[{"data":1,"prerenderedAt":556},["ShallowReactive",2],{"uc-chargeback-and-representment":3,"uc-regulations":348},{"useCase":4,"evidence":187,"blitsAiDeployments":249,"benchmarks":250,"indicative":257,"related":260,"indexability":346,"includeUnpublished":192},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":24,"channels":29,"audience":32,"autonomy":33,"adoptionStage":34,"segment":32,"problem":35,"problemStats":36,"howItWorks":42,"valueDrivers":43,"kpis":48,"indicativeValue":54,"macroEstimates":88,"feasibility":89,"implementation":102,"risk":141,"blitsAi":164,"faq":166,"related":176,"datePublished":182,"dateModified":182,"lastVerified":182,"changelog":183,"slug":186},"AI for chargeback and representment operations","Chargeback and representment","AI chargeback and representment automation","AI maps card disputes to reason codes, assembles evidence and drafts representment responses. Stripe says GitHub Sponsors saves 20 hours a month on average.","published","AI that runs the dispute engine room for issuers, acquirers and merchants: it maps each dispute to the network reason code, gathers the matching evidence, assembles a network compliant chargeback or representment package, drafts the rebuttal, tracks every deadline and processes pre dispute alerts so a refund can be issued before a chargeback lands.",[12,13,14,15],"chargeback automation","representment automation","dispute operations AI","card dispute back office",[17,18,19],"payments","banking","retail-and-ecommerce",[21,22,23],"operations","fraud-prevention","customer-service",[25,26,27,28],"agentic-workflow","document-processing","content-generation","classification-and-routing",[30,31],"internal-tools","api","back-office","supervised-agent","early-adopters","Card disputes are a volume problem wrapped in a rulebook. Each network defines its own reason\ncodes, evidence requirements and deadlines, and revises them regularly. For every\ndispute an analyst at the issuer, the acquirer or the merchant has to find the transaction, pull\nauthorisation and 3DS records, delivery or usage evidence and prior correspondence, decide\nwhether to accept or fight, and write the case in the format the network expects, before the\nwindow closes.\n\nMuch of that work is low value but unforgiving. A missed deadline usually means the case is lost,\nweak packages lose winnable cases, and first party misuse (a customer disputing a purchase they\nmade) is hard to separate from genuine fraud. Dispute volumes keep rising, so teams that work by\nhand grow with them. The customer facing side, where a cardholder first reports \"I do not\nrecognise this charge\", is a separate job; this page is about what happens after the case is\nopened.",[37],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"Visa reports that it processed 106 million disputes globally in 2025, a 35% increase since 2019.","Visa Unveils New Services to Modernize Dispute Resolution Process","https://usa.visa.com/about-visa/newsroom/press-releases.releaseId.22261.html",2026,"1. **Ingest and classify.** New disputes, retrieval requests and pre dispute alerts arrive from\n   the network systems. The agent maps each one to the reason code and the applicable rule set.\n2. **Gather the evidence.** It pulls authorisation and clearing data, 3DS and device records,\n   delivery and usage logs, refund history and correspondence, and checks them against the\n   evidence the reason code requires.\n3. **Recommend accept or fight.** It estimates the chance of winning from the evidence and past\n   outcomes, and recommends refunding, accepting or contesting, with reasons.\n4. **Assemble and draft.** For contested cases it builds the package in the network's format and\n   drafts the rebuttal narrative from the evidence.\n5. **Submit and track.** An analyst approves the package where required, the system submits it,\n   tracks each deadline through pre arbitration and arbitration, and records the outcome for\n   learning.",[44,45,46,47],"cost-to-serve","risk-reduction","speed","employee-productivity",[49,50,51,52,53],"automation-rate","handling-time-reduction","hours-saved","cost-reduction","interactions-handled",{"referenceOrg":55,"inputs":56,"formula":83,"currency":84,"period":85,"resultLabel":86,"caveat":87},"A card issuer or acquirer working 200,000 disputes a year",[57,63,70,77],{"key":58,"label":59,"low":60,"high":60,"unit":61,"note":62},"disputes","Disputes worked per year",200000,"disputes per year","The reference organization. Replace with your own dispute volume.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"minutesPerDispute","Analyst minutes per dispute today",15,30,"minutes per dispute","Editorial assumption covering evidence gathering, decision and package. Replace with your own time study.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"effortReduction","Share of analyst time the AI removes",0.2,0.3,"fraction of time per dispute","Editorial assumption, capped at the one handle time figure on this page (a vendor reports nearly 30% lower handle time per assignment); analysts still approve contested cases and write offs. Replace with your own.",{"key":78,"label":79,"low":67,"high":80,"unit":81,"note":82},"costPerHour","Fully loaded dispute analyst cost per hour",55,"USD per hour","Editorial assumption, replace with your own.","disputes * minutesPerDispute / 60 * effortReduction * costPerHour","USD","per year","Dispute handling effort avoided","Labour only. It leaves out recovered losses from better packages and fewer missed deadlines, fees avoided through pre dispute refunds, and the cost of the platform and network integrations.",[],{"complexity":90,"complexityNote":91,"dataPrerequisites":92,"integrations":96},"medium","The networks already provide structured dispute systems and APIs. The work is gathering evidence from many internal systems and keeping the rule logic current with network releases.",[93,94,95],"Dispute history with reason codes, evidence submitted and outcomes","Current network rules and evidence requirements per reason code","Access to authorisation, clearing, 3DS, device and delivery data",[97,98,99,100,101],"Card network dispute systems, such as Visa Resolve Online","Card management and transaction processing systems","Fraud and authentication platforms","Merchant order, delivery and refund systems (acquirer and merchant side)","Pre dispute alert services",{"steps":103,"guardrails":119,"humanInTheLoop":124,"kpisToInstrument":125,"failureModes":131},[104,107,110,113,116],{"title":105,"detail":106},"Segment disputes by reason code and value","Find the reason codes with the most volume and the most avoidable losses. Low value disputes are often best refunded automatically; high value ones need the best packages.",{"title":108,"detail":109},"Build the evidence map","For each reason code list the evidence that wins, where it lives and how to fetch it. This map is the core asset, with or without AI.",{"title":111,"detail":112},"Automate assembly before decisions","Let the AI gather evidence and build packages while analysts decide, then measure package completeness and win rates.",{"title":114,"detail":115},"Add recommendations with thresholds","Allow automatic accept or refund below a value threshold and on alerts, and keep analyst approval for contested and high value cases.",{"title":117,"detail":118},"Keep the rules current","Assign an owner to network rule releases and run regression tests on the rule logic before each release date.",[120,121,122,123],"Rebuttals use only evidence from the case; the model never invents facts or documents","Rules and deadlines come from a maintained rule set, not from the model's memory","Write offs and contested cases above the threshold need analyst approval","Cardholder data masked in prompts and logs in line with PCI DSS","Analysts approve contested cases, high value decisions and write offs, and handle suspected fraud or hardship. A quality team samples automatic accepts and refunds each week, and the rule owner signs off changes when networks publish new rules.",[126,127,128,129,130],"Win rate on contested cases, by reason code","Deadlines missed","Analyst minutes per dispute","Share of disputes resolved by pre dispute alerts or automatic refund","Net losses from disputes as a share of sales or volume",[132,135,138],{"title":133,"detail":134},"Outdated rules","A network rule change invalidates the evidence logic and cases start losing. Version the rule set and test it on every release.",{"title":136,"detail":137},"Fighting everything","Automation makes it cheap to contest, so weak cases are fought and fees rise. Use win probability and value thresholds.",{"title":139,"detail":140},"Invented evidence","A drafted rebuttal describes evidence that is not in the package. Link every statement to an attached document.",{"euAiAct":142,"regulations":145,"guidance":151,"controls":158,"incidents":163},{"tier":143,"basis":144},"minimal","Dispute processing between issuers, acquirers and merchants is not listed in Annex III. It is not an evaluation of creditworthiness or credit scoring under Annex III point 5(b), and because cardholders do not interact with the system directly, the Article 50(1) transparency duty for AI that talks to people does not apply. Article 50(2) marking of generated text is a duty of the provider of the AI system that generates it, which includes an institution that builds its own dispute drafting agent and puts it into service under its own name. A drafted rebuttal built from attached case evidence performs an assistive function for standard editing of that evidence and does not substantially alter the underlying input, so it falls under the Article 50(2) exception and does not need machine readable marking. With that point checked, the tier stays minimal. A customer facing intake agent is assessed separately.",[146,147,148,149,150],"eu-ai-act","pci-dss","gdpr","dora","apra-cps-230",[152],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Visa Core Rules and Visa Product and Service Rules","Visa","global","https://usa.visa.com/content/dam/VCOM/download/about-visa/visa-rules-public.pdf","The public rulebook that defines dispute conditions, evidence and time limits for Visa transactions.",[159,160,161,162],"Versioned rule set per network with an owner and release testing","Deadline tracking with alerts well before each network cut off","Audit trail of evidence, decision, approver and outcome for every dispute","Documented value thresholds for automatic accept and refund",[],{"howToBuild":165},"On Blits.ai this is an **agentic workflow** triggered through the API when a new dispute arrives\nfrom the network systems. **Custom functions** fetch transaction, authentication and delivery\nevidence, the network rules and evidence maps sit in the **knowledge base** with hybrid\nretrieval, and an **AI agent** with **structured output** builds the package and drafts the\nrebuttal. **Agentic tasks** recheck deadlines on a schedule.\n\n**Human in the loop approval** holds contested and high value cases for an analyst. The custom\nfunctions return masked card data, so the agent sees only what each step needs. Each run keeps a\n**full audit trail**, and **test suites** replay past disputes with known outcomes before rule or\nprompt changes go live. A customer facing agent on the same platform can open the dispute and\nshow its status to the cardholder; in that chat, card numbers the cardholder types are detected\nand tokenized at the gateway.",[167,170,173],{"question":168,"answer":169},"How are chargeback operations different from dispute intake?","Intake is the customer facing moment when a cardholder reports a charge and the case is opened. Chargeback and representment is the back office work that follows: reason codes, evidence, network packages, deadlines and arbitration, on the issuer, acquirer and merchant side.",{"question":171,"answer":172},"What results have been published?","Mostly vendor figures. Stripe reports that GitHub Sponsors, using its Smart Disputes product to generate and submit dispute evidence, spends 20 hours a month less on disputes on average, and the dispute platform vendor Quavo reports that institutions using its product cut average handle time per assignment by nearly 30%. Visa announced AI dispute tools for issuers, acquirers and merchants in April 2026, some generally available and some in pilot, without outcome figures.",{"question":174,"answer":175},"Should the AI decide to write off a dispute?","Below an agreed value threshold, automatic accept or refund can make sense, because fighting a small dispute can cost more than it recovers. Above it, and for contested cases, an analyst should approve, because the network rules and the economics of each case differ.",[177,178,179,180,181],"card-dispute-and-chargeback-intake","payment-investigations-and-exceptions","ledger-and-payment-reconciliation","fee-and-interest-leakage-detection","order-status-and-returns-agent","2026-09-27",[184],{"date":182,"note":185},"First published","chargeback-and-representment",[188,217],{"title":189,"useCases":190,"organization":191,"vendors":194,"summary":197,"stage":198,"year":41,"channels":199,"languages":201,"metrics":203,"outcomeDisclosed":192,"sources":204,"verification":211,"grade":214,"id":215,"organizationSlug":216},"Visa: AI dispute resolution services for issuers, acquirers and merchants",[177,186],{"name":154,"anonymized":192,"country":193,"region":155,"industry":17},false,"US",[195],{"name":154,"role":196},"in-house","In April 2026 Visa announced six new and enhanced dispute resolution tools. For issuers and acquirers they include Dispute Intelligence (predictive models that support case by case decisions, generally available), Dispute Doc Analyzer (AI summaries of merchant documents for issuer analysts and auto populated questionnaires for acquirers) and Visa Dispute Case Manager, which unifies dispute workflows from intake to resolution. Merchant tools cover pre dispute handling, generative AI representment responses and Compelling Evidence 3.0 to reduce friendly fraud. Several tools are still in pilot or planned for late 2026; no outcome figures were disclosed.","production",[31,200],"agent-desktop",[202],"en",[],[205,207],{"url":40,"title":39,"publisher":154,"date":206},"2026-04-01",{"url":208,"title":209,"publisher":210,"date":206},"https://www.cnbc.com/2026/04/01/visa-ai-tools-dispute-management.html","Visa launches new AI tools to manage the charge dispute process","CNBC",{"level":212,"checkedAt":213},"source-verified","2026-09-26","B","visa-dispute-resolution-services","visa",{"title":218,"useCases":219,"organization":220,"vendors":224,"summary":228,"stage":198,"year":229,"channels":230,"languages":231,"metrics":232,"outcomeDisclosed":241,"sources":242,"verification":245,"grade":246,"id":247,"organizationSlug":248},"GitHub Sponsors: AI generated chargeback evidence with Stripe Smart Disputes",[186],{"name":221,"anonymized":192,"country":193,"region":222,"industry":223},"GitHub","north-america","technology",[225],{"name":226,"role":227},"Stripe","platform","GitHub Sponsors, the platform through which people fund open source maintainers, uses Stripe Smart Disputes, which generates and submits evidence to contest chargebacks automatically. Before, the team reviewed disputes by hand and rarely contested them because gathering evidence took too long. The Stripe case study reports that the team now saves four to five hours of work a week and headlines an average reduction of 20 hours a month in time spent on disputes.",2025,[31],[202],[233],{"kpi":51,"value":234,"unit":235,"qualifier":236,"period":237,"claimant":238,"quote":239,"sourceUrl":240},20,"hours","exact","per month, on average","vendor","Time spent addressing disputes reduced by 20 hours per month, on average","https://stripe.com/gb/customers/github",true,[243],{"url":240,"title":244,"publisher":226},"Github case study | Stripe",{"level":212,"checkedAt":213},"C","github-sponsors-stripe-smart-disputes",null,0,[251],{"kpi":51,"label":252,"unit":235,"aggregate":192,"higherIsBetter":241,"n":253,"nUpTo":249,"median":234,"min":234,"max":234,"byClaimant":254,"vendorOnly":241,"points":255},"Hours saved",1,{"organization":249,"vendor":253,"regulator":249,"independent":249},[256],{"evidenceId":247,"organization":221,"value":234,"qualifier":236,"claimant":238,"grade":246,"pooled":241},{"low":258,"high":259},300000,1650000,[261,277,296,314,328],{"slug":177,"title":262,"shortTitle":263,"definition":264,"status":9,"industries":265,"functions":266,"patterns":267,"audience":270,"autonomy":33,"adoptionStage":34,"segment":271,"evidenceCount":272,"publicEvidenceCount":273,"organizations":274,"bestGrade":214,"headline":248,"lastVerified":182,"indexable":241},"AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[18,17],[23,22,21],[268,269,28,26,25],"conversational-agent","voice-agent","customer-facing","front-office",4,3,[275,276,154],"Commonwealth Bank of Australia","Klarna",{"slug":178,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":282,"patterns":283,"audience":32,"autonomy":33,"adoptionStage":284,"segment":32,"evidenceCount":285,"publicEvidenceCount":285,"organizations":286,"bestGrade":214,"headline":289,"lastVerified":182,"indexable":241},"AI for payment investigations and exceptions","Payment investigations and exceptions","AI that works the payments that fall out of straight through processing: it reads the failure, repairs or enriches the message, drafts the ISO 20022 or SWIFT investigation, chases the counterparty bank and proposes a return, recall or correction, while an operator approves anything that moves money.",[18,17],[21,23],[25,26,28,27],"emerging",2,[287,288],"BNY","JPMorgan Chase",{"kpi":49,"label":290,"unit":291,"n":253,"nUpTo":249,"kind":292,"value":293,"qualifier":294,"claimant":295,"organization":287,"vendorReported":192},"Automation rate","percent","reported",10,"at-least","organization",{"slug":179,"title":297,"shortTitle":298,"definition":299,"status":9,"industries":300,"functions":305,"patterns":307,"audience":32,"autonomy":33,"adoptionStage":34,"segment":32,"evidenceCount":272,"publicEvidenceCount":272,"organizations":309,"bestGrade":214,"headline":248,"lastVerified":182,"indexable":241},"AI for ledger and payment reconciliation","Ledger and payment reconciliation","AI that matches entries across nostro and vostro statements, card and scheme settlement files, the general ledger and suspense accounts, proposes matches and clearing journals, and routes only the genuine breaks to an operator with a plain language explanation.",[18,17,301,302,303,304],"capital-markets","cross-industry","wealth-and-asset-management","government",[306,21],"finance-and-accounting",[25,308,26],"anomaly-detection",[310,311,312,313],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme",{"slug":180,"title":315,"shortTitle":316,"definition":317,"status":9,"industries":318,"functions":319,"patterns":322,"audience":32,"autonomy":324,"adoptionStage":284,"segment":32,"evidenceCount":253,"publicEvidenceCount":253,"organizations":325,"bestGrade":214,"headline":248,"lastVerified":327,"indexable":241},"AI for fee and interest leakage detection","Fee and interest leakage","An independent verification layer that recomputes what each fee, FX margin, spread and interest charge should have been under the contract and pricing tables, compares it with what was actually billed, and surfaces overcharges and undercharges account by account for correction, customer remediation and revenue recovery.",[18,17,302],[306,320,321,21],"product-and-pricing","regulatory-compliance",[308,25,323],"rag-knowledge-assistant","copilot",[326],"State Bank of India","2026-09-28",{"slug":181,"title":329,"shortTitle":330,"definition":331,"status":9,"industries":332,"functions":333,"patterns":334,"audience":270,"autonomy":33,"adoptionStage":335,"evidenceCount":336,"publicEvidenceCount":336,"organizations":337,"bestGrade":214,"headline":342,"lastVerified":213,"indexable":241},"AI agent for order status, delivery changes and returns","Order status and returns","An AI agent that answers \"where is my order\", changes delivery details and arranges returns, exchanges and refunds end to end for online and omnichannel shoppers, by reading and writing to the order, carrier and returns systems within the retailer's policy, and hands exceptions such as damaged goods, disputes and upset customers to a person.",[302,19,17],[23,21],[268,25,269,323],"mainstream",5,[338,339,276,340,341],"BARK","Best Buy","Next","Sun & Ski Sports",{"kpi":343,"label":344,"unit":291,"n":285,"nUpTo":249,"kind":292,"value":345,"qualifier":236,"claimant":238,"organization":338,"vendorReported":241},"customer-satisfaction","Customer satisfaction",98,{"indexable":241,"reasons":347},[],[349,356,361,368,375,380,387,394,402,408,413,419,426,432,438,443,450,456,462,468,474,480,485,490,495,502,509,514,520,527,533,539,545,550],{"id":146,"label":350,"issuer":351,"region":352,"url":353,"description":354,"useCases":355,"indexable":241},"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":148,"label":357,"issuer":351,"region":352,"url":358,"description":359,"useCases":360,"indexable":241},"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":362,"label":363,"issuer":364,"region":155,"url":365,"description":366,"useCases":367,"indexable":241},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":369,"label":370,"issuer":371,"region":222,"url":372,"description":373,"useCases":374,"indexable":241},"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":149,"label":376,"issuer":351,"region":352,"url":377,"description":378,"useCases":379,"indexable":241},"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":381,"label":382,"issuer":383,"region":352,"url":384,"description":385,"useCases":386,"indexable":241},"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":388,"label":389,"issuer":390,"region":352,"url":391,"description":392,"useCases":393,"indexable":241},"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":395,"label":396,"issuer":397,"region":398,"url":399,"description":400,"useCases":401,"indexable":241},"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":150,"label":403,"issuer":404,"region":398,"url":405,"description":406,"useCases":407,"indexable":241},"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":147,"label":409,"issuer":410,"region":155,"url":411,"description":412,"useCases":234,"indexable":241},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":414,"label":415,"issuer":416,"region":222,"url":417,"description":418,"useCases":234,"indexable":241},"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":420,"label":421,"issuer":422,"region":352,"url":423,"description":424,"useCases":425,"indexable":241},"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":427,"label":428,"issuer":429,"region":155,"url":430,"description":431,"useCases":66,"indexable":241},"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":433,"label":434,"issuer":351,"region":352,"url":435,"description":436,"useCases":437,"indexable":241},"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":439,"label":440,"issuer":351,"region":352,"url":441,"description":442,"useCases":437,"indexable":241},"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":444,"label":445,"issuer":446,"region":222,"url":447,"description":448,"useCases":449,"indexable":241},"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":451,"label":452,"issuer":351,"region":352,"url":453,"description":454,"useCases":455,"indexable":241},"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":457,"label":458,"issuer":459,"region":222,"url":460,"description":461,"useCases":455,"indexable":241},"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":463,"label":464,"issuer":465,"region":155,"url":466,"description":467,"useCases":455,"indexable":241},"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":469,"label":470,"issuer":351,"region":352,"url":471,"description":472,"useCases":473,"indexable":241},"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":475,"label":476,"issuer":477,"region":222,"url":478,"description":479,"useCases":473,"indexable":241},"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":481,"label":482,"issuer":397,"region":398,"url":483,"description":484,"useCases":293,"indexable":241},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":486,"label":487,"issuer":351,"region":352,"url":488,"description":489,"useCases":293,"indexable":241},"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":491,"label":492,"issuer":351,"region":352,"url":493,"description":494,"useCases":293,"indexable":241},"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":496,"label":497,"issuer":498,"region":352,"url":499,"description":500,"useCases":501,"indexable":241},"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":503,"label":504,"issuer":505,"region":222,"url":506,"description":507,"useCases":508,"indexable":241},"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":510,"label":511,"issuer":351,"region":352,"url":512,"description":513,"useCases":508,"indexable":241},"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":515,"label":516,"issuer":351,"region":352,"url":517,"description":518,"useCases":519,"indexable":241},"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.",6,{"id":521,"label":522,"issuer":523,"region":524,"url":525,"description":526,"useCases":336,"indexable":241},"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":528,"label":529,"issuer":530,"region":352,"url":531,"description":532,"useCases":272,"indexable":241},"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":534,"label":535,"issuer":536,"region":352,"url":537,"description":538,"useCases":272,"indexable":241},"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":540,"label":541,"issuer":542,"region":398,"url":543,"description":544,"useCases":273,"indexable":241},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":546,"label":547,"issuer":351,"region":352,"url":548,"description":549,"useCases":273,"indexable":241},"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":551,"label":552,"issuer":553,"region":222,"url":554,"description":555,"useCases":273,"indexable":241},"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.",1790598299361]