[{"data":1,"prerenderedAt":717},["ShallowReactive",2],{"uc-scam-payment-interception":3,"uc-regulations":516},{"useCase":4,"evidence":216,"blitsAiDeployments":403,"benchmarks":404,"indicative":425,"related":428,"indexability":514,"includeUnpublished":222},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":18,"patterns":21,"channels":26,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":41,"valueDrivers":42,"kpis":46,"indicativeValue":52,"macroEstimates":85,"feasibility":86,"implementation":100,"risk":146,"blitsAi":191,"faq":193,"related":203,"datePublished":210,"dateModified":210,"lastVerified":211,"changelog":212,"slug":215},"AI scam intervention for instant payments","Scam payment interception","AI scam intervention questions customers before a risky payment and holds it for a specialist. Revolut and Commonwealth Bank report lower scam losses.","published","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.",[11,12,13,14],"APP scam intervention","authorised push payment scam warning","dynamic scam warnings","scam payment friction",[16,17],"banking","payments",[19,20],"fraud-prevention","customer-service",[22,23,24,25],"conversational-agent","prediction-and-scoring","agentic-workflow","voice-agent",[27,28,29],"mobile-app","web-chat","voice","customer-facing","supervised-agent","early-adopters","front-office","In an authorised push payment scam the customer sends the money themselves, usually after a\nconvincing story: a romance, an investment, a fake invoice, a caller posing as the bank. The\npayment passes every authentication check because the real customer makes it, and on instant\npayment rails such as Faster Payments in the UK or the New Payments Platform in Australia it\nleaves the account in seconds.\n\nA generic \"are you sure?\" warning is easy to click through for a customer who is being guided by\na scammer, which is why Revolut and Starling both describe their tools as breaking the scammer's\n\"spell\". At the same time regulators are moving the cost onto banks. In the UK, payment firms must\nreimburse most APP scam victims on Faster Payments and CHAPS, with the cost split 50:50 between\nthe sending and receiving firm. Singapore's Shared Responsibility Framework requires banks and\ntelcos to pay phishing scam victims when they breach set duties, and Australia's Scams Prevention\nFramework sets obligations to prevent, detect, disrupt and respond to scams, next to the banks'\nown Scam-Safe Accord. That makes the quality of the intervention, and the record of it, a\nfinancial and a regulatory question.",[36],{"statement":37,"sourceTitle":38,"sourceUrl":39,"year":40},"UK Finance data cited by Starling Bank show that Britons lost GBP 576.4 million to authorised push payment fraud in 2025, an increase of 19% on the previous year.","New AI feature detects romance scammers, investment heists and deepfake phishing attempts","https://www.starlingbank.com/news/new-ai-feature-detects-romance-scammers/",2026,"1. **Score the payment in real time.** The fraud and scam models, the Confirmation of Payee or\n   name check result, mule account signals on the payee and the customer's own behaviour produce\n   a risk level before the payment is sent.\n2. **Choose the intervention by risk.** Low risk payments go straight through. Medium risk gets a\n   warning specific to the payment's purpose, not a generic one. High risk opens a short\n   conversation in the app.\n3. **Ask, listen and explain.** The agent asks why the customer is paying, how they met the\n   payee and who suggested the payment, looks for signs of coaching or urgency, and explains the\n   matching scam pattern in plain words. Starling's in app assistant does this for transfers a\n   customer describes, and Revolut runs a similar flow for card payments its model has declined.\n4. **Hold and escalate.** When the risk stays high the payment is held and the customer is offered\n   a call with a scam specialist. On the call, an assistant can transcribe and flag indicators for\n   the banker, as Westpac reported piloting in 2025.\n5. **Decide within policy.** The agent can release low and medium risk payments after the\n   conversation; releasing a held high risk payment, or declining it, is a human decision with a\n   documented reason.\n6. **Record everything.** Every warning shown, every answer given and every override is stored,\n   because reimbursement and shared responsibility regimes ask what the bank did and when.\n7. **Learn.** Confirmed scams and false alarms flow back into the models and into the questions\n   the agent asks.",[43,44,45],"risk-reduction","customer-experience","compliance",[47,48,49,50,51],"fraud-loss-reduction","detection-rate-improvement","false-positive-reduction","customer-satisfaction","interactions-handled",{"referenceOrg":53,"inputs":54,"formula":80,"currency":81,"period":82,"resultLabel":83,"caveat":84},"A retail bank with 1 million digitally active customers",[55,60,67,74],{"key":56,"label":57,"low":58,"high":58,"unit":56,"note":59},"customers","Digitally active customers",1000000,"The reference bank.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"scamLossPerCustomer","Scam losses per customer per year",1,4,"USD per customer per year","Editorial assumption, replace with your own reported scam losses divided by active customers.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"lossReduction","Reduction in scam losses from better intervention",0.1,0.3,"fraction of scam losses","Conservative against the evidence on this page. Revolut reports a 30% fall in card scam losses for investment scams; Commonwealth Bank's 76% fall since the peak covers its whole program, not the intervention alone.",{"key":75,"label":76,"low":77,"high":78,"unit":72,"note":79},"bankBorneShare","Share of scam losses the bank bears",0.5,0.9,"Editorial assumption; depends on the reimbursement regime in your market and your own policy.","customers * scamLossPerCustomer * lossReduction * bankBorneShare","USD","per year","Scam losses borne by the bank that are avoided","Counts avoided losses the bank would carry. It leaves out the losses customers avoid, the cost of added friction on genuine payments, specialist call time, the cost of the models and the reputational effect.",[],{"complexity":87,"complexityNote":88,"dataPrerequisites":89,"integrations":94},"high","The intervention sits in the payment path, so it must answer within the payment's latency budget, work with the fraud engine and payee checks, and hold a payment without breaking payment scheme rules. The conversation design and the evidence trail matter as much as the model.",[90,91,92,93],"Labelled scam cases by typology (romance, investment, purchase, impersonation, invoice)","Payee check results and mule account intelligence","Payment purpose and customer behaviour signals in real time","Approved scam education content per typology",[95,96,97,98,99],"Payment initiation in the app and online banking, with the ability to hold or delay","Fraud and scam scoring engine","Confirmation of Payee or equivalent name check service","Contact centre platform for specialist calls with context","Case management for held payments and reimbursement claims",{"steps":101,"guardrails":120,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":133},[102,105,108,111,114,117],{"title":103,"detail":104},"Start from your scam typologies","Take last year's confirmed scams, group them by typology and value, and write for each the signals, the questions that expose it and the words that land with a customer.",{"title":106,"detail":107},"Tier the interventions","Agree risk bands with the fraud team and a different response for each: no friction, a tailored warning, a conversation, a hold with a call. Measure how many genuine payments each band touches.",{"title":109,"detail":110},"Design the conversation with victims","Test the questions with people who were scammed; Starling designed its romance scam feature with advice from a romance scam survivor. Scripts written by fraud analysts alone tend to sound like accusations.",{"title":112,"detail":113},"Wire the hold and the specialist route","Make sure a held payment has an owner, a service level and a callback, and that the specialist sees the conversation so the customer does not repeat the story.",{"title":115,"detail":116},"Build the evidence trail","Store each warning, answer and override with timestamps in a form your reimbursement and complaints teams can retrieve per payment.",{"title":118,"detail":119},"Run it as a champion and challenger test","Compare scam losses, cancelled payments and complaints between the new intervention and the current warnings on a random split before full rollout.",[121,122,123,124,125],"Releasing a held high risk payment or declining it requires a human with a recorded reason","The agent never asks for passcodes, card details or remote access, and says so","Warnings and questions come from approved content per typology","Vulnerability signals route to a specialist rather than to more automated questions","Latency budget and a safe default when the scoring service is unavailable","Scam specialists handle every held high risk payment and decide on release, delay or decline, with the conversation in front of them. The fraud team reviews new detection rules before they go live, as Commonwealth Bank does with its detection agent, and samples released payments that later turned out to be scams.",[128,129,130,131,132],"Scam losses per million payments, by typology, against a control group","Share of high risk payments cancelled after the intervention","Share of genuine payments that received friction, and their abandonment rate","Time to specialist contact for held payments","Reimbursement claims where the record shows no effective warning",[134,137,140,143],{"title":135,"detail":136},"Warning fatigue","Too many warnings on genuine payments train customers to click through. Keep friction for the risk bands that justify it and measure how often genuine customers see it.",{"title":138,"detail":139},"The scammer coaches the answers","Victims are often told what to say. Ask questions that are hard to script, watch for coaching signals and escalate to a human rather than accepting a clean answer.",{"title":141,"detail":142},"Held payments with no owner","A hold without a fast specialist call angers genuine customers and pushes them to other banks. Staff the queue before switching the hold on.",{"title":144,"detail":145},"No usable evidence trail","The bank did intervene but cannot show it per payment. Store the exact warning and the answers, not just a flag.",{"euAiAct":147,"regulations":150,"guidance":161,"controls":184,"incidents":190},{"tier":148,"basis":149},"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.",[151,152,153,154,155,156,157,158,159,160],"eu-ai-act","gdpr","uk-consumer-duty","dora","mas-ai-risk-management","apra-cps-230","eu-psd2","uk-psr-app-reimbursement","mas-shared-responsibility-framework","au-scams-prevention-framework",[162,168,174,179],{"title":163,"issuer":164,"region":165,"url":166,"note":167},"APP scams","Payment Systems Regulator","europe","https://www.psr.org.uk/our-work/app-scams/","UK reimbursement requirement for APP scam victims paying by Faster Payments or CHAPS, with costs split 50:50 between sending and receiving firms and most victims reimbursed within five business days.",{"title":169,"issuer":170,"region":171,"url":172,"note":173},"Guidelines on Shared Responsibility Framework","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/regulation/guidelines/guidelines-on-shared-responsibility-framework","Assigns duties to financial institutions and telcos to mitigate phishing scams and requires payouts to victims where those duties are breached; in force since 16 December 2024.",{"title":175,"issuer":176,"region":171,"url":177,"note":178},"Keeping Australia Scam Safe","Australian Banking Association","https://www.ausbanking.org.au/priorities/scam-safe-accord/","The Australian banks' Scam-Safe Accord, including Confirmation of Payee and commitments to more warnings, payment delays and security questions on risky payments.",{"title":180,"issuer":181,"region":165,"url":182,"note":183},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https://artificialintelligenceact.eu/annex/3/","Point 5(b) excludes AI systems used for detecting financial fraud from the creditworthiness high risk category.",[185,186,187,188,189],"AI disclosure in the intervention conversation","Documented risk bands and intervention per band, approved by the fraud risk owner","Per payment record of warnings, answers, overrides and human decisions","Model monitoring for detection, false positives and drift, with human approval of new rules","Regular review of outcomes for vulnerable customers",[],{"howToBuild":192},"On Blits.ai the intervention is an **AI agent** called from the payment journey through the\n**REST API or WebSocket API channel**, or shown in the embedded **chat widget**, with the payment\ncontext and the risk band passed in. A **flow** fixes the regulated steps per band (warning, questions, hold offer)\nand the agent handles the open conversation. **Custom functions** call the bank's scoring\nservice, the payee check and the payment hold API; a **knowledge base** holds the approved scam\neducation content per typology.\n\nHeld payments go through an **agentic workflow with human in the loop approval**, so a scam\nspecialist approves or rejects release with the conversation attached, and every step lands in\nthe run's audit trail. **Human handover** passes the customer and the conversation history to a\nspecialist in chat or on a **voice** call, so they do not repeat the story. **Guardrails** and **PII masking** keep account data\nout of prompts, **test suites** replay scam scripts (including coached answers) on every change,\nand **analytics** track cancellations and handovers per typology. The platform is model agnostic\nand runs in EU and UAE regions for data residency.",[194,197,200],{"question":195,"answer":196},"Does AI actually reduce scam losses?","The published results point that way, with caveats. Revolut reports a 30% fall in losses from card scams involving investment opportunities after launching its AI detection and intervention flow, and Commonwealth Bank reports a 76% fall in customer scam losses since the peak, across its whole scam program. Measure it yourself with a control group.",{"question":198,"answer":199},"Should the AI be allowed to block a payment?","It can pause a payment and start a conversation within agreed risk bands. Releasing or declining a held high risk payment should stay with a trained specialist who has the full conversation in front of them, and the decision should be recorded.",{"question":201,"answer":202},"Is scam detection high risk under the EU AI Act?","Not as such. Annex III point 5(b) excludes AI used to detect financial fraud from the creditworthiness category. The customer conversation still needs an AI disclosure, a voice component that recognises emotions would be high risk under Annex III point 1(c), and GDPR applies to the personal data used.",[204,205,206,207,208,209],"real-time-fraud-scoring","mule-network-detection","fraud-alert-confirmation","spam-and-scam-call-blocking","telecom-fraud-detection","card-dispute-and-chargeback-intake","2026-09-27","2026-09-26",[213],{"date":210,"note":214},"First published","scam-payment-interception",[217,273,299,329,351,371],{"title":218,"useCases":219,"organization":220,"vendors":224,"summary":228,"stage":229,"year":230,"channels":231,"languages":233,"metrics":235,"outcomeDisclosed":256,"sources":257,"verification":268,"grade":270,"id":271,"organizationSlug":272},"Commonwealth Bank: proactive scam warnings, in app transaction verification and a fraud detection agent",[215,206,204],{"name":221,"anonymized":222,"country":223,"region":171,"industry":16},"Commonwealth Bank of Australia",false,"AU",[225],{"name":226,"role":227},"Snowflake","platform","Commonwealth Bank combines several AI controls against scams and fraud. Its fraud systems monitor more than 80 million signals a day and the CommBank app sends proactive warning alerts on payments that look risky; NameCheck and Confirmation of Payee check payee details on first time payments. From August 2025 customers are asked to verify certain online card transactions in the app, in real time, before they are authorised. In April 2026 the bank described an agentic system that spots emerging fraud patterns and proposes new detection rules, which the fraud analytics team reviews and approves before they go live. The bank reports a 76% fall in customer scam losses since their peak without attributing it to any single tool, and says its fraud detection technology played a role in cutting fraud losses by over 20% in the first half of FY26.","scaled",2025,[27,232],"api",[234],"en",[236,245,252],{"kpi":47,"value":237,"unit":238,"qualifier":239,"period":240,"baseline":241,"claimant":242,"quote":243,"sourceUrl":244},76,"percent","exact","second half of FY25 versus first half of FY23 (the peak)","customer scam losses at their peak in the first half of FY23","organization","CommBank has seen a 76% drop in customer scam losses since peak (2H25 vs. 1H23)","https://www.commbank.com.au/articles/newsroom/2025/08/commbank-customer-scam-losses-fall-truyu.html",{"kpi":51,"value":246,"unit":247,"qualifier":248,"period":249,"claimant":242,"quote":250,"sourceUrl":251},40000,"count","at-least","per day on average, proactive warning alerts in the CommBank app","Each day, CommBank processes more than 20 million payments on average and sends more than 40,000 proactive warning alerts on average to customers via the CommBank app.","https://www.commbank.com.au/articles/newsroom/2026/04/ai-agent-spots-fraud-in-real-time.html",{"kpi":47,"value":253,"unit":238,"qualifier":248,"period":254,"claimant":242,"quote":255,"sourceUrl":251},20,"first half of FY26 versus first half of FY25","The bank’s fraud detection technology has played a role in helping to reduce fraud losses by over 20% in the first half of the 2026 financial year compared to the first half of the 2025 financial year.",true,[258,261,264],{"url":244,"title":259,"publisher":221,"date":260},"CBA sees customer scam losses fall by 76% and adds two new forms of armour to help keep customers safe","2025-08-11",{"url":251,"title":262,"publisher":221,"date":263},"CommBank develops AI agent that spots new fraud and helps build defences","2026-04-24",{"url":265,"title":266,"publisher":221,"date":267},"https://www.commbank.com.au/articles/newsroom/2025/07/scam-protection-confirmation-of-payee.html","Strengthening scam protection: Introducing Confirmation of Payee","2025-07-02",{"level":269,"checkedAt":211},"source-verified","B","commonwealth-bank-scam-and-fraud-interventions","commonwealth-bank-of-australia",{"title":274,"useCases":275,"organization":276,"vendors":278,"summary":281,"stage":282,"year":230,"channels":283,"languages":285,"metrics":286,"outcomeDisclosed":222,"sources":287,"verification":296,"grade":270,"id":297,"organizationSlug":298},"Westpac: real time AI call assistant for scam conversations",[215,206],{"name":277,"anonymized":222,"country":223,"region":171,"industry":16},"Westpac",[279],{"name":277,"role":280},"in-house","In May 2025 Westpac announced that it was piloting an AI call assistant with its specialist scam and fraud team. It transcribes live customer calls, flags indicators that the customer may be about to pay a scammer or is being coached in the background, and suggests questions for the banker. It sits alongside SaferPay (questions before high risk payments), SafeCall (verified calls through the app to resist spoofing), Westpac Verify (payee name mismatch warnings) and inbound payment detection. The bank reports early qualitative results only.","pilot",[29,284],"agent-desktop",[234],[],[288,292],{"url":289,"title":290,"publisher":277,"date":291},"https://www.westpac.com.au/about-westpac/media/media-releases/2025/29-may/","Using AI to put scammers out of business","2025-05-29",{"url":293,"title":294,"publisher":295,"date":291},"https://www.westpac.com.au/news/making-news/2025/05/westpac-deploys-real-time-AI-to-take-on-scammers/","Westpac deploys real-time AI to take on scammers","Westpac Wire",{"level":269,"checkedAt":211},"westpac-scam-call-assistant",null,{"title":300,"useCases":301,"organization":302,"vendors":305,"summary":307,"stage":308,"year":309,"channels":310,"languages":311,"metrics":312,"outcomeDisclosed":256,"sources":318,"verification":327,"grade":270,"id":328,"organizationSlug":298},"Revolut: AI card scam detection with an in app intervention flow",[215,206,204],{"name":303,"anonymized":222,"country":304,"region":165,"industry":16},"Revolut","GB",[306],{"name":303,"role":280},"In February 2024 Revolut launched a machine learning feature, built by its financial crime team, that estimates whether a card payment is part of a scam. When the risk is high it declines the payment, blocks similar payments and sends the customer through an in app intervention flow that asks about the payment, checks whether someone is guiding them, shows scam stories and offers a chat with a fraud specialist.","production",2024,[27],[234],[313],{"kpi":47,"value":314,"unit":238,"qualifier":239,"period":315,"claimant":242,"quote":316,"sourceUrl":317},30,"since launch, fraud losses from card scams where money was sent for investment opportunities","Since the launch of the card scam detection feature, Revolut has observed a 30% reduction in the fraud losses resulting from card scams where money has been sent for investment opportunities.","https://www.revolut.com/en-US/news/revolut_launches_ai_feature_to_protect_customers_from_card_scams_and_break_the_scammers_spell/",[319,323],{"url":317,"title":320,"publisher":303,"date":321,"archivedUrl":322},"Revolut launches AI feature to protect customers from card scams and break the scammers \"spell\"","2024-02-15","https://web.archive.org/web/20250917122343/https://www.revolut.com/en-US/news/revolut_launches_ai_feature_to_protect_customers_from_card_scams_and_break_the_scammers_spell/",{"url":324,"title":325,"publisher":326,"date":321},"https://www.openbankingexpo.com/news/revolut-introduces-new-ai-powered-card-scam-detection-feature/","Revolut introduces AI-powered card scam detection feature","Open Banking Expo",{"level":269,"checkedAt":211},"revolut-card-scam-detection",{"title":330,"useCases":331,"organization":332,"vendors":335,"summary":336,"stage":308,"year":309,"channels":337,"languages":338,"metrics":339,"outcomeDisclosed":256,"sources":344,"verification":348,"grade":270,"id":349,"organizationSlug":350},"Vodafone: Scam Signal network data service against impersonation fraud",[208,215],{"name":333,"anonymized":222,"country":304,"region":165,"industry":334},"Vodafone","telecommunications",[],"Vodafone Carrier Services launched Scam Signal, an API that analyses real time network data during a live bank transaction to detect social engineering behind authorised push payment fraud, so banks can stop fraudulent transfers as they happen. It sits in Vodafone's Identity Hub next to the SIM Swap and Number Verify APIs, which use CAMARA open standards. JT Group, working with FICO, was the first channel partner to offer it. In a three month pilot with a UK bank that Vodafone does not name, scam detection improved by 30%.",[232],[],[340],{"kpi":48,"value":314,"unit":238,"qualifier":239,"period":341,"claimant":242,"quote":342,"sourceUrl":343},"three month pilot with a UK bank","Scam detection using this service improved by 30% after only three months of a successful pilot with a leading UK bank.","https://www.vodafone.com/news/newsroom/technology/vodafone-business-launches-scam-signal-to-defend-against-impersonation-fraud",[345],{"url":343,"title":346,"publisher":333,"date":347},"Vodafone Business launches scam signal to defend against impersonation fraud","2024-04-23",{"level":269,"checkedAt":210},"vodafone-scam-signal","vodafone",{"title":352,"useCases":353,"organization":354,"vendors":356,"summary":358,"stage":308,"year":359,"channels":360,"languages":361,"metrics":362,"outcomeDisclosed":222,"sources":363,"verification":368,"grade":270,"id":369,"organizationSlug":370},"Mastercard: Consumer Fraud Risk scores for account to account payments in the UK",[215,204],{"name":355,"anonymized":222,"country":304,"region":165,"industry":17},"Mastercard",[357],{"name":355,"role":280},"Mastercard's Consumer Fraud Risk uses AI and its view of account to account payment flows to give UK banks a real time risk score on outgoing payments, so a bank can intervene before money reaches a scammer. Mastercard says it is live with 10 large UK banks, with NatWest among the first users. The only outcome it cites is a TSB extrapolation of what the UK could save if all banks matched TSB's performance, which is a projection, not a measured result.",2023,[232],[234],[],[364],{"url":365,"title":366,"publisher":355,"date":367},"https://newsroom.mastercard.com/news/press/2024/april/mastercard-transforms-the-fight-against-scams-with-latest-ai-tech/","Mastercard transforms the fight against scams with latest AI tech","2024-04-24",{"level":269,"checkedAt":211},"mastercard-consumer-fraud-risk","mastercard",{"title":372,"useCases":373,"organization":374,"vendors":376,"summary":380,"stage":308,"year":230,"channels":381,"languages":382,"metrics":383,"outcomeDisclosed":256,"sources":391,"verification":400,"grade":401,"id":402,"organizationSlug":298},"Starling Bank: Scam Intelligence agent inside the banking app",[215],{"name":375,"anonymized":222,"country":304,"region":165,"industry":16},"Starling Bank",[377],{"name":378,"role":379},"Google Cloud","model-provider","Starling launched Scam Intelligence in October 2025, letting customers upload marketplace ads and messages so a Gemini based model can flag signs of a purchase scam before they pay. In June 2026 the feature became an agent inside Starling Assistant, available to its five million customers: when a customer describes a planned transfer that looks like a romance, investment or other scam, the assistant asks probing questions, gives its view and suggests a call with the support team. Use is opt in and data stays in the bank's cloud environment.",[27],[234],[384],{"kpi":48,"value":385,"unit":238,"qualifier":239,"period":386,"baseline":387,"claimant":388,"quote":389,"sourceUrl":390},300,"since launch (October 2025), rate at which customers cancel marketplace payments; period and baseline not stated","the rate at which customers cancelled marketplace payments before Scam Intelligence","vendor","Scam Intelligence has already increased the rate at which customers cancel marketplace payments by 300%.","https://cloud.google.com/customers/starling",[392,396,398],{"url":393,"title":394,"publisher":375,"date":395},"https://www.starlingbank.com/news/scam-intelligence-launch/","Starling launches UK-first AI tool to combat scams","2025-10-27",{"url":39,"title":38,"publisher":375,"date":397},"2026-06-24",{"url":390,"title":399,"publisher":378},"Starling case study",{"level":269,"checkedAt":211},"C","starling-bank-scam-intelligence",0,[405,413,420],{"kpi":48,"label":406,"unit":238,"aggregate":256,"higherIsBetter":256,"n":407,"nUpTo":403,"median":408,"min":314,"max":385,"byClaimant":409,"vendorOnly":222,"points":410},"Detection improvement",2,165,{"organization":63,"vendor":63,"regulator":403,"independent":403},[411,412],{"evidenceId":402,"organization":375,"value":385,"qualifier":239,"claimant":388,"grade":401,"pooled":256},{"evidenceId":349,"organization":333,"value":314,"qualifier":239,"claimant":242,"grade":270,"pooled":256},{"kpi":47,"label":414,"unit":238,"aggregate":256,"higherIsBetter":256,"n":407,"nUpTo":403,"median":415,"min":314,"max":237,"byClaimant":416,"vendorOnly":222,"points":417},"Fraud loss reduction",53,{"organization":407,"vendor":403,"regulator":403,"independent":403},[418,419],{"evidenceId":271,"organization":221,"value":237,"qualifier":239,"claimant":242,"grade":270,"pooled":256},{"evidenceId":328,"organization":303,"value":314,"qualifier":239,"claimant":242,"grade":270,"pooled":256},{"kpi":51,"label":421,"unit":247,"aggregate":222,"higherIsBetter":256,"n":63,"nUpTo":403,"median":246,"min":246,"max":246,"byClaimant":422,"vendorOnly":222,"points":423},"Interactions handled",{"organization":63,"vendor":403,"regulator":403,"independent":403},[424],{"evidenceId":271,"organization":221,"value":246,"qualifier":248,"claimant":242,"grade":270,"pooled":256},{"low":426,"high":427},50000,1080000,[429,451,464,478,492,503],{"slug":204,"title":430,"shortTitle":431,"definition":432,"status":8,"industries":433,"functions":434,"patterns":435,"audience":437,"autonomy":438,"adoptionStage":439,"segment":440,"evidenceCount":441,"publicEvidenceCount":441,"organizations":442,"bestGrade":270,"headline":448,"lastVerified":210,"indexable":256},"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.",[16,17],[19],[23,436],"anomaly-detection","back-office","autonomous","mainstream","middle-office",9,[443,221,355,444,445,303,446,447],"ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","NatWest Group","Pay.UK","Stripe","Visa",{"kpi":47,"label":414,"unit":238,"n":449,"nUpTo":403,"kind":450,"value":314,"qualifier":239,"claimant":242,"organization":298,"vendorReported":222},3,"median",{"slug":205,"title":452,"shortTitle":453,"definition":454,"status":8,"industries":455,"functions":456,"patterns":458,"audience":437,"autonomy":460,"adoptionStage":32,"segment":440,"evidenceCount":449,"publicEvidenceCount":449,"organizations":461,"bestGrade":270,"headline":298,"lastVerified":210,"indexable":256},"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.",[16,17],[19,457],"financial-crime-compliance",[436,23,24,459],"summarization","copilot",[462,443,463],"BigPay","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":206,"title":465,"shortTitle":466,"definition":467,"status":8,"industries":468,"functions":469,"patterns":470,"audience":30,"autonomy":31,"adoptionStage":471,"segment":33,"evidenceCount":472,"publicEvidenceCount":472,"organizations":473,"bestGrade":270,"headline":476,"lastVerified":210,"indexable":256},"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.",[16,17],[19,20],[22,25,24],"emerging",5,[474,221,475,303,277],"Capital One","Macquarie Bank",{"kpi":47,"label":414,"unit":238,"n":407,"nUpTo":403,"kind":477,"value":237,"qualifier":239,"claimant":242,"organization":221,"vendorReported":222},"reported",{"slug":207,"title":479,"shortTitle":480,"definition":481,"status":8,"industries":482,"functions":483,"patterns":484,"audience":30,"autonomy":438,"adoptionStage":439,"segment":486,"evidenceCount":472,"publicEvidenceCount":472,"organizations":487,"bestGrade":270,"headline":298,"lastVerified":211,"indexable":256},"AI spam and scam call blocking for mobile and landline subscribers","Spam and scam call blocking","AI in the operator's network that protects subscribers from unwanted calls: it analyses incoming calls in real time, blocks known fraudulent calls, and labels suspected scam, spam and spoofed calls on the customer's screen before they answer, so subscribers can decide whether to pick up. Fraud against the operator itself, such as SIM swap or revenue share fraud, is a separate use case.",[334],[19,20],[436,485,23],"classification-and-routing","customer-protection",[488,489,490,491],"Bell Canada","BT Group","Telstra","Virgin Media O2",{"slug":208,"title":493,"shortTitle":494,"definition":495,"status":8,"industries":496,"functions":497,"patterns":500,"audience":437,"autonomy":31,"adoptionStage":32,"segment":486,"evidenceCount":64,"publicEvidenceCount":64,"organizations":501,"bestGrade":270,"headline":502,"lastVerified":210,"indexable":256},"AI for telecom fraud detection (SIM swap, IRSF and Wangiri)","Telecom fraud detection","AI that protects the operator's own network, revenue and numbers from fraud: it watches call, messaging, roaming and account activity to detect SIM swap and port out takeovers, international revenue share fraud (IRSF) and Wangiri one ring scams, blocks or flags them in real time, and shares risk signals with banks and other businesses that rely on the phone number for security. Scam calls aimed at subscribers are handled by call blocking.",[334],[19,498,499],"network-operations","security-operations",[436,23,485],[490,333],{"kpi":48,"label":406,"unit":238,"n":63,"nUpTo":403,"kind":477,"value":314,"qualifier":239,"claimant":242,"organization":333,"vendorReported":222},{"slug":209,"title":504,"shortTitle":505,"definition":506,"status":8,"industries":507,"functions":508,"patterns":510,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"evidenceCount":64,"publicEvidenceCount":449,"organizations":512,"bestGrade":270,"headline":298,"lastVerified":210,"indexable":256},"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.",[16,17],[20,19,509],"operations",[22,25,485,511,24],"document-processing",[221,513,447],"Klarna",{"indexable":256,"reasons":515},[],[517,522,527,535,543,548,555,561,566,572,578,584,591,598,604,609,616,622,628,634,640,646,652,657,661,667,674,679,685,692,698,701,706,711],{"id":151,"label":518,"issuer":181,"region":165,"url":519,"description":520,"useCases":521,"indexable":256},"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":152,"label":523,"issuer":181,"region":165,"url":524,"description":525,"useCases":526,"indexable":256},"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":528,"label":529,"issuer":530,"region":531,"url":532,"description":533,"useCases":534,"indexable":256},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":536,"label":537,"issuer":538,"region":539,"url":540,"description":541,"useCases":542,"indexable":256},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":154,"label":544,"issuer":181,"region":165,"url":545,"description":546,"useCases":547,"indexable":256},"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":549,"label":550,"issuer":551,"region":165,"url":552,"description":553,"useCases":554,"indexable":256},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":153,"label":556,"issuer":557,"region":165,"url":558,"description":559,"useCases":560,"indexable":256},"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":155,"label":562,"issuer":170,"region":171,"url":563,"description":564,"useCases":565,"indexable":256},"MAS AI risk management guidelines","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":156,"label":567,"issuer":568,"region":171,"url":569,"description":570,"useCases":571,"indexable":256},"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":573,"label":574,"issuer":575,"region":531,"url":576,"description":577,"useCases":253,"indexable":256},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":579,"label":580,"issuer":581,"region":539,"url":582,"description":583,"useCases":253,"indexable":256},"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":585,"label":586,"issuer":587,"region":165,"url":588,"description":589,"useCases":590,"indexable":256},"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":592,"label":593,"issuer":594,"region":531,"url":595,"description":596,"useCases":597,"indexable":256},"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":599,"label":600,"issuer":181,"region":165,"url":601,"description":602,"useCases":603,"indexable":256},"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":605,"label":606,"issuer":181,"region":165,"url":607,"description":608,"useCases":603,"indexable":256},"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":610,"label":611,"issuer":612,"region":539,"url":613,"description":614,"useCases":615,"indexable":256},"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":617,"label":618,"issuer":181,"region":165,"url":619,"description":620,"useCases":621,"indexable":256},"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":623,"label":624,"issuer":625,"region":539,"url":626,"description":627,"useCases":621,"indexable":256},"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":629,"label":630,"issuer":631,"region":531,"url":632,"description":633,"useCases":621,"indexable":256},"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":635,"label":636,"issuer":181,"region":165,"url":637,"description":638,"useCases":639,"indexable":256},"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":641,"label":642,"issuer":643,"region":539,"url":644,"description":645,"useCases":639,"indexable":256},"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":647,"label":648,"issuer":170,"region":171,"url":649,"description":650,"useCases":651,"indexable":256},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":653,"label":654,"issuer":181,"region":165,"url":655,"description":656,"useCases":651,"indexable":256},"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":157,"label":658,"issuer":181,"region":165,"url":659,"description":660,"useCases":651,"indexable":256},"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":662,"label":663,"issuer":664,"region":165,"url":665,"description":666,"useCases":441,"indexable":256},"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.",{"id":668,"label":669,"issuer":670,"region":539,"url":671,"description":672,"useCases":673,"indexable":256},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":675,"label":676,"issuer":181,"region":165,"url":677,"description":678,"useCases":673,"indexable":256},"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":680,"label":681,"issuer":181,"region":165,"url":682,"description":683,"useCases":684,"indexable":256},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",6,{"id":686,"label":687,"issuer":688,"region":689,"url":690,"description":691,"useCases":472,"indexable":256},"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":693,"label":694,"issuer":695,"region":165,"url":696,"description":697,"useCases":64,"indexable":256},"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":158,"label":699,"issuer":164,"region":165,"url":166,"description":700,"useCases":64,"indexable":256},"UK APP scam reimbursement rules","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":160,"label":702,"issuer":703,"region":171,"url":704,"description":705,"useCases":449,"indexable":256},"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":707,"label":708,"issuer":181,"region":165,"url":709,"description":710,"useCases":449,"indexable":256},"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":712,"label":713,"issuer":714,"region":539,"url":715,"description":716,"useCases":449,"indexable":256},"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.",1790598305936]