[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"uc-reg-uk-consumer-duty":3},{"regulation":4,"includeUnpublished":11,"indexable":12,"useCases":13},{"id":5,"label":6,"issuer":7,"region":8,"url":9,"description":10},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","europe","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",false,true,[14,53,71,89,116,139,172,191,209,223,243,260,273,283,301,314,330,343,365,381,400,412,425,449,465,482,496,511,524,541,553,563,577,590,601,615,636,651,667,686,696,714,728,741,751,764,777],{"slug":15,"title":16,"shortTitle":17,"definition":18,"status":19,"industries":20,"functions":23,"patterns":26,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":35,"publicEvidenceCount":36,"organizations":37,"bestGrade":40,"headline":41,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":52},"account-and-card-servicing-agent","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.","published",[21,22],"banking","payments",[24,25],"customer-service","operations",[27,28,29,30],"conversational-agent","voice-agent","agentic-workflow","rag-knowledge-assistant","customer-facing","supervised-agent","mainstream","front-office",4,2,[38,39],"Commonwealth Bank of Australia","DBS Bank","B",{"kpi":42,"label":43,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":47,"qualifier":48,"claimant":49,"organization":39,"vendorReported":11},"containment-rate","Containment rate","percent",0,"reported",90,"approximately","organization","2026-09-27","limited","Article 50(1): people must be informed that they are interacting with an AI system, unless that is obvious from the context. Servicing existing accounts and cards is not an Annex III use. It would become high risk under Annex III point 5(b) if the agent itself evaluated the creditworthiness of a natural person, for example to decide a credit limit increase.",{"slug":54,"title":55,"shortTitle":56,"definition":57,"status":19,"industries":58,"functions":59,"patterns":60,"audience":31,"autonomy":32,"adoptionStage":61,"segment":34,"evidenceCount":62,"publicEvidenceCount":62,"organizations":63,"bestGrade":40,"headline":65,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":70},"atm-and-self-service-device-assistance","AI agent for ATM and self service device assistance","ATM and device assistance","An AI agent that helps customers with problems at or around ATMs and other self service devices, such as a withdrawal that did not pay out, a retained card, a blocked PIN or finding a working machine with cash, over the app, chat or phone, and that opens and tracks the claim or hands it to a person when it cannot be resolved.",[21],[24,25],[27,28,29],"emerging",1,[64],"NatWest Group",{"kpi":66,"label":67,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":68,"qualifier":69,"claimant":49,"organization":64,"vendorReported":11},"customer-satisfaction-uplift","Satisfaction uplift",150,"exact","A customer facing assistant must tell people they are interacting with an AI system unless that is obvious (Article 50(1)). It does not evaluate creditworthiness (Annex III point 5(b)) or eligibility for public assistance benefits (point 5(a)), so it is not high risk; biometric verification whose sole purpose is to confirm identity is excluded from Annex III point 1(a).",{"slug":72,"title":73,"shortTitle":74,"definition":75,"status":19,"industries":76,"functions":77,"patterns":79,"audience":31,"autonomy":32,"adoptionStage":82,"segment":34,"evidenceCount":35,"publicEvidenceCount":83,"organizations":84,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":88},"card-dispute-and-chargeback-intake","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.",[21,22],[24,78,25],"fraud-prevention",[27,28,80,81,29],"classification-and-routing","document-processing","early-adopters",3,[38,85,86],"Klarna","Visa",null,"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.",{"slug":90,"title":91,"shortTitle":92,"definition":93,"status":19,"industries":94,"functions":98,"patterns":101,"audience":104,"autonomy":105,"adoptionStage":82,"segment":106,"evidenceCount":36,"publicEvidenceCount":36,"organizations":107,"bestGrade":40,"headline":109,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":115},"complaints-handling-agent","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.",[95,21,22,96,97],"cross-industry","insurance","telecommunications",[99,24,100],"case-management","regulatory-compliance",[80,102,103,29,30],"summarization","content-generation","employee-facing","copilot","middle-office",[108,64],"Lloyds Banking Group",{"kpi":110,"label":111,"unit":112,"n":62,"nUpTo":45,"kind":46,"value":113,"qualifier":48,"claimant":49,"organization":108,"vendorReported":11},"time-saved-per-task","Time saved per task","minutes",5,"context-dependent","Complaint handling is not listed in Annex III, so internal classification and drafting for a handler who decides is minimal risk. Where the agent talks to customers to take the complaint, Article 50(1) requires telling them they are dealing with AI. Only a system that also assessed creditworthiness or priced life and health insurance (Annex III point 5(b) or 5(c)) would be high risk for that part.",{"slug":117,"title":118,"shortTitle":119,"definition":120,"status":19,"industries":121,"functions":125,"patterns":127,"audience":31,"autonomy":32,"adoptionStage":82,"segment":128,"evidenceCount":83,"publicEvidenceCount":36,"organizations":129,"bestGrade":132,"headline":133,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":138},"collections-and-hardship-agent","AI agent for early collections and hardship support","Collections and hardship agent","A voice and messaging agent that contacts customers in early arrears and answers their inbound calls, takes payments and sets up payment arrangements within preapproved rules, and recognises signs of hardship or vulnerability so those customers go straight to a trained person.",[95,21,22,97,122,123,124],"energy-and-utilities","automotive","professional-services",[126,24],"collections-and-recovery",[28,27,29,80],"lending",[130,131],"Day Knight & Associates","SameDay Auto Finance","C",{"kpi":134,"label":135,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":136,"qualifier":69,"claimant":137,"organization":131,"vendorReported":12},"cost-reduction","Cost reduction",75,"vendor","A customer facing collections agent must disclose that it is AI (Article 50). It is not listed in Annex III as long as it applies preapproved arrangement rules and does not itself evaluate creditworthiness; an affordability model that decides who gets which arrangement for individuals should be assessed separately against Annex III point 5(b).",{"slug":140,"title":141,"shortTitle":142,"definition":143,"status":19,"industries":144,"functions":148,"patterns":149,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"evidenceCount":150,"publicEvidenceCount":151,"organizations":152,"bestGrade":40,"headline":167,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":171},"first-line-contact-centre-agent","AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[95,21,22,97,145,146,147],"travel-and-hospitality","retail-and-ecommerce","wealth-and-asset-management",[24],[27,28,30,80],25,18,[153,154,155,156,157,38,158,159,85,160,161,64,162,163,164,165,166],"Air India","Airbnb","Bank of America","Bank of the Philippine Islands","BT Group","Ingka Group","JetBlue","Lufthansa Group","Mobily","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":42,"label":43,"unit":44,"n":168,"nUpTo":45,"kind":169,"value":170,"qualifier":69,"claimant":87,"organization":87,"vendorReported":11},7,"median",47,"An AI system that interacts directly with people must be designed so that they know they are dealing with AI, unless that is obvious from the context (Article 50(1)). It is not high risk under Annex III as long as it does not evaluate eligibility for essential public assistance benefits and services (point 5(a)), creditworthiness (point 5(b)), risk and pricing for life and health insurance (point 5(c)) or emergency calls (point 5(d)). This holds only if emotion or vulnerability signals are inferred from what the customer says (text or transcript content), not from voice or other biometric features; an agent that infers emotion from a caller's voice is an emotion recognition system (Article 3(39)), which is high risk under Annex III point 1(c) and triggers the deployer disclosure duty in Article 50(3).",{"slug":173,"title":174,"shortTitle":175,"definition":176,"status":19,"industries":177,"functions":178,"patterns":180,"audience":31,"autonomy":32,"adoptionStage":82,"segment":179,"evidenceCount":113,"publicEvidenceCount":113,"organizations":181,"bestGrade":40,"headline":187,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":190},"claims-first-notice-of-loss-agent","AI agent for first notice of loss claims intake","First notice of loss agent","An AI agent that takes the first notice of loss from a policyholder by phone, chat or app, identifies the policy, collects the facts of the incident and the evidence the claim type needs, opens the claim in the claims system and tells the customer what happens next, handing complex, injured or vulnerable claimants to a human handler.",[96],[179,24],"claims",[27,28,29,81],[182,183,184,185,186],"DOMCURA","Hippo","Lemonade","Progressive","Travelers",{"kpi":188,"label":189,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":47,"qualifier":69,"claimant":137,"organization":182,"vendorReported":12},"accuracy","Accuracy","A customer facing intake agent must be designed so that people know they are interacting with AI (Article 50(1)). Claims intake and claims handling are not listed in Annex III: point 5(c) covers risk assessment and pricing in life and health insurance, not claims. One design choice changes this: detecting distress by inferring emotions from the caller's voice is emotion recognition based on biometric data, which is high risk under Annex III point 1(c) and needs disclosure under Article 50(3). Detecting vulnerability from what the caller says does not. The limited tier assumes that design: every handover signal on this page (injury, distress, anger, vulnerability) is detected from the words of the conversation, and inferring emotions from the voice itself is out of scope.",{"slug":192,"title":193,"shortTitle":194,"definition":195,"status":19,"industries":196,"functions":197,"patterns":198,"audience":31,"autonomy":32,"adoptionStage":61,"segment":34,"evidenceCount":113,"publicEvidenceCount":113,"organizations":199,"bestGrade":40,"headline":204,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":208},"fraud-alert-confirmation","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.",[21,22],[78,24],[27,28,29],[200,38,201,202,203],"Capital One","Macquarie Bank","Revolut","Westpac",{"kpi":205,"label":206,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":207,"qualifier":69,"claimant":49,"organization":38,"vendorReported":11},"fraud-loss-reduction","Fraud loss reduction",76,"Confirming flagged transactions with cardholders is not listed in Annex III, and point 5(b) expressly excludes AI used to detect financial fraud from the creditworthiness category, so the system is not high risk. An agent that messages or calls customers must tell them they are dealing with AI under Article 50(1), and synthetic voice output must be marked as AI generated under Article 50(2).",{"slug":210,"title":211,"shortTitle":212,"definition":213,"status":19,"industries":214,"functions":215,"patterns":216,"audience":104,"autonomy":32,"adoptionStage":82,"segment":106,"evidenceCount":83,"publicEvidenceCount":36,"organizations":218,"bestGrade":132,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":221,"euAiActBasis":222},"fraud-alert-triage","AI agent for fraud alert triage","Fraud alert triage","An AI agent that works the fraud alert queue behind the scenes as the analyst's first pass, without contacting the customer: it enriches each alert with customer, device and payment context, closes clear false positives under documented rules, merges duplicates, and routes genuine risk to an analyst with a drafted rationale.",[21,22],[78,25],[29,80,102,217],"prediction-and-scoring",[219,220],"Coast","SEB","minimal","Internal triage of fraud alerts is not listed in Annex III, and point 5(b) explicitly excludes fraud detection from the high risk creditworthiness category. Article 50(1) covers any system that interacts directly with people, analysts included, but it does not apply where the use of AI is obvious to a reasonably well informed user, as it is in an internal analyst tool; the marking duties for generated content in Article 50(2) sit with the provider. Reassess if its output feeds credit decisions. Decisions that affect customers remain subject to GDPR and consumer protection rules.",{"slug":224,"title":225,"shortTitle":226,"definition":227,"status":19,"industries":228,"functions":229,"patterns":230,"audience":31,"autonomy":32,"adoptionStage":82,"segment":231,"evidenceCount":232,"publicEvidenceCount":232,"organizations":233,"bestGrade":40,"headline":239,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":242},"insurance-policy-servicing-agent","AI agent for insurance policy servicing","Policy servicing agent","An AI agent that answers policyholders' coverage questions from their own policy documents and completes routine policy changes and document requests (address and vehicle changes, adding a named driver or item, payment method updates, certificates and proof of cover) across chat, messaging and phone, and hands anything complex or sensitive to a human with the context.",[96],[24,25],[27,28,30,29],"policy-administration",6,[234,184,235,236,237,238],"LAQO","Nsure.com","Sun Life","Waterdrop","Zurich Insurance (Hong Kong)",{"kpi":42,"label":43,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":240,"qualifier":241,"claimant":49,"organization":184,"vendorReported":11},50,"at-least","A customer facing assistant must be designed so that people know they are interacting with AI (Article 50(1), applicable from 2 August 2026). It is not high risk as long as it does not carry out risk assessment and pricing in relation to natural persons in life and health insurance (Annex III point 5(c)).",{"slug":244,"title":245,"shortTitle":246,"definition":247,"status":19,"industries":248,"functions":249,"patterns":251,"audience":31,"autonomy":32,"adoptionStage":61,"segment":34,"evidenceCount":252,"publicEvidenceCount":168,"organizations":253,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":259},"agentic-payment-initiation","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.",[22,21,146],[24,250,25],"sales",[29,27],8,[39,254,255,256,257,258,86],"ING","Majid Al Futtaim","PayPal","Banco Santander","Ulta Beauty","A customer facing agent must make clear that people are dealing with AI, unless that is obvious from the context (Article 50). Initiating payments within a customer's mandate is not listed in Annex III. It becomes high risk if the same agent evaluates creditworthiness, for example by deciding on a buy now pay later or credit line at checkout (Annex III point 5(b)).",{"slug":261,"title":262,"shortTitle":263,"definition":264,"status":19,"industries":265,"functions":266,"patterns":268,"audience":31,"autonomy":270,"adoptionStage":33,"segment":34,"evidenceCount":252,"publicEvidenceCount":83,"organizations":271,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":272},"offers-and-rewards-agent","AI agent for personalized offers and rewards","Offers and rewards","A customer facing AI agent for banks and card issuers that picks the offer, reward or loyalty action most relevant to each customer at each moment from their transactions and context, delivers it in the app, in messaging or through a colleague, and helps the customer understand, track and redeem rewards in conversation. Unlike campaign personalization, it works inside the customer's own account and loyalty relationship, one moment at a time.",[21,22],[267,250,24],"marketing",[269,217,27],"recommendation-and-personalization","autonomous",[155,38,39],"Ranking offers is generally minimal risk and the conversational part carries the Article 50 transparency duty. Using AI to evaluate creditworthiness for a credit offer is high risk (Annex III point 5(b)), and Article 5 prohibits techniques that exploit vulnerabilities due to a person's social or economic situation to distort their behaviour in a harmful way.",{"slug":274,"title":275,"shortTitle":276,"definition":277,"status":19,"industries":278,"functions":279,"patterns":280,"audience":31,"autonomy":32,"adoptionStage":61,"segment":34,"evidenceCount":83,"publicEvidenceCount":83,"organizations":281,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":282},"proactive-outbound-engagement-agent","AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[21,22],[267,250,24],[27,28,29,269],[155,200,38],"A customer facing agent must disclose that it is AI (Article 50(1)). It stays out of Annex III as long as eligibility for credit offers is decided upstream by the bank's own, separately governed credit processes; if the agent itself assessed creditworthiness it would be high risk under point 5(b).",{"slug":284,"title":285,"shortTitle":286,"definition":287,"status":19,"industries":288,"functions":289,"patterns":290,"audience":31,"autonomy":32,"adoptionStage":61,"segment":179,"evidenceCount":83,"publicEvidenceCount":36,"organizations":292,"bestGrade":40,"headline":295,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":300},"travel-insurance-claims-and-assistance-agent","AI agent for travel insurance claims and assistance","Travel insurance claims and assistance","An AI agent that helps insured travellers around the clock and in their own language: it answers cover questions, takes claims for delays, cancellations, lost baggage and medical costs, reads the receipts and certificates they upload, settles simple claims within set limits, and connects medical emergencies and complex cases to the assistance team at once.",[96,145],[179,24],[27,28,81,29,291],"translation",[293,294],"Allianz Partners","General Insurance Association of Singapore",{"kpi":296,"label":297,"unit":44,"n":45,"nUpTo":62,"kind":46,"value":298,"qualifier":299,"claimant":49,"organization":293,"vendorReported":11},"automation-rate","Automation rate",70,"up-to","A customer facing agent must disclose that it is AI (Article 50), unless this is obvious from the context. Travel insurance claims handling is not listed in Annex III; point 5(c) covers risk assessment and pricing in life and health insurance, not the handling of claims. Handing a traveller who reports a medical emergency to the assistance team is not the classification of emergency calls or the patient triage in point 5(d), as long as the agent only hands over and does not set medical priorities. Claim decisions based solely on automated processing are subject to GDPR Article 22 (and its UK equivalent), and medical data is special category data under Article 9.",{"slug":302,"title":303,"shortTitle":304,"definition":305,"status":19,"industries":306,"functions":307,"patterns":309,"audience":104,"autonomy":105,"adoptionStage":61,"segment":34,"evidenceCount":36,"publicEvidenceCount":36,"organizations":310,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":313},"goal-based-financial-planning-assistant","AI assistant for goal based financial planning","Goal based planning","An AI assistant that turns a client's goals into projections and what if scenarios using a rules based planning engine, explains the trade offs in plain language and prepares the plan for an advisor to validate, with every assumption disclosed and reproducible.",[147,21],[250,24,308],"product-and-pricing",[27,103,29],[311,312],"CIMB Niaga","Vanguard","Planning support for advisors is not listed in Annex III. A client facing version must disclose that the client is talking to AI (Article 50). It becomes high risk if it is used to assess the creditworthiness of individuals (Annex III point 5(b)) or for risk assessment and pricing of life or health insurance for individuals (Annex III point 5(c)).",{"slug":315,"title":316,"shortTitle":317,"definition":318,"status":19,"industries":319,"functions":320,"patterns":322,"audience":104,"autonomy":323,"adoptionStage":82,"segment":324,"evidenceCount":113,"publicEvidenceCount":113,"organizations":325,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":329},"insurance-broker-and-agent-assistant","AI assistant for insurance brokers and agents","Broker and agent assistant","An AI assistant for tied agents, independent brokers, advisors and the insurer's own distribution staff that answers product, underwriting and process questions from approved sources, prepares personalized customer engagement and follow ups, validates and prioritizes leads, and drafts meeting notes and emails, so producers spend more time with customers.",[96],[250,321],"knowledge-management",[30,269,103,102],"assist","distribution",[326,327,236,237,328],"Manulife","Prudential plc","Zurich Insurance Group","An employee facing assistant for knowledge answers and drafting is not listed in Annex III and is minimal risk. A lead qualification agent that talks to customers must tell them they are dealing with AI (Article 50). Using performance insights to monitor and evaluate individual agents, or to allocate leads based on their behaviour or traits, is high risk under Annex III point 4(b), and any component that does risk assessment or pricing of life or health insurance for individuals is high risk under Annex III point 5(c).",{"slug":331,"title":332,"shortTitle":333,"definition":334,"status":19,"industries":335,"functions":336,"patterns":338,"audience":104,"autonomy":105,"adoptionStage":61,"segment":34,"evidenceCount":36,"publicEvidenceCount":36,"organizations":339,"bestGrade":40,"headline":87,"lastVerified":341,"indexable":12,"euAiActTier":114,"euAiActBasis":342},"suitability-assessment-assistant","AI assistant for investment suitability assessment and reports","Suitability assessment","An AI assistant that checks whether a proposed product or portfolio fits a client's risk tolerance, objectives, knowledge, experience and financial situation against the firm's rules, flags mismatches, and drafts the suitability rationale and report for the advisor to confirm, while hard rule failures are decided by deterministic checks, not by the model.",[147,21],[100,250,337],"risk-management",[29,103,80],[340,312],"Morgan Stanley","2026-09-26","Investment suitability assessment is not listed in Annex III, so the tier depends on design. It becomes high risk where the same system assesses creditworthiness, for example for lending against a portfolio (Annex III point 5(b)). MiFID II suitability duties apply regardless of the AI Act tier.",{"slug":344,"title":345,"shortTitle":346,"definition":347,"status":19,"industries":348,"functions":349,"patterns":351,"audience":104,"autonomy":105,"adoptionStage":82,"segment":353,"evidenceCount":113,"publicEvidenceCount":113,"organizations":354,"bestGrade":40,"headline":360,"lastVerified":341,"indexable":12,"euAiActTier":114,"euAiActBasis":364},"insurance-pricing-and-actuarial-copilot","AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[96],[308,337,350],"analytics-and-reporting",[217,352,29,102],"code-generation","pricing",[355,356,357,358,359],"Accelerant Holdings","Europ Assistance","Generali France","Kinsale Capital Group","MAIF",{"kpi":361,"label":362,"unit":363,"n":62,"nUpTo":45,"kind":46,"value":113,"qualifier":69,"claimant":49,"organization":357,"vendorReported":11},"productivity-gain","Productivity gain","multiplier","Pricing and risk assessment of natural persons for life and health insurance is high risk under Annex III point 5(c). Pricing for property and casualty products, and actuarial analysis that does not price individuals, are not listed, although supervisors still expect sound model governance.",{"slug":366,"title":367,"shortTitle":368,"definition":369,"status":19,"industries":370,"functions":372,"patterns":374,"audience":104,"autonomy":105,"adoptionStage":82,"evidenceCount":113,"publicEvidenceCount":83,"organizations":375,"bestGrade":40,"headline":378,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":380},"marketing-content-compliance-copilot","AI copilot for marketing content with compliance pre review","Marketing content and compliance","A copilot that drafts campaign copy, product explainers and social posts on brand and in the customer's language from approved product facts, then runs a first pass compliance check against advertising rules and required disclosures, flagging unsupported claims and missing warnings before a human in marketing compliance approves publication.",[95,21,96,22,147,371],"pharma-and-life-sciences",[267,100,373],"legal",[103,30,80,291],[376,377,85],"Ally Financial","JPMorgan Chase",{"kpi":361,"label":362,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":379,"qualifier":69,"claimant":49,"organization":376,"vendorReported":11},34,"An internal drafting and review aid that makes no decisions about people. Article 50 transparency duties apply to generated content: providers must mark synthetic content, and deployers must disclose deep fake images, audio or video. Personalized targeting of individuals is governed mainly by data protection and consumer law rather than the AI Act.",{"slug":382,"title":383,"shortTitle":384,"definition":385,"status":19,"industries":386,"functions":387,"patterns":390,"audience":391,"autonomy":32,"adoptionStage":82,"segment":128,"evidenceCount":113,"publicEvidenceCount":113,"organizations":392,"bestGrade":40,"headline":87,"lastVerified":341,"indexable":12,"euAiActTier":398,"euAiActBasis":399},"alternative-data-credit-scoring","AI credit scoring with alternative data for thin file applicants","Alternative data credit scoring","A machine learning credit model that adds consumer permissioned alternative data, such as bank account cash flow, rent, utility and telco payments or ecosystem data, to credit bureau data, so a lender can assess applicants with thin or no credit files and return a decision with specific reasons.",[21,22],[388,389,337],"lending-and-credit","underwriting",[217,81,27],"back-office",[393,394,395,396,397],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network","high","Annex III point 5(b): AI systems intended to evaluate the creditworthiness of natural persons or establish their credit score are high risk, except systems used to detect financial fraud. Providers need risk management, data governance, logging and human oversight. Deployers must carry out a fundamental rights impact assessment before use (Article 27), and affected persons have a right to an explanation of individual decisions from the deployer (Article 86).",{"slug":401,"title":402,"shortTitle":403,"definition":404,"status":19,"industries":405,"functions":406,"patterns":407,"audience":104,"autonomy":105,"adoptionStage":61,"segment":128,"evidenceCount":36,"publicEvidenceCount":36,"organizations":408,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":411},"adverse-action-explanations","AI drafted explanations for credit declines and adverse actions","Adverse action explanations","An assistant that turns the reason codes of a credit model into an accurate, specific and readable explanation of a decline, reduced limit or repricing for the customer, and a matching internal rationale for the file, without adding any reason the model did not produce.",[21,22],[388,100,24],[103,30,27],[409,410],"Discover Financial Services","Wells Fargo","The drafting assistant does not assess creditworthiness, so on its own it is not the Annex III point 5(b) credit scoring system. It helps the lender meet the Article 86 right of affected people to a clear and meaningful explanation of decisions based on such a high risk system. If it is built into the scoring system it shares that system's high risk obligations; as a separate drafting tool its tier depends on its design and on how its output is reviewed. The follow up chat assistant must tell customers they are dealing with an AI system (Article 50).",{"slug":413,"title":414,"shortTitle":415,"definition":416,"status":19,"industries":417,"functions":418,"patterns":419,"audience":31,"autonomy":32,"adoptionStage":82,"segment":34,"evidenceCount":252,"publicEvidenceCount":232,"organizations":420,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":424},"financial-wellbeing-coach","AI financial wellbeing coach in the banking app","Financial wellbeing coach","An in app AI assistant that the customer opens to understand their own money: it uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[21],[24,267],[27,269,217,29],[155,38,421,422,423,203],"Hyundai Card","Royal Bank of Canada","Starling Bank","The conversational assistant carries the Article 50 transparency duty: customers must be told they are interacting with an AI system. The system becomes high risk if it is used to evaluate the creditworthiness of natural persons or establish their credit score (Annex III point 5(b)). Article 5(1)(b) prohibits AI that exploits vulnerabilities due to a person's specific social or economic situation to materially distort their behaviour in a way that causes, or is reasonably likely to cause, significant harm.",{"slug":426,"title":427,"shortTitle":428,"definition":429,"status":19,"industries":430,"functions":432,"patterns":434,"audience":391,"autonomy":32,"adoptionStage":82,"segment":34,"evidenceCount":232,"publicEvidenceCount":232,"organizations":437,"bestGrade":40,"headline":444,"lastVerified":341,"indexable":12,"euAiActTier":114,"euAiActBasis":448},"application-and-identity-fraud-detection","AI for application and identity fraud detection","Application and identity fraud","AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.",[21,22,95,431,97],"government",[78,433,388],"onboarding-and-kyc",[81,435,436,217],"anomaly-detection","computer-vision",[438,439,440,441,442,443],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":445,"label":446,"unit":363,"n":62,"nUpTo":45,"kind":46,"value":447,"qualifier":69,"claimant":49,"organization":441,"vendorReported":11},"detection-rate-improvement","Detection improvement",2.5,"Annex III point 5(b) excludes AI used to detect financial fraud from the high risk credit scoring category, but a system that in effect decides on creditworthiness is high risk, and remote biometric identification is high risk under point 1(a), which excludes one to one biometric verification. When a public authority uses the model on claims for public benefits, point 5(a) can apply, because it covers AI used to grant, reduce, revoke or reclaim benefits and has no fraud exception. Keep fraud detection separate from the credit or eligibility decision and use biometrics only for one to one verification.",{"slug":450,"title":451,"shortTitle":452,"definition":453,"status":19,"industries":454,"functions":455,"patterns":456,"audience":391,"autonomy":32,"adoptionStage":82,"segment":391,"evidenceCount":83,"publicEvidenceCount":36,"organizations":457,"bestGrade":132,"headline":460,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":464},"account-servicing-execution","AI for back office account servicing execution","Account servicing execution","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.",[21,96,147],[25,388],[29,81,80],[458,459],"Banco Supervielle","SS&C Technologies",{"kpi":461,"label":462,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":463,"qualifier":69,"claimant":137,"organization":459,"vendorReported":12},"processing-time-reduction","Cycle time reduction",95,"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.",{"slug":466,"title":467,"shortTitle":468,"definition":469,"status":19,"industries":470,"functions":471,"patterns":472,"audience":391,"autonomy":32,"adoptionStage":82,"segment":179,"evidenceCount":473,"publicEvidenceCount":168,"organizations":474,"bestGrade":40,"headline":479,"lastVerified":341,"indexable":12,"euAiActTier":114,"euAiActBasis":481},"claims-triage-and-straight-through-processing","AI for claims triage and straight through processing","Claims triage and STP","AI that reads each new insurance claim and its documents, scores its complexity, cover questions, fraud and recovery signals, sends it to the right handling path and handler, and settles simple, low risk claims end to end within set limits without a person touching them.",[96],[179,25],[80,217,81,29,102],9,[475,293,476,184,477,478,186],"Admiral Seguros","Hiscox","Sedgwick","Tokio Marine & Nichido Fire Insurance",{"kpi":296,"label":297,"unit":44,"n":62,"nUpTo":62,"kind":46,"value":480,"qualifier":48,"claimant":49,"organization":184,"vendorReported":11},55,"Claims handling as such is not listed in Annex III. The same system becomes high risk when it is also used for risk assessment and pricing of natural persons in life and health insurance (point 5(c)), or when it is used by or on behalf of a public authority to grant, reduce, revoke or reclaim essential public assistance benefits and services, including healthcare services (point 5(a)). Otherwise the tier is minimal, so the design and the operator decide. Decisions on claims based solely on automated processing are also subject to Article 22 of the GDPR and the UK GDPR.",{"slug":483,"title":484,"shortTitle":485,"definition":486,"status":19,"industries":487,"functions":488,"patterns":489,"audience":391,"autonomy":105,"adoptionStage":61,"segment":490,"evidenceCount":83,"publicEvidenceCount":83,"organizations":491,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":221,"euAiActBasis":495},"complaints-root-cause-analysis","AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[95,21,96,22,97,431],[100,24,350],[80,102,29,30],"second-line",[492,493,494],"Centers for Medicare and Medicaid Services","Board of Governors of the Federal Reserve System","Federal Trade Commission","Analysing complaints in aggregate to find causes is not listed in Annex III, is not a practice prohibited by Article 5 and does not decide on individuals. It does not interact with the public, so the disclosure duty in Article 50(1) does not apply; the machine readable marking of generated text in Article 50(2) is a duty of the provider of the generative model or system that writes the summaries. If the same system decided individual complaint outcomes or redress, or its themes were used to evaluate the performance of individual complaint handlers (Annex III point 4), that design would need its own assessment.",{"slug":497,"title":498,"shortTitle":499,"definition":500,"status":19,"industries":501,"functions":503,"patterns":504,"audience":104,"autonomy":105,"adoptionStage":82,"segment":391,"evidenceCount":113,"publicEvidenceCount":113,"organizations":505,"bestGrade":40,"headline":508,"lastVerified":341,"indexable":12,"euAiActTier":51,"euAiActBasis":510},"outbound-notice-drafting","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.",[95,21,96,431,502,147],"healthcare",[25,24,126,100,179],[103,30,291],[506,476,507,459],"Acentra Health","Health Resources and Services Administration",{"kpi":461,"label":462,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":150,"qualifier":69,"claimant":137,"organization":509,"vendorReported":12},"SS&C GIDS and RS","Drafting letters for human approval is not listed in Annex III. The decision the letter communicates may come from a separate high risk system, such as credit scoring (Annex III point 5(b)) or a public body's eligibility decision on benefits (point 5(a)); the drafting tool does not make that decision. Article 50(2) requires the provider of an AI system that generates text to mark the output as artificially generated, which puts this on the limited risk (transparency) tier; this includes an organization that builds its own drafting tool. Article 50(2) does not apply where the AI has only an assistive function for standard editing and does not substantially alter the input data or the semantics of the output.",{"slug":512,"title":513,"shortTitle":514,"definition":515,"status":19,"industries":516,"functions":517,"patterns":519,"audience":391,"autonomy":105,"adoptionStage":61,"segment":391,"evidenceCount":62,"publicEvidenceCount":62,"organizations":520,"bestGrade":40,"headline":87,"lastVerified":522,"indexable":12,"euAiActTier":221,"euAiActBasis":523},"fee-and-interest-leakage-detection","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.",[21,22,95],[518,308,100,25],"finance-and-accounting",[435,29,30],[521],"State Bank of India","2026-09-28","Verifying charges against contracts is not listed in Annex III and is not a practice prohibited by Article 5. The system is internal, so the Article 50(1) duty to tell people they are dealing with AI does not arise; the Article 50(2) duty to mark generated text, such as the discrepancy explanations, falls on the provider of the generative model or system. It supports, but does not take, decisions about individual customers; remediation decisions stay with people.",{"slug":525,"title":526,"shortTitle":527,"definition":528,"status":19,"industries":529,"functions":530,"patterns":531,"audience":391,"autonomy":32,"adoptionStage":33,"segment":391,"evidenceCount":232,"publicEvidenceCount":232,"organizations":532,"bestGrade":40,"headline":538,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":540},"correspondence-triage-and-routing","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.",[95,21,96,431],[25,24,99],[80,81,102],[533,534,535,536,186,537],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","U.S. Department of Veterans Affairs",{"kpi":188,"label":189,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":539,"qualifier":69,"claimant":137,"organization":186,"vendorReported":12},91,"It depends on where the system runs. Classifying and routing a bank's or insurer's correspondence is not a use listed in Annex III, so it is minimal risk: the AI literacy duty of Article 4 applies, and the Article 50 duty to tell people they are dealing with AI does not, because the system does not interact with the sender. Used by or for a public authority in a benefits process covered by Annex III point 5(a), the provider can treat it as not high risk only while it performs a narrow procedural or preparatory task under Article 6(3); the provider must then document that assessment before it goes live (Article 6(4)) and register the system in the EU database (Article 49(2)). If the system evaluates eligibility for benefits or profiles the people who write in, it is high risk, so those judgements stay with people.",{"slug":542,"title":543,"shortTitle":544,"definition":545,"status":19,"industries":546,"functions":547,"patterns":548,"audience":391,"autonomy":323,"adoptionStage":33,"segment":179,"evidenceCount":113,"publicEvidenceCount":113,"organizations":549,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":552},"claims-fraud-detection","AI for insurance claims fraud detection","Claims fraud detection","AI that scores every insurance claim for fraud from first notice of loss onwards, combining claim, policy, document, image and network data to find suspicious claims, organised rings and inflated losses, and sends each alert with its reasons to a claims handler or special investigations unit for review.",[96],[179,78],[435,217,81,436,80],[550,551,294,184,478],"Assurant","AXA Switzerland","Claims fraud detection by an insurer is not listed in Annex III, and point 5(b) explicitly excludes AI systems used to detect financial fraud from the credit scoring category. Point 5(c) covers only risk assessment and pricing in life and health insurance, so a fraud model becomes high risk when it also feeds those decisions, or when it is used by or on behalf of a public authority to grant, reduce, revoke or reclaim public assistance benefits (point 5(a)). Profiling and automated decisions remain subject to GDPR, including Article 22 where a claim is refused on a decision based solely on automated processing.",{"slug":554,"title":555,"shortTitle":556,"definition":557,"status":19,"industries":558,"functions":559,"patterns":560,"audience":391,"autonomy":105,"adoptionStage":61,"segment":324,"evidenceCount":83,"publicEvidenceCount":83,"organizations":561,"bestGrade":40,"headline":87,"lastVerified":341,"indexable":12,"euAiActTier":114,"euAiActBasis":562},"insurance-renewal-and-retention","AI for insurance renewal processing and customer retention","Renewal and retention","AI that prepares and runs the renewal cycle: it digitizes renewal submissions and changes in risk for underwriters, flags policies at risk of lapsing or leaving, prepares the renewal conversation and answers customers' renewal questions, while renewal prices stay governed by the insurer's pricing rules and fair value obligations.",[96],[389,24,250],[217,81,27,269],[476,235,328],"Renewal intake for commercial lines and outreach are not listed in Annex III. Renewal risk assessment or pricing for life or health insurance of natural persons is high risk under point 5(c), and so is a lapse score that feeds those decisions; a lapse score used only to decide who gets a service call is not listed. Customer facing renewal assistants carry the Article 50(1) duty to tell people they are interacting with an AI system, unless that is obvious from the context.",{"slug":564,"title":565,"shortTitle":566,"definition":567,"status":19,"industries":568,"functions":569,"patterns":571,"audience":391,"autonomy":105,"adoptionStage":82,"segment":106,"evidenceCount":83,"publicEvidenceCount":83,"organizations":572,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":221,"euAiActBasis":576},"mule-network-detection","AI for money mule account and network detection","Mule network detection","Graph and behavioural machine learning that finds money mule accounts and the networks around them, such as circular flows, layering chains and clusters of newly linked accounts, and supports investigators in tracing scam proceeds and restricting accounts before the money is gone.",[21,22],[78,570],"financial-crime-compliance",[435,217,29,102],[573,574,575],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)","Detecting mule accounts is fraud and AML detection by a private firm, which Annex III does not list; point 5(b) explicitly excludes systems used to detect financial fraud from the credit scoring category. Restricting an account based solely on an automated score can be a decision with similarly significant effects under GDPR Article 22, so keep a human decision and a route to challenge.",{"slug":578,"title":579,"shortTitle":580,"definition":581,"status":19,"industries":582,"functions":583,"patterns":584,"audience":31,"autonomy":32,"adoptionStage":82,"segment":179,"evidenceCount":113,"publicEvidenceCount":113,"organizations":585,"bestGrade":132,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":589},"photo-based-damage-assessment","AI for photo based damage assessment in insurance claims","Photo damage assessment","Computer vision that assesses damage from photos or video of a vehicle or property taken by the policyholder, a repairer or an adjuster, identifies the damaged parts and the repair or replace decision, produces or checks the repair estimate, and flags total losses and inconsistencies for a person to review.",[96],[179],[436,217,29],[475,586,587,588,478],"Covéa","Foyer","PZU","Assessing damage to vehicles or property for property and casualty claims is not listed in Annex III, which covers insurance only for risk assessment and pricing of natural persons in life and health insurance (point 5(c)). Article 50(1) transparency duties apply when the customer interacts directly with the AI, for example a guided photo journey that returns an AI estimate or offer, or a chat agent. A purely internal repairer estimate review with no customer interaction is minimal. A settlement or refusal decided solely by automated processing can fall under GDPR Article 22.",{"slug":591,"title":592,"shortTitle":593,"definition":594,"status":19,"industries":595,"functions":596,"patterns":597,"audience":391,"autonomy":105,"adoptionStage":82,"segment":106,"evidenceCount":83,"publicEvidenceCount":36,"organizations":598,"bestGrade":132,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":600},"portfolio-reporting-and-commentary","AI generated client portfolio reports and commentary","Portfolio commentary","AI that drafts each client's periodic portfolio commentary and report narrative (performance, attribution, what drove returns, positioning and outlook) in plain language and in the client's language, where every figure comes from the portfolio system of record and a reviewer approves the text before delivery.",[147,21],[350,24,25],[103,102,291],[340,599],"Quilter","Drafting client reports for human review is not listed in Annex III and is not a practice prohibited by Article 5, so the tier turns on the firm's role under Article 50. A firm that deploys a third party generator (for example a feature of its portfolio platform) for private client reports has no Article 50 duty: the Article 50(4) disclosure duty covers AI generated text published to inform the public on matters of public interest, which private client reports are not, and it lapses anyway after human review under editorial responsibility. For that firm the tier is minimal. A firm that builds the generating system or places it on the market under its own name is a provider under Article 50(2) and must mark the synthetic text in a machine readable format; drafting whole commentaries goes beyond the exemption for an assistive function for standard editing, so for that firm the tier is limited.",{"slug":602,"title":603,"shortTitle":604,"definition":605,"status":19,"industries":606,"functions":608,"patterns":609,"audience":31,"autonomy":32,"adoptionStage":82,"segment":34,"evidenceCount":83,"publicEvidenceCount":83,"organizations":610,"bestGrade":132,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":614},"home-loan-assistant-and-prequalification","AI home loan assistant with pre qualification","Home loan assistant","A customer facing assistant that answers home loan questions (rates, loan to value, fees, the documents needed), runs indicative affordability and borrowing estimates from the bank's published rules, and books the customer with a mortgage specialist, grounded in the bank's current, versioned product and policy documents.",[21,607],"real-estate",[388,250,24],[30,27,28],[611,612,613],"Figure","Loft","Safe Rate","Answering questions and giving indicative estimates from published rules is limited risk with an Article 50(1) disclosure that the customer is talking to an AI system. If the assistant evaluates an individual's creditworthiness to decide or filter access to a loan, it falls under Annex III point 5(b) and is high risk.",{"slug":616,"title":617,"shortTitle":618,"definition":619,"status":19,"industries":620,"functions":622,"patterns":623,"audience":391,"autonomy":32,"adoptionStage":33,"evidenceCount":252,"publicEvidenceCount":168,"organizations":624,"bestGrade":40,"headline":631,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":635},"personalized-marketing-at-scale","AI marketing personalization at scale","Marketing personalization at scale","AI that runs marketing campaigns at the level of the individual: it decides for each customer which product, offer, message or content to show next across email, app, web and paid media, and generates the matching copy and creative variants within brand and compliance rules. It is the marketing team's engine across many campaigns and channels, not an agent that converses with the customer.",[95,145,621,146,21],"media-and-entertainment",[267,250],[269,217,103],[625,626,38,627,628,629,630],"Amazon","Catchtable","Radisson Hotel Group","Square Enix","Swarovski","Virgin Voyages",{"kpi":632,"label":633,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":634,"qualifier":69,"claimant":137,"organization":626,"vendorReported":12},"conversion-rate-uplift","Conversion uplift",30,"Most personalization and content generation is minimal risk. Providers of systems that generate synthetic audio, image, video or text content must mark the output as artificially generated, and deployers must disclose deep fakes (Article 50(2) and 50(4)). Personalization that deploys manipulative or deceptive techniques, or exploits vulnerabilities due to age, disability or a specific social or economic situation, in a way that causes or is reasonably likely to cause significant harm, is prohibited under Article 5(1)(a) and (b). Using AI to assess creditworthiness or to price life and health insurance is high risk under Annex III point 5(b) and 5(c) and belongs on its own page. Outside the AI Act, the FCA Consumer Duty applies only to FCA regulated firms (the financial services slice of this use case), and the Telephone Consumer Protection Act applies only to campaigns delivered by call or text message in the US.",{"slug":637,"title":638,"shortTitle":639,"definition":640,"status":19,"industries":641,"functions":642,"patterns":643,"audience":104,"autonomy":105,"adoptionStage":33,"segment":34,"evidenceCount":232,"publicEvidenceCount":232,"organizations":645,"bestGrade":40,"headline":648,"lastVerified":50,"indexable":12,"euAiActTier":221,"euAiActBasis":650},"client-meeting-notes-and-crm-update","AI meeting notes and CRM update for wealth advisors","Advisor meeting notes","An AI notetaker for wealth advisors that turns a client advice meeting, recorded with the client's consent, into the file note, follow up message and CRM record the firm needs to evidence its advice; unlike a general meeting summarizer, its output becomes part of the regulated client record. It drafts a structured note with the client's goals, circumstances, decisions and action items, and writes it into the CRM once the advisor has approved it.",[147,21],[250,100,25],[102,644,29,103],"speech-analytics",[155,646,340,599,220,647],"Commerzbank","UniSuper",{"kpi":361,"label":362,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":649,"qualifier":69,"claimant":137,"organization":220,"vendorReported":12},15,"Transcribing and summarizing meetings for an employee is not a use listed in Annex III, and the advisor reviews every note before it is filed or sent. The tier would change if the tool inferred emotions: emotion recognition is high risk under Annex III point 1(c), and inferring the emotions of employees at work is prohibited under Article 5(1)(f). Both stay out of scope.",{"slug":652,"title":653,"shortTitle":654,"definition":655,"status":19,"industries":656,"functions":657,"patterns":658,"audience":104,"autonomy":323,"adoptionStage":82,"segment":34,"evidenceCount":113,"publicEvidenceCount":113,"organizations":659,"bestGrade":40,"headline":662,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":666},"next-best-action-for-advisors","AI next best action prompts for wealth advisors","Advisor next best action","An AI engine for wealth advisors, not customers, that scans an advisor's whole book and surfaces a short, ranked list of client specific prompts, such as idle cash, a maturing deposit, a concentration to review, a life event or an early sign of attrition, each with the reasoning and data behind it, for the advisor to act on or dismiss.",[147,21],[250,267,350],[269,217,103],[311,660,377,340,661],"Citi","UBS",{"kpi":663,"label":664,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":665,"qualifier":69,"claimant":49,"organization":661,"vendorReported":11},"employee-adoption","Employee adoption",80,"Ranking investment and service prompts for an advisor is not listed in Annex III. It becomes high risk if the system evaluates the creditworthiness of natural persons, for example to decide which clients are offered lending (Annex III point 5(b)), so keep credit decisions out of the prompt engine. It is also high risk if the system itself is used to monitor or evaluate advisors' performance and behaviour, for example by scoring or ranking advisors on how they act on prompts (Annex III point 4(b)), so keep adoption reporting separate from performance management.",{"slug":668,"title":669,"shortTitle":670,"definition":671,"status":19,"industries":672,"functions":673,"patterns":674,"audience":391,"autonomy":32,"adoptionStage":82,"evidenceCount":113,"publicEvidenceCount":113,"organizations":675,"bestGrade":132,"headline":681,"lastVerified":50,"indexable":12,"euAiActTier":398,"euAiActBasis":685},"call-quality-and-compliance-monitoring","AI quality and compliance monitoring of every customer interaction","Call quality and compliance","Automated quality assurance that transcribes and scores every customer interaction, voice and chat, against the organization's own rubric, checking required disclosures and script adherence, flagging conduct and mis selling risk, and surfacing coaching opportunities, instead of the small sample a human QA team can review.",[95,21,96,122,97,146],[24,100,25],[644,80,102],[676,677,678,679,680],"British Gas","Central Bank","DoorDash","Oportun","VitalityHealth",{"kpi":682,"label":683,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":684,"qualifier":48,"claimant":137,"organization":676,"vendorReported":12},"quality-score-uplift","Quality score uplift",10,"Scoring individual agents' interactions to monitor and evaluate their performance and behaviour falls under Annex III point 4(b), employment and worker management. The Article 6(3) exception does not apply where the system profiles natural persons. Inferring agents' emotions is prohibited under Article 5(1)(f), except for medical or safety reasons. Inferring customers' emotions from their voice is emotion recognition on biometric data: high risk under Annex III point 1(c), and Article 50(3) requires informing the people exposed to it. Analytics that only aggregate interaction themes without evaluating individuals can fall outside the high risk category.",{"slug":687,"title":688,"shortTitle":689,"definition":690,"status":19,"industries":691,"functions":692,"patterns":693,"audience":104,"autonomy":105,"adoptionStage":61,"segment":128,"evidenceCount":36,"publicEvidenceCount":62,"organizations":694,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":695},"loan-restructuring-recommendations","AI recommendations for loan restructuring and hardship arrangements","Restructuring recommendations","An assistant that assembles a stressed borrower's position, tests restructuring options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, and recommends the best fit with a written rationale for a person to approve.",[21],[126,388,337],[29,30,81,269],[38],"Recommending restructuring terms for individuals involves assessing their ability to pay, which can amount to evaluating the creditworthiness of natural persons under Annex III point 5(b). Human approval alone does not remove that: the Article 6(3) exception covers only systems that do not materially influence the decision, such as a narrow procedural or preparatory task, and never applies when the system profiles natural persons. A tool that only assembles the case file can fall under the exception; restructuring for companies is outside point 5(b).",{"slug":697,"title":698,"shortTitle":699,"definition":700,"status":19,"industries":701,"functions":703,"patterns":704,"audience":104,"autonomy":105,"adoptionStage":33,"evidenceCount":232,"publicEvidenceCount":232,"organizations":705,"bestGrade":40,"headline":712,"lastVerified":50,"indexable":12,"euAiActTier":51,"euAiActBasis":713},"email-and-ticket-reply-drafting","AI reply drafting for customer email and support tickets","Email and ticket reply drafting","A copilot for asynchronous service work that drafts the reply to an incoming customer email, message or ticket once it has reached an agent: it summarizes the request, pulls the relevant customer data and approved knowledge, and drafts a reply in the organization's tone and the customer's language for the agent to check, edit and send. Live calls and chats, and the sorting of the inbox itself, are separate use cases.",[95,431,21,97,702],"technology",[24,25],[103,102,30,80],[706,707,708,709,710,711],"Centers for Disease Control and Prevention","First National Bank","HYPE","Nomad eSIM","Transportation Security Administration","Turing",{"kpi":461,"label":462,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":240,"qualifier":48,"claimant":137,"organization":708,"vendorReported":12},"A drafting copilot whose output an agent reviews and sends falls under the transparency tier at most. When replies are sent without human review, customers interact with the AI system directly, and Article 50(1) requires that they are informed unless this is obvious from the context. Article 50(2) separately requires the provider of a system that generates text to mark its output in a machine readable format as artificially generated, whether or not a person reviews the draft. It becomes high risk only if it is used for a purpose listed in Annex III, such as evaluating eligibility for public benefits or creditworthiness (point 5), or evaluating the performance of the agents who use it (point 4).",{"slug":715,"title":716,"shortTitle":717,"definition":718,"status":19,"industries":719,"functions":720,"patterns":722,"audience":104,"autonomy":323,"adoptionStage":82,"evidenceCount":83,"publicEvidenceCount":83,"organizations":723,"bestGrade":40,"headline":725,"lastVerified":341,"indexable":12,"euAiActTier":114,"euAiActBasis":727},"conversation-roleplay-training","AI roleplay training for customer conversations","Conversation roleplay training","A training simulator in which generative AI plays a realistic customer, by voice or text, so service, sales and crisis staff can rehearse difficult conversations as often as they need before they handle live ones, and receive structured feedback against the organization's own standards.",[95,21,96,97,431,502],[721,24,250],"human-resources",[27,28,103],[155,724,537],"GoHealth",{"kpi":632,"label":633,"unit":44,"n":62,"nUpTo":45,"kind":46,"value":726,"qualifier":69,"claimant":137,"organization":724,"vendorReported":12},21,"Used only for practice and feedback, the simulator is limited risk. Article 50 requires that people know they are interacting with AI unless that is obvious from the context, as it usually is in a training session, and the provider must mark synthetic voice or text output as AI generated in a machine readable format. It becomes high risk under Annex III point 4(b) if its scores are used to evaluate the performance of workers or to decide on their promotion or termination, and can fall under point 3(b) when a vocational training institution uses it to evaluate learning outcomes. Inferring trainees' emotions from voice or face in the workplace is prohibited under Article 5(1)(f), except for medical or safety reasons.",{"slug":729,"title":730,"shortTitle":731,"definition":732,"status":19,"industries":733,"functions":734,"patterns":735,"audience":31,"autonomy":32,"adoptionStage":82,"segment":34,"evidenceCount":232,"publicEvidenceCount":232,"organizations":736,"bestGrade":40,"headline":738,"lastVerified":341,"indexable":12,"euAiActTier":51,"euAiActBasis":740},"scam-payment-interception","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.",[21,22],[78,24],[27,217,29,28],[38,737,202,423,166,203],"Mastercard",{"kpi":445,"label":446,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":739,"qualifier":69,"claimant":137,"organization":423,"vendorReported":12},300,"Annex III point 5(b) expressly excludes AI systems used to detect financial fraud from the high risk creditworthiness category, so the scoring is not high risk. The conversational part must disclose that it is AI under Article 50(1). If a voice component infers the customer's emotions from their voice, it becomes an emotion recognition system under Annex III point 1(c), which is high risk and needs the Article 50(3) notice, so keep coaching detection to what is said rather than to biometric signals.",{"slug":742,"title":743,"shortTitle":744,"definition":745,"status":19,"industries":746,"functions":747,"patterns":748,"audience":31,"autonomy":32,"adoptionStage":82,"segment":324,"evidenceCount":62,"publicEvidenceCount":62,"organizations":749,"bestGrade":40,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":750},"conversational-insurance-quote-and-buy","Conversational AI for insurance quote and buy","Conversational quote and buy","A customer facing AI agent that sells insurance directly in a conversation: it asks the rating questions in plain language, explains cover options, returns a price from the insurer's rating engine, handles objections and takes payment to bind the policy, with a licensed human available for advice and anything outside its limits.",[96],[250,24],[27,29,269,28],[184],"The conversational layer carries the Article 50 transparency duty. If the system assesses risk or sets prices for life or health insurance of natural persons, that part is high risk under Annex III point 5(c); pricing for property and casualty products is not listed.",{"slug":752,"title":753,"shortTitle":754,"definition":755,"status":19,"industries":756,"functions":757,"patterns":758,"audience":31,"autonomy":32,"adoptionStage":82,"segment":34,"evidenceCount":232,"publicEvidenceCount":113,"organizations":759,"bestGrade":132,"headline":87,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":763},"conversational-loan-application-intake","Conversational AI for loan application intake","Loan application intake","A conversational assistant on web, app, messaging or voice that explains loan products, captures the application through dialogue in the customer's language, checks documents and basic eligibility rules, and hands a complete, structured application to origination, without making the credit decision.",[21],[388,250,24],[27,81,30,28],[760,611,108,761,762],"Absa Bank","Oper Credits","Rocket Mortgage","Explaining products and capturing an application is limited risk with an Article 50 disclosure. If the assistant evaluates creditworthiness or filters applicants on its own assessment, it falls under Annex III point 5(b) and becomes high risk, so keep the decision in the governed credit process.",{"slug":765,"title":766,"shortTitle":767,"definition":768,"status":19,"industries":769,"functions":770,"patterns":771,"audience":104,"autonomy":323,"adoptionStage":33,"evidenceCount":168,"publicEvidenceCount":113,"organizations":772,"bestGrade":40,"headline":775,"lastVerified":50,"indexable":12,"euAiActTier":114,"euAiActBasis":776},"live-agent-assist","Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[95,21,96,97,502,146,702],[24,25],[644,30,102,103],[39,773,679,220,774],"Definity","SIGNAL IDUNA",{"kpi":361,"label":362,"unit":44,"n":36,"nUpTo":45,"kind":46,"value":649,"qualifier":69,"claimant":137,"organization":773,"vendorReported":12},"As a pure assist tool for agents it is minimal risk; the customer does not interact with the AI. It becomes high risk under Annex III point 4(b) if its data is used to monitor and evaluate individual agents' performance, and inferring agents' emotions at work is prohibited under Article 5(1)(f).",{"slug":778,"title":779,"shortTitle":780,"definition":781,"status":19,"industries":782,"functions":783,"patterns":784,"audience":391,"autonomy":270,"adoptionStage":33,"segment":106,"evidenceCount":473,"publicEvidenceCount":473,"organizations":785,"bestGrade":40,"headline":788,"lastVerified":50,"indexable":12,"euAiActTier":221,"euAiActBasis":789},"real-time-fraud-scoring","Real time fraud scoring for card and instant payments","Real time fraud scoring","Machine learning that decides in milliseconds, without any conversation, how likely each card authorization and account to account payment is to be fraudulent, combining behavioural, device and network signals, so the bank can approve, challenge or block a payment before the money leaves. Working the resulting alerts and talking to the customer about them are separate use cases.",[21,22],[78],[217,435],[574,38,737,64,786,202,787,86],"Pay.UK","Stripe",{"kpi":205,"label":206,"unit":44,"n":83,"nUpTo":45,"kind":169,"value":634,"qualifier":69,"claimant":49,"organization":87,"vendorReported":11},"Annex III point 5(b) lists creditworthiness assessment and credit scoring of natural persons as high risk but explicitly excludes AI systems used for the purpose of detecting financial fraud, and payment fraud scoring is not otherwise listed in Annex III or prohibited by Article 5. Behavioural biometrics used only to confirm that customers are who they claim to be fall under the biometric verification exclusion in Annex III point 1(a). The model does not interact with people, so Article 50 does not apply. GDPR Article 22 can still apply to solely automated declines with significant effects on customers.",1790598319257]