[{"data":1,"prerenderedAt":119},["ShallowReactive",2],{"uc-reg-uk-psr-app-reimbursement":3},{"regulation":4,"includeUnpublished":11,"indexable":12,"useCases":13},{"id":5,"label":6,"issuer":7,"region":8,"url":9,"description":10},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","europe","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.",false,true,[14,45,63,96],{"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":42,"indexable":12,"euAiActTier":43,"euAiActBasis":44},"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.","published",[21,22],"banking","payments",[24,25],"fraud-prevention","operations",[27,28,29,30],"agentic-workflow","classification-and-routing","summarization","prediction-and-scoring","employee-facing","supervised-agent","early-adopters","middle-office",3,2,[38,39],"Coast","SEB","C",null,"2026-09-27","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":46,"title":47,"shortTitle":48,"definition":49,"status":19,"industries":50,"functions":51,"patterns":53,"audience":55,"autonomy":56,"adoptionStage":33,"segment":34,"evidenceCount":35,"publicEvidenceCount":35,"organizations":57,"bestGrade":61,"headline":41,"lastVerified":42,"indexable":12,"euAiActTier":43,"euAiActBasis":62},"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],[24,52],"financial-crime-compliance",[54,30,27,29],"anomaly-detection","back-office","copilot",[58,59,60],"BigPay","ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Reserve Bank Innovation Hub (Reserve Bank of India)","B","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":64,"title":65,"shortTitle":66,"definition":67,"status":19,"industries":68,"functions":69,"patterns":71,"audience":74,"autonomy":32,"adoptionStage":33,"segment":75,"evidenceCount":76,"publicEvidenceCount":76,"organizations":77,"bestGrade":61,"headline":84,"lastVerified":93,"indexable":12,"euAiActTier":94,"euAiActBasis":95},"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],[24,70],"customer-service",[72,30,27,73],"conversational-agent","voice-agent","customer-facing","front-office",6,[78,79,80,81,82,83],"Commonwealth Bank of Australia","Mastercard","Revolut","Starling Bank","Vodafone","Westpac",{"kpi":85,"label":86,"unit":87,"n":36,"nUpTo":88,"kind":89,"value":90,"qualifier":91,"claimant":92,"organization":81,"vendorReported":12},"detection-rate-improvement","Detection improvement","percent",0,"reported",300,"exact","vendor","2026-09-26","limited","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":97,"title":98,"shortTitle":99,"definition":100,"status":19,"industries":101,"functions":102,"patterns":103,"audience":55,"autonomy":104,"adoptionStage":105,"segment":34,"evidenceCount":106,"publicEvidenceCount":106,"organizations":107,"bestGrade":61,"headline":112,"lastVerified":42,"indexable":12,"euAiActTier":43,"euAiActBasis":118},"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],[24],[30,54],"autonomous","mainstream",9,[59,78,79,108,109,80,110,111],"NatWest Group","Pay.UK","Stripe","Visa",{"kpi":113,"label":114,"unit":87,"n":35,"nUpTo":88,"kind":115,"value":116,"qualifier":91,"claimant":117,"organization":41,"vendorReported":11},"fraud-loss-reduction","Fraud loss reduction","median",30,"organization","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.",1790598320166]