[{"data":1,"prerenderedAt":661},["ShallowReactive",2],{"uc-card-dispute-and-chargeback-intake":3,"uc-regulations":459},{"useCase":4,"evidence":230,"blitsAiDeployments":342,"benchmarks":343,"indicative":350,"related":353,"indexability":457,"includeUnpublished":236},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":23,"channels":29,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"problem":38,"problemStats":39,"howItWorks":50,"valueDrivers":51,"kpis":56,"indicativeValue":63,"macroEstimates":105,"feasibility":106,"implementation":120,"risk":166,"blitsAi":206,"faq":208,"related":218,"datePublished":225,"dateModified":225,"lastVerified":225,"changelog":226,"slug":229},"AI agent for card dispute intake","Card dispute intake","AI agent for card dispute and chargeback intake","An AI agent finds the charge, separates fraud from merchant disputes and opens complete cases. Visa processed 106 million disputes in 2025, up 35% since 2019.","published","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.",[12,13,14,15],"transaction dispute chatbot","unrecognised charge assistant","dispute intake agent","chargeback claim intake",[17,18],"banking","payments",[20,21,22],"customer-service","fraud-prevention","operations",[24,25,26,27,28],"conversational-agent","voice-agent","classification-and-routing","document-processing","agentic-workflow",[30,31,32,33],"mobile-app","web-chat","voice","whatsapp","customer-facing","supervised-agent","early-adopters","front-office","Card disputes keep rising, and every one of them starts with a customer who is worried about\nmoney. Many are not fraud at all: Visa's Andrew Torre told CNBC that many disputes start with a\ncardholder who does not recognise a charge on the statement. Others are genuine fraud\nthat needs the card blocked now, and others again are merchant problems (goods not received, a\nrefund that never arrived) that follow the card scheme's dispute rules.\n\nIntake is where much of the later cost and risk starts. A dispute filed under the wrong category,\nor without the details the network requires, can bounce between teams, miss deadlines and end in\na write off. Timelines and refunds are regulated: in the US by Regulation E for debit cards and\nRegulation Z for credit cards, and in the EU by PSD2, which requires a refund of an unauthorised\npayment by the end of the following business day unless the provider has reasonable grounds to\nsuspect fraud. A vague or wrong promise to the customer therefore becomes a compliance issue. The US Consumer Financial Protection Bureau has also\nwarned that chatbots and highly scripted representatives may only recognise a dispute when the\ncustomer uses specific words.",[40,45],{"statement":41,"sourceTitle":42,"sourceUrl":43,"year":44},"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,{"statement":46,"sourceTitle":47,"sourceUrl":48,"year":49},"Salesforce reported in April 2024 that Americans had disputed USD 83 billion in charges the previous year.","Salesforce Launches AI-Powered Capabilities to Help Banks Quickly Resolve Transaction Disputes","https://www.salesforce.com/news/stories/financial-services-cloud-ai-capabilities/",2024,"1. **Recognise the dispute in the customer's own words.** \"I never bought this\", \"they charged me\n   twice\" and \"my refund never came\" are all disputes, whatever the phrasing or language.\n2. **Find the transaction.** In an authenticated session the agent lists recent transactions and\n   enriches the one in question with the merchant's clear name, location and order details.\n   Because many disputes start with a charge the cardholder does not recognise, some of them can\n   be resolved right here, without a case.\n3. **Triage.** The agent separates three paths: unauthorised use (block the card, fraud claim),\n   an authorised purchase that went wrong (merchant dispute), or confusion (explain and close).\n   Signs of a scam, where the customer was tricked into paying, go to the scam team instead.\n4. **Collect what the rules need.** It asks only the questions the chosen path requires (dates,\n   contact with the merchant, cancellation proof) and accepts receipts and screenshots, which\n   document AI reads into structured fields.\n5. **Set expectations from rules, not from the model.** Timelines, provisional credit and next\n   steps come from a deterministic rules engine per product and market, so the promise is right\n   and auditable.\n6. **Open the case.** The agent creates the case in the dispute system with a suggested reason\n   category and the evidence attached, for an analyst to confirm, and gives the customer a\n   reference number.\n7. **Hand over when it matters.** Vulnerable customers, hardship, high values, repeat disputes and\n   anything the agent cannot classify go to a human with the full summary.",[52,53,54,55],"cost-to-serve","customer-experience","compliance","risk-reduction",[57,58,59,60,61,62],"handling-time-reduction","first-contact-resolution","accuracy","automation-rate","customer-satisfaction","interactions-handled",{"referenceOrg":64,"inputs":65,"formula":100,"currency":101,"period":102,"resultLabel":103,"caveat":104},"A card issuer with 1 million active cards",[66,72,79,86,93],{"key":67,"label":68,"low":69,"high":69,"unit":70,"note":71},"activeCards","Active cards",1000000,"cards","The reference issuer.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"disputesPerCard","Disputes raised per card per year",0.01,0.03,"disputes per card per year","Editorial assumption, replace with your own dispute volume.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"agentShare","Share of disputes started with the agent",0.3,0.6,"fraction of disputes","Editorial assumption; depends on how prominent the digital route is in the app and on the phone menu.",{"key":87,"label":88,"low":89,"high":90,"unit":91,"note":92},"minutesSaved","Intake and rework minutes saved per dispute",5,10,"minutes per dispute","Editorial assumption. Quavo reports that its clients cut handle time per assignment by nearly 30% (vendor claim, grade D), which supports a modest saving.",{"key":94,"label":95,"low":96,"high":97,"unit":98,"note":99},"costPerMinute","Fully loaded cost of a dispute agent minute",0.8,1.2,"USD per minute","Editorial assumption, replace with your own fully loaded cost.","activeCards * disputesPerCard * agentShare * minutesSaved * costPerMinute","USD","per year","Dispute intake and rework cost avoided","Counts intake and rework time only. It leaves out write offs avoided through correct classification and on time filing, disputes prevented by explaining unfamiliar charges, the cost of running the AI and the integration work.",[],{"complexity":107,"complexityNote":108,"dataPrerequisites":109,"integrations":114},"medium","The conversation is the easy part. The work is in reading transactions and merchant data in real time, encoding timeline and provisional credit rules per product and market, and writing clean cases into the dispute system that analysts trust.",[110,111,112,113],"Transaction history with enriched merchant names, locations and, where available, order details","Dispute rules per product and market (categories, required information, timelines, provisional credit)","Historical disputes with outcomes, to test triage and the suggested categories","Approved customer wording for rights, timelines and next steps",[115,116,117,118,119],"Card management and core banking (transactions, card block and reissue)","Dispute or case management system (case creation, status, documents)","Merchant data enrichment and network dispute services where the issuer uses them","Identity and step up authentication","Contact centre platform for handover with context",{"steps":121,"guardrails":140,"humanInTheLoop":146,"kpisToInstrument":147,"failureModes":153},[122,125,128,131,134,137],{"title":123,"detail":124},"Map the intake paths","From last year's disputes, list the real paths (fraud, not received, not as described, duplicate, cancelled subscription, unrecognised) with the questions and documents each one needs. This becomes the agent's playbook and your test set.",{"title":126,"detail":127},"Put the rules outside the model","Encode timelines, provisional credit and eligibility per product and market in a rules service. The agent calls it and repeats its answer; it never works out a deadline itself.",{"title":129,"detail":130},"Resolve confusion first","Show the merchant's clear name, logo, location and order details before offering a dispute. Visa's Order Insight service rests on the same idea: surfacing transaction details clears up confusion over legitimate charges before they become disputes.",{"title":132,"detail":133},"Make the case analyst ready","Agree with dispute operations exactly which fields and documents a case needs, and have the agent suggest a category with its reasoning for the analyst to confirm, not apply.",{"title":135,"detail":136},"Route fraud and scams separately","Unauthorised use triggers a card block and the fraud path; a customer who was tricked into paying goes to the scam team, because the rules and the customer's rights differ.",{"title":138,"detail":139},"Pilot on one product and one channel","Start with debit or credit cards in the logged in app, compare case quality and rework against the human channel for a month, then widen.",[141,142,143,144,145],"Timelines, provisional credit and eligibility come only from the rules service, never from the model","Suggested reason categories are confirmed by an analyst before the case is filed with the network","Card numbers are tokenized and personal data masked before any text reaches a model","Automatic handover on vulnerability signals, hardship, suspected scams and high values","The agent never tells a customer a dispute will succeed","Dispute analysts confirm the category, decide on provisional credit where rules leave room, and own every case once it is opened. Specialists take over for scam victims, vulnerable customers and complaints, and a weekly sample of agent opened cases is reviewed for completeness and correct triage.",[148,149,150,151,152],"Share of disputes resolved without a case (confusion explained), with repeat contact within 30 days counted as not resolved","Case completeness at first submission and analyst rework rate","Accuracy of suggested categories against the analyst's final category","Time from first message to case opened","Customer satisfaction on dispute conversations versus the phone channel",[154,157,160,163],{"title":155,"detail":156},"Wrong promises on timelines or credit","The model paraphrases a policy and gets a date or an amount wrong. Keep all such statements in the rules service and test them on every change.",{"title":158,"detail":159},"Scam victims treated as disputes","A customer who authorised a payment under false pretences is pushed into a fraud chargeback that is unlikely to succeed. Train triage on scam signals and route them to specialists.",{"title":161,"detail":162},"Dispute not recognised","The customer describes a problem in words the agent treats as a question, and no dispute is opened. The CFPB names this as a legal risk; test with real, messy phrasing.",{"title":164,"detail":165},"Friendly fraud made easier","A frictionless flow invites false claims. Surface repeat patterns to analysts and keep evidence requirements proportionate to value.",{"euAiAct":167,"regulations":170,"guidance":178,"controls":199,"incidents":205},{"tier":168,"basis":169},"limited","A customer facing assistant must tell people they are interacting with an AI system (Article 50(1)). It triages and opens cases but does not evaluate creditworthiness (Annex III point 5(b), which in any case excludes systems used to detect financial fraud) or decide access to an essential service, so it is not high risk under Annex III.",[171,172,173,174,175,176,177],"eu-ai-act","gdpr","uk-gdpr","pci-dss","uk-consumer-duty","dora","eu-psd2",[179,185,189,193],{"title":180,"issuer":181,"region":182,"url":183,"note":184},"Chatbots in consumer finance","Consumer Financial Protection Bureau","north-america","https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/","Warns that chatbots which fail to recognise and resolve disputes can breach federal consumer financial law.",{"title":186,"issuer":181,"region":182,"url":187,"note":188},"Regulation E, section 1005.11, procedures for resolving errors","https://www.consumerfinance.gov/rules-policy/regulations/1005/11/","Sets the investigation timelines and provisional credit rules for electronic fund transfer errors in the US; the agent's promises must match them.",{"title":190,"issuer":181,"region":182,"url":191,"note":192},"Regulation Z, section 1026.13, billing error resolution","https://www.consumerfinance.gov/rules-policy/regulations/1026/13/","Sets the billing error procedure for US credit cards, including completing the investigation within two complete billing cycles.",{"title":194,"issuer":195,"region":196,"url":197,"note":198},"Payment Services Directive (EU) 2015/2366, Article 73, refunds for unauthorised payment transactions","European Union","europe","https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32015L2366","Requires the payer's provider to refund an unauthorised payment by the end of the following business day, unless it has reasonable grounds to suspect fraud and reports them to the national authority.",[200,201,202,203,204],"AI disclosure at the start of the conversation and a clear route to a person","Versioned rules service for timelines and credit, with change control and tests","Audit trail of every statement about rights, timelines and credit made to the customer","Analyst confirmation of the reason category before network filing","Monitoring of dispute outcomes and complaints by channel",[],{"howToBuild":207},"On Blits.ai the intake runs as a **flow** for the regulated steps (identity check, choosing the\ntransaction, required questions, **receive attachment** for receipts) with an **AI agent**\ninside it for free text understanding and explanations. **Custom functions** call the card\nplatform, the rules service and the case system over REST, so the agent reads transactions,\nblocks a card and opens a case only through the functions it has been given. Approved dispute\npolicy content sits in the **knowledge base** with hybrid retrieval.\n\n**PII masking** and card number tokenization at the gateway keep card data away from the model.\n**Guardrails** check input and output, and **human handover** passes the conversation to the\ncontact centre when triage points to a scam, vulnerability or a high value. **Test suites**\nbuilt from real dispute conversations are run before every change, **execution tracing** shows\nhow each conversation was triaged, and **analytics** track volumes and satisfaction. The same\nagent serves **web chat**, **WhatsApp** and **voice**, and reaches the issuer's mobile app\nthrough the **REST or WebSocket API channel**; the platform is model agnostic.",[209,212,215],{"question":210,"answer":211},"Can an AI agent decide who gets a chargeback?","It should not. The agent collects facts and suggests a category; an analyst confirms it and the card scheme rules decide the outcome. The issuer tools Visa announced in April 2026 follow the same split, with AI supporting analysts through predictions and document summaries rather than deciding cases.",{"question":213,"answer":214},"How is this different from chargeback automation in the back office?","Intake is the customer conversation that creates the case. Chargeback and representment operations work the case afterwards: evidence packages, network filings and deadlines. Good intake makes the back office cheaper because cases arrive complete and correctly classified.",{"question":216,"answer":217},"Do AI assistants already handle disputes in production?","Yes. Klarna says its AI assistant handles disputes alongside refunds and returns, and Commonwealth Bank routes fraud disputes through a guarded, deterministic path inside its AI orchestration. Published results cover customer service as a whole, not disputes alone.",[219,220,221,222,223,224],"chargeback-and-representment","fraud-alert-confirmation","account-and-card-servicing-agent","scam-payment-interception","complaints-handling-agent","agentic-payment-initiation","2026-09-27",[227],{"date":225,"note":228},"First published","card-dispute-and-chargeback-intake",[231,263,310],{"title":232,"useCases":233,"organization":234,"vendors":239,"summary":242,"stage":243,"year":44,"channels":244,"languages":247,"metrics":249,"outcomeDisclosed":236,"sources":250,"verification":257,"grade":260,"id":261,"organizationSlug":262},"Visa: AI dispute resolution services for issuers, acquirers and merchants",[229,219],{"name":235,"anonymized":236,"country":237,"region":238,"industry":18},"Visa",false,"US","global",[240],{"name":235,"role":241},"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",[245,246],"api","agent-desktop",[248],"en",[],[251,253],{"url":43,"title":42,"publisher":235,"date":252},"2026-04-01",{"url":254,"title":255,"publisher":256,"date":252},"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":258,"checkedAt":259},"source-verified","2026-09-26","B","visa-dispute-resolution-services","visa",{"title":264,"useCases":265,"organization":268,"vendors":271,"summary":275,"stage":276,"year":49,"channels":277,"languages":278,"metrics":281,"outcomeDisclosed":298,"sources":299,"verification":307,"grade":260,"id":308,"organizationSlug":309},"Klarna: AI assistant as the first line of customer service",[266,229,267],"first-line-contact-centre-agent","order-status-and-returns-agent",{"name":269,"anonymized":236,"country":270,"region":196,"industry":18},"Klarna","SE",[272],{"name":273,"role":274},"OpenAI","model-provider","Klarna announced in February 2024 that its AI assistant built on OpenAI models had been live globally for a month as the first line of its customer service, handling refunds, returns, payment issues, cancellations and disputes in more than 35 languages across 23 markets. In 2025 the company said it had gone too far in replacing people and began recruiting human agents again so that customers can always reach a person; the assistant still handles the majority of inquiries. The record is useful precisely because it shows both the gain and the correction.","scaled",[30],[248,279,280],"ar","fr",[282,290],{"kpi":62,"value":283,"unit":284,"qualifier":285,"period":286,"claimant":287,"quote":288,"sourceUrl":289},2300000,"count","exact","first month after launch","organization","The AI assistant has had 2.3 million conversations, two-thirds of Klarna’s customer service chats","https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/",{"kpi":291,"value":292,"unit":293,"qualifier":285,"period":294,"baseline":295,"claimant":287,"quote":296,"sourceUrl":297},"response-time-reduction",82,"percent","since launch, as reported in 2025","before the AI assistant","Since launch, response times have improved by 82%, and Klarna has seen a 25% drop in repeat issues.","https://www.customerexperiencedive.com/news/klarna-reinvests-human-talent-customer-service-AI-chatbot/747586/",true,[300,303],{"url":289,"title":301,"publisher":269,"date":302},"Klarna AI assistant handles two-thirds of customer service chats in its first month","2024-02-27",{"url":297,"title":304,"publisher":305,"date":306},"Klarna changes its AI tune and again recruits humans for customer service","CX Dive","2025-05-09",{"level":258,"checkedAt":259},"klarna-ai-assistant-customer-service",null,{"title":311,"useCases":312,"organization":313,"vendors":317,"summary":321,"stage":276,"year":49,"channels":322,"languages":323,"metrics":324,"outcomeDisclosed":298,"sources":333,"verification":338,"grade":339,"id":340,"organizationSlug":341},"Commonwealth Bank: AI orchestration for messaging service with human handoff",[221,229,266],{"name":314,"anonymized":236,"country":315,"region":316,"industry":17},"Commonwealth Bank of Australia","AU","asia-pacific",[318],{"name":319,"role":320},"Microsoft","platform","Commonwealth Bank built a central AI orchestration agent that reads the customer's intent and routes it to a conversational AI, retrieval over public content, a deterministic guarded path for regulated journeys such as fraud disputes, or a human specialist with the full context, on its messaging channel. It migrated nearly 700 chatbot topics and launched a generative AI banking chatbot in November 2024. Voice bots are a planned extension of the orchestration layer.",[],[248],[325],{"kpi":326,"value":327,"unit":293,"qualifier":328,"period":329,"claimant":330,"quote":331,"sourceUrl":332},"containment-rate",84.6,"approximately","May 2026, self service messaging","vendor","In May 2026, approximately 84.6% of self-service messaging interactions were resolved end-to-end in the messaging channel.","https://news.microsoft.com/source/asia/features/how-commonwealth-bank-and-microsoft-are-reimagining-the-future-of-customer-service/",[334],{"url":332,"title":335,"publisher":336,"date":337},"How Commonwealth Bank and Microsoft are reimagining the future of customer service","Microsoft Source Asia","2026-07-08",{"level":258,"checkedAt":225},"C","commonwealth-bank-customer-service-orchestration","commonwealth-bank-of-australia",0,[344],{"kpi":62,"label":345,"unit":284,"aggregate":236,"higherIsBetter":298,"n":346,"nUpTo":342,"median":283,"min":283,"max":283,"byClaimant":347,"vendorOnly":236,"points":348},"Interactions handled",1,{"organization":346,"vendor":342,"regulator":342,"independent":342},[349],{"evidenceId":308,"organization":269,"value":283,"qualifier":285,"claimant":287,"grade":260,"pooled":298},{"low":351,"high":352},12000,216000,[354,368,386,401,418,441],{"slug":219,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":360,"patterns":361,"audience":363,"autonomy":35,"adoptionStage":36,"segment":363,"evidenceCount":364,"publicEvidenceCount":365,"organizations":366,"bestGrade":260,"headline":309,"lastVerified":225,"indexable":298},"AI for chargeback and representment operations","Chargeback and representment","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.",[18,17,359],"retail-and-ecommerce",[22,21,20],[28,27,362,26],"content-generation","back-office",3,2,[367,235],"GitHub",{"slug":220,"title":369,"shortTitle":370,"definition":371,"status":9,"industries":372,"functions":373,"patterns":374,"audience":34,"autonomy":35,"adoptionStage":375,"segment":37,"evidenceCount":89,"publicEvidenceCount":89,"organizations":376,"bestGrade":260,"headline":381,"lastVerified":225,"indexable":298},"AI agent for fraud alert confirmation with cardholders","Fraud alert confirmation","A customer facing AI agent that contacts the cardholder as soon as the fraud engine flags a card transaction, in the channel they actually respond to, verifies them, asks whether they made the transaction and acts on the answer: releasing the block so a retry succeeds, or freezing the card and starting the fraud claim.",[17,18],[21,20],[24,25,28],"emerging",[377,314,378,379,380],"Capital One","Macquarie Bank","Revolut","Westpac",{"kpi":382,"label":383,"unit":293,"n":365,"nUpTo":342,"kind":384,"value":385,"qualifier":285,"claimant":287,"organization":314,"vendorReported":236},"fraud-loss-reduction","Fraud loss reduction","reported",76,{"slug":221,"title":387,"shortTitle":388,"definition":389,"status":9,"industries":390,"functions":391,"patterns":392,"audience":34,"autonomy":35,"adoptionStage":394,"segment":37,"evidenceCount":395,"publicEvidenceCount":365,"organizations":396,"bestGrade":260,"headline":398,"lastVerified":225,"indexable":298},"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.",[17,18],[20,22],[24,25,28,393],"rag-knowledge-assistant","mainstream",4,[314,397],"DBS Bank",{"kpi":326,"label":399,"unit":293,"n":365,"nUpTo":342,"kind":384,"value":400,"qualifier":328,"claimant":287,"organization":397,"vendorReported":236},"Containment rate",90,{"slug":222,"title":402,"shortTitle":403,"definition":404,"status":9,"industries":405,"functions":406,"patterns":407,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"evidenceCount":409,"publicEvidenceCount":409,"organizations":410,"bestGrade":260,"headline":414,"lastVerified":259,"indexable":298},"AI scam intervention for instant payments","Scam payment interception","AI that talks to the customer when they are about to authorise an instant payment that looks like a scam: it combines the payee check and the risk score, asks targeted questions about the payment in plain language, explains the specific scam pattern, and holds, delays or escalates the payment to a human specialist when the risk stays high. Unlike fraud scoring, which stops payments the customer did not make, it protects customers from payments they are being manipulated into making.",[17,18],[21,20],[24,408,28,25],"prediction-and-scoring",6,[314,411,379,412,413,380],"Mastercard","Starling Bank","Vodafone",{"kpi":415,"label":416,"unit":293,"n":365,"nUpTo":342,"kind":384,"value":417,"qualifier":285,"claimant":330,"organization":412,"vendorReported":298},"detection-rate-improvement","Detection improvement",300,{"slug":223,"title":419,"shortTitle":420,"definition":421,"status":9,"industries":422,"functions":426,"patterns":429,"audience":431,"autonomy":432,"adoptionStage":36,"segment":433,"evidenceCount":365,"publicEvidenceCount":365,"organizations":434,"bestGrade":260,"headline":437,"lastVerified":225,"indexable":298},"AI agent for complaints recognition, investigation and response","Complaints handling","An AI agent that recognizes when a customer interaction is a complaint, logs it against the regulatory definition, classifies its root cause and severity, gathers the evidence, drafts the acknowledgement and the response for a human handler to approve, and tracks every statutory deadline until the case is closed.",[423,17,18,424,425],"cross-industry","insurance","telecommunications",[427,20,428],"case-management","regulatory-compliance",[26,430,362,28,393],"summarization","employee-facing","copilot","middle-office",[435,436],"Lloyds Banking Group","NatWest Group",{"kpi":438,"label":439,"unit":440,"n":346,"nUpTo":342,"kind":384,"value":89,"qualifier":328,"claimant":287,"organization":435,"vendorReported":236},"time-saved-per-task","Time saved per task","minutes",{"slug":224,"title":442,"shortTitle":443,"definition":444,"status":9,"industries":445,"functions":446,"patterns":448,"audience":34,"autonomy":35,"adoptionStage":375,"segment":37,"evidenceCount":449,"publicEvidenceCount":450,"organizations":451,"bestGrade":260,"headline":309,"lastVerified":225,"indexable":298},"AI agent for payment initiation within a customer mandate","Agentic payment initiation","An AI agent that initiates and completes payments or purchases on a customer's behalf, within a mandate the customer set in advance (spending caps, allowed merchants or categories, a tokenized credential and rules for when to ask for confirmation), and then confirms and reconciles every transaction it made.",[18,17,359],[20,447,22],"sales",[28,24],8,7,[397,452,453,454,455,456,235],"ING","Majid Al Futtaim","PayPal","Banco Santander","Ulta Beauty",{"indexable":298,"reasons":458},[],[460,465,470,477,484,489,495,501,508,515,521,527,534,541,547,552,559,565,571,577,583,589,594,599,603,610,615,620,625,632,638,644,650,655],{"id":171,"label":461,"issuer":195,"region":196,"url":462,"description":463,"useCases":464,"indexable":298},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":172,"label":466,"issuer":195,"region":196,"url":467,"description":468,"useCases":469,"indexable":298},"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":471,"label":472,"issuer":473,"region":238,"url":474,"description":475,"useCases":476,"indexable":298},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":478,"label":479,"issuer":480,"region":182,"url":481,"description":482,"useCases":483,"indexable":298},"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":176,"label":485,"issuer":195,"region":196,"url":486,"description":487,"useCases":488,"indexable":298},"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":173,"label":490,"issuer":491,"region":196,"url":492,"description":493,"useCases":494,"indexable":298},"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":175,"label":496,"issuer":497,"region":196,"url":498,"description":499,"useCases":500,"indexable":298},"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":502,"label":503,"issuer":504,"region":316,"url":505,"description":506,"useCases":507,"indexable":298},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":509,"label":510,"issuer":511,"region":316,"url":512,"description":513,"useCases":514,"indexable":298},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":174,"label":516,"issuer":517,"region":238,"url":518,"description":519,"useCases":520,"indexable":298},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":522,"label":523,"issuer":524,"region":182,"url":525,"description":526,"useCases":520,"indexable":298},"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":528,"label":529,"issuer":530,"region":196,"url":531,"description":532,"useCases":533,"indexable":298},"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":535,"label":536,"issuer":537,"region":238,"url":538,"description":539,"useCases":540,"indexable":298},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":542,"label":543,"issuer":195,"region":196,"url":544,"description":545,"useCases":546,"indexable":298},"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":548,"label":549,"issuer":195,"region":196,"url":550,"description":551,"useCases":546,"indexable":298},"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":553,"label":554,"issuer":555,"region":182,"url":556,"description":557,"useCases":558,"indexable":298},"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":560,"label":561,"issuer":195,"region":196,"url":562,"description":563,"useCases":564,"indexable":298},"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":566,"label":567,"issuer":568,"region":182,"url":569,"description":570,"useCases":564,"indexable":298},"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":572,"label":573,"issuer":574,"region":238,"url":575,"description":576,"useCases":564,"indexable":298},"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":578,"label":579,"issuer":195,"region":196,"url":580,"description":581,"useCases":582,"indexable":298},"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":584,"label":585,"issuer":586,"region":182,"url":587,"description":588,"useCases":582,"indexable":298},"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":590,"label":591,"issuer":504,"region":316,"url":592,"description":593,"useCases":90,"indexable":298},"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":595,"label":596,"issuer":195,"region":196,"url":597,"description":598,"useCases":90,"indexable":298},"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":177,"label":600,"issuer":195,"region":196,"url":601,"description":602,"useCases":90,"indexable":298},"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":604,"label":605,"issuer":606,"region":196,"url":607,"description":608,"useCases":609,"indexable":298},"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":611,"label":612,"issuer":181,"region":182,"url":613,"description":614,"useCases":449,"indexable":298},"us-ecoa-reg-b","ECOA and Regulation B","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":616,"label":617,"issuer":195,"region":196,"url":618,"description":619,"useCases":449,"indexable":298},"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":621,"label":622,"issuer":195,"region":196,"url":623,"description":624,"useCases":409,"indexable":298},"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":626,"label":627,"issuer":628,"region":629,"url":630,"description":631,"useCases":89,"indexable":298},"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":633,"label":634,"issuer":635,"region":196,"url":636,"description":637,"useCases":395,"indexable":298},"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":639,"label":640,"issuer":641,"region":196,"url":642,"description":643,"useCases":395,"indexable":298},"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":645,"label":646,"issuer":647,"region":316,"url":648,"description":649,"useCases":364,"indexable":298},"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":651,"label":652,"issuer":195,"region":196,"url":653,"description":654,"useCases":364,"indexable":298},"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":656,"label":657,"issuer":658,"region":182,"url":659,"description":660,"useCases":364,"indexable":298},"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.",1790598294343]