter comes from a fixed template, since executing servicing changes is not listed in Annex III. It moves to limited risk when the same system talks to customers directly (the Article 50 transparency duty, described on the customer facing servicing page) or when generative AI drafts the confirmation or letter text: the provider of that generative function, the bank if it builds the system, then carries the Article 50(2) duty to mark the generated content in a machine readable way, unless the output only gets an assistive role or standard editing that does not substantially alter the input data. An AI system used to evaluate the creditworthiness of natural persons, for example to decide on a loan restructuring, is high risk under Annex III point 5(b); keep that assessment outside this agent, which only executes the decided change.",[140,141,142,143,144,145],"eu-ai-act","gdpr","dora","uk-consumer-duty","apra-cps-230","pci-dss",[147],{"title":148,"issuer":149,"region":150,"url":151,"note":152},"Revisions to the principles for the sound management of operational risk","Basel Committee on Banking Supervision","global","https://www.bis.org/bcbs/publ/d515.htm","The 2021 revision updates the guidance on change management and on information and communication technology; its control and mitigation principles apply equally to changes that an automated agent makes.",[154,155,156,157],"Documented boundary of what the agent may execute on its own, approved by the accountable executive","Full log of every change, the evidence used, and the maker and checker","Change control and regression tests for every new request type","Reconciliation of executed changes against the core system record",[],{"howToBuild":160},"On Blits.ai this is an **agentic workflow** that is started through the API with an API token,\nor from a flow (for example one behind the email channel or the customer facing servicing\nagent) through a **trigger workflow block**. The agent reads\nthe request it is given, retrieves the procedure from the **knowledge base**, and calls\n**custom functions** for the entitlement check and the change in core systems, limited by a\n**tool execution policy**.\n\n**Human in the loop confirmation** holds any change above the configured threshold for a\nchecker, and every run keeps a **full audit trail**. A flow can start the workflow through a\ntrigger workflow block and wait for its result, or confirm completion to the customer by\n**email**. **Test suites** run sample requests against the workflow before a new request type\ngoes live, and **PII masking** at the gateway can mask or redact personal data the model does\nnot need.",[162,165,168],{"question":163,"answer":164},"How is this different from an account and card servicing chatbot?","The chatbot talks to the customer and resolves what it can instantly through front end APIs. This use case is the operations layer behind every channel: it executes the requests that need documents, checks, calculations or changes in core systems, including those that arrive by letter, branch form or handover from the chatbot.",{"question":166,"answer":167},"Which servicing changes can an AI execute without a second person?","Only low risk, reversible changes inside set limits, such as statement preferences or contact details with a notification to both old and new details. Changes to payees, standing instructions, mandates and loan terms keep dual control.",{"question":169,"answer":170},"What results have been published?","SS&C Blue Prism reports a 58% cut in processing time for judicial orders on accounts at Banco Supervielle (average response time from 12 to 5 minutes), and SS&C says its generative AI document agents process loan credit agreements 95% faster than by hand. Both are vendor case studies of processes next to servicing (court orders and loan document intake) rather than customer servicing changes themselves.",[172,173,174,175,176],"account-and-card-servicing-agent","correspondence-triage-and-routing","outbound-notice-drafting","intelligent-document-processing","corporate-account-onboarding-orchestration","2026-09-27",[179],{"date":177,"note":180},"First published","account-servicing-execution",[183,221],{"title":184,"useCases":185,"organization":186,"vendors":191,"summary":195,"stage":196,"year":197,"channels":198,"languages":199,"metrics":201,"outcomeDisclosed":210,"sources":211,"verification":215,"grade":218,"id":219,"organizationSlug":220},"Banco Supervielle: AI and automation for judicial orders on customer accounts",[181],{"name":187,"anonymized":188,"country":189,"region":190,"industry":17},"Banco Supervielle",false,"AR","latin-america",[192],{"name":193,"role":194},"SS&C Blue Prism","platform","Banco Supervielle in Argentina automated the handling of judicial notifications, the court orders that require a bank to freeze, release or transfer funds on customer accounts. Digital workers that use natural language processing and machine learning connect to court systems, identify the type of order, perform the account checks, generate and submit the response letter and close the case. The vendor reports a 58% cut in processing time and full compliance with judicial deadlines. The bank also automated its loan disbursement process.","production",2025,[28],[200],"es",[202],{"kpi":44,"value":203,"unit":204,"qualifier":205,"period":206,"claimant":207,"quote":208,"sourceUrl":209},58,"percent","exact","judicial notification processing, average response time from 12 to 5 minutes","vendor","By combining automation and AI, the bank achieved a 58% reduction in processing time — cutting average response times from 12 minutes to just five — and increased case-handling capacity by 43% during a six-month period.","https://www.blueprism.com/resources/case-studies/banco-supervielle-legal-process-ai-automation/",true,[212],{"url":209,"title":213,"publisher":193,"date":214},"Banco Supervielle | Legal Process AI Automation Case Study | SS&C Blue Prism","2025-12-29",{"level":216,"checkedAt":217},"source-verified","2026-09-26","C","banco-supervielle-judicial-notification-automation",null,{"title":222,"useCases":223,"organization":224,"vendors":227,"summary":230,"stage":196,"year":231,"channels":232,"languages":233,"metrics":235,"outcomeDisclosed":210,"sources":242,"verification":246,"grade":218,"id":247,"organizationSlug":248},"SS&C Technologies: generative AI document agents for loan servicing and address changes",[181],{"name":225,"anonymized":188,"country":226,"region":150,"industry":19},"SS&C Technologies","US",[228],{"name":193,"role":229},"in-house","SS&C Technologies, which provides software and services to wealth and asset management firms and runs fund administration, linked its robotic process automation digital workers to a secure, proprietary large language model so they can read unstructured documents. For loan credit agreements the digital workers ask the model for key terms, validate the answers and enter them into SS&C GoLoans, routing discrepancies to an employee. It reports that credit agreements are now processed in six minutes, 95% faster than by hand, and it uses the same approach for address changes and collateral margin call agreements.",2024,[28],[234],"en",[236],{"kpi":44,"value":237,"unit":204,"qualifier":205,"period":238,"baseline":239,"claimant":207,"quote":240,"sourceUrl":241},95,"loan credit agreement processing, now six minutes","manual review of two hours per document","These digital workers, also known as document agents, now process loan credit agreements in just six minutes — 95% faster than the manual process.","https://www.blueprism.com/resources/case-studies/ssc-ai-lending-loan-unstructured-data/",[243],{"url":241,"title":244,"publisher":193,"date":245},"SS&C Tech | Automation & AI for Unstructured Loan Lending Data | SS&C Blue Prism","2024-09-10",{"level":216,"checkedAt":217},"ssc-technologies-generative-ai-document-agents","ss-and-c-technologies",1,[251],{"kpi":44,"label":252,"unit":204,"aggregate":210,"higherIsBetter":210,"n":253,"nUpTo":254,"median":255,"min":203,"max":237,"byClaimant":256,"vendorOnly":210,"points":257},"Cycle time reduction",2,0,76.5,{"organization":254,"vendor":253,"regulator":254,"independent":254},[258,259],{"evidenceId":247,"organization":225,"value":237,"qualifier":205,"claimant":207,"grade":218,"pooled":210},{"evidenceId":219,"organization":187,"value":203,"qualifier":205,"claimant":207,"grade":218,"pooled":210},{"low":261,"high":262},600000,4125000,[264,291,314,337,357],{"slug":172,"title":265,"shortTitle":266,"definition":267,"status":9,"industries":268,"functions":270,"patterns":272,"audience":276,"autonomy":31,"adoptionStage":277,"segment":278,"evidenceCount":279,"publicEvidenceCount":253,"organizations":280,"bestGrade":283,"headline":284,"lastVerified":177,"indexable":210},"AI agent for account and card servicing","Account and card servicing","An AI agent that resolves routine account and card requests end to end, such as balances, statements, card blocks and replacements, PIN resets and limit changes, across app, web, messaging and phone, and hands anything sensitive or unusual to a human with the full context.",[17,269],"payments",[271,21],"customer-service",[273,274,24,275],"conversational-agent","voice-agent","rag-knowledge-assistant","customer-facing","mainstream","front-office",4,[281,282],"Commonwealth Bank of Australia","DBS Bank","B",{"kpi":285,"label":286,"unit":204,"n":253,"nUpTo":254,"kind":287,"value":288,"qualifier":289,"claimant":290,"organization":282,"vendorReported":188},"containment-rate","Containment rate","reported",90,"approximately","organization",{"slug":173,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":298,"patterns":300,"audience":30,"autonomy":31,"adoptionStage":277,"segment":30,"evidenceCount":302,"publicEvidenceCount":302,"organizations":303,"bestGrade":283,"headline":310,"lastVerified":177,"indexable":210},"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.",[296,17,18,297],"cross-industry","government",[21,271,299],"case-management",[26,25,301],"summarization",6,[304,305,306,307,308,309],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":311,"label":312,"unit":204,"n":249,"nUpTo":254,"kind":287,"value":313,"qualifier":205,"claimant":207,"organization":308,"vendorReported":210},"accuracy","Accuracy",91,{"slug":174,"title":315,"shortTitle":316,"definition":317,"status":9,"industries":318,"functions":320,"patterns":324,"audience":327,"autonomy":328,"adoptionStage":32,"segment":30,"evidenceCount":329,"publicEvidenceCount":329,"organizations":330,"bestGrade":283,"headline":334,"lastVerified":217,"indexable":210},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, 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.",[296,17,18,297,319,19],"healthcare",[21,271,321,322,323],"collections-and-recovery","regulatory-compliance","claims",[325,275,326],"content-generation","translation","employee-facing","copilot",5,[331,332,333,225],"Acentra Health","Hiscox","Health Resources and Services Administration",{"kpi":44,"label":252,"unit":204,"n":249,"nUpTo":254,"kind":287,"value":335,"qualifier":205,"claimant":207,"organization":336,"vendorReported":210},25,"SS&C GIDS and RS",{"slug":175,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":344,"patterns":346,"audience":30,"autonomy":31,"adoptionStage":277,"evidenceCount":348,"publicEvidenceCount":329,"organizations":349,"bestGrade":283,"headline":355,"lastVerified":177,"indexable":210},"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.",[296,297,342,343],"automotive","manufacturing",[21,299,345],"finance-and-accounting",[25,347,26],"computer-vision",7,[350,351,352,353,354],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":311,"label":312,"unit":204,"n":249,"nUpTo":254,"kind":287,"value":288,"qualifier":356,"claimant":207,"organization":350,"vendorReported":210},"at-least",{"slug":176,"title":358,"shortTitle":359,"definition":360,"status":9,"industries":361,"functions":362,"patterns":364,"audience":276,"autonomy":328,"adoptionStage":365,"segment":366,"evidenceCount":253,"publicEvidenceCount":253,"organizations":367,"bestGrade":283,"headline":220,"lastVerified":177,"indexable":210},"AI orchestration of corporate account opening and channel setup","Corporate onboarding operations","An AI agent that runs the operational setup of a corporate client after the due diligence has been approved: it reads mandates, board resolutions and signatory documents, prepares accounts, users, roles and payment entitlements for approval, configures channel access, and chases outstanding items with the client, turning a manual setup that passes between several teams into a tracked, guided flow.",[17],[363,21],"onboarding-and-kyc",[24,25,273,325],"emerging","specialized-businesses",[368,369],"Citi","Standard Chartered",{"indexable":210,"reasons":371},[],[373,380,385,392,400,405,412,418,426,431,437,443,450,456,462,467,474,480,486,492,498,504,510,515,520,527,533,538,543,550,556,562,569,574],{"id":140,"label":374,"issuer":375,"region":376,"url":377,"description":378,"useCases":379,"indexable":210},"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":141,"label":381,"issuer":375,"region":376,"url":382,"description":383,"useCases":384,"indexable":210},"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":386,"label":387,"issuer":388,"region":150,"url":389,"description":390,"useCases":391,"indexable":210},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":393,"label":394,"issuer":395,"region":396,"url":397,"description":398,"useCases":399,"indexable":210},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":142,"label":401,"issuer":375,"region":376,"url":402,"description":403,"useCases":404,"indexable":210},"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":406,"label":407,"issuer":408,"region":376,"url":409,"description":410,"useCases":411,"indexable":210},"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":143,"label":413,"issuer":414,"region":376,"url":415,"description":416,"useCases":417,"indexable":210},"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":419,"label":420,"issuer":421,"region":422,"url":423,"description":424,"useCases":425,"indexable":210},"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":144,"label":427,"issuer":428,"region":422,"url":429,"description":430,"useCases":335,"indexable":210},"APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":145,"label":432,"issuer":433,"region":150,"url":434,"description":435,"useCases":436,"indexable":210},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":438,"label":439,"issuer":440,"region":396,"url":441,"description":442,"useCases":436,"indexable":210},"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":444,"label":445,"issuer":446,"region":376,"url":447,"description":448,"useCases":449,"indexable":210},"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":451,"label":452,"issuer":453,"region":150,"url":454,"description":455,"useCases":60,"indexable":210},"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.",{"id":457,"label":458,"issuer":375,"region":376,"url":459,"description":460,"useCases":461,"indexable":210},"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":463,"label":464,"issuer":375,"region":376,"url":465,"description":466,"useCases":461,"indexable":210},"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":468,"label":469,"issuer":470,"region":396,"url":471,"description":472,"useCases":473,"indexable":210},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":475,"label":476,"issuer":375,"region":376,"url":477,"description":478,"useCases":479,"indexable":210},"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":481,"label":482,"issuer":483,"region":396,"url":484,"description":485,"useCases":479,"indexable":210},"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":487,"label":488,"issuer":489,"region":150,"url":490,"description":491,"useCases":479,"indexable":210},"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":493,"label":494,"issuer":375,"region":376,"url":495,"description":496,"useCases":497,"indexable":210},"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":499,"label":500,"issuer":501,"region":396,"url":502,"description":503,"useCases":497,"indexable":210},"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":505,"label":506,"issuer":421,"region":422,"url":507,"description":508,"useCases":509,"indexable":210},"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":511,"label":512,"issuer":375,"region":376,"url":513,"description":514,"useCases":509,"indexable":210},"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":516,"label":517,"issuer":375,"region":376,"url":518,"description":519,"useCases":509,"indexable":210},"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":521,"label":522,"issuer":523,"region":376,"url":524,"description":525,"useCases":526,"indexable":210},"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":528,"label":529,"issuer":530,"region":396,"url":531,"description":532,"useCases":59,"indexable":210},"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.",{"id":534,"label":535,"issuer":375,"region":376,"url":536,"description":537,"useCases":59,"indexable":210},"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":539,"label":540,"issuer":375,"region":376,"url":541,"description":542,"useCases":302,"indexable":210},"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":544,"label":545,"issuer":546,"region":547,"url":548,"description":549,"useCases":329,"indexable":210},"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":551,"label":552,"issuer":553,"region":376,"url":554,"description":555,"useCases":279,"indexable":210},"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":557,"label":558,"issuer":559,"region":376,"url":560,"description":561,"useCases":279,"indexable":210},"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":563,"label":564,"issuer":565,"region":422,"url":566,"description":567,"useCases":568,"indexable":210},"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":570,"label":571,"issuer":375,"region":376,"url":572,"description":573,"useCases":568,"indexable":210},"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":575,"label":576,"issuer":577,"region":396,"url":578,"description":579,"useCases":568,"indexable":210},"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.",1790598299127]