[{"data":1,"prerenderedAt":826},["ShallowReactive",2],{"uc-real-time-fraud-scoring":3,"uc-regulations":632},{"useCase":4,"evidence":222,"blitsAiDeployments":488,"benchmarks":489,"indicative":522,"related":525,"indexability":630,"includeUnpublished":228},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":21,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":41,"valueDrivers":42,"kpis":46,"indicativeValue":52,"macroEstimates":94,"feasibility":95,"implementation":109,"risk":155,"blitsAi":198,"faq":200,"related":210,"datePublished":217,"dateModified":217,"lastVerified":217,"changelog":218,"slug":221},"Real time fraud scoring for card and instant payments","Real time fraud scoring","AI fraud scoring for card and instant payments","Machine learning approves, challenges or blocks each card and instant payment in milliseconds. At NatWest, Featurespace reports 57% more value of fraud detected.","published","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.",[12,13,14,15],"transaction fraud scoring","real time payment fraud detection","card authorization fraud model","instant payment fraud screening",[17,18],"banking","payments",[20],"fraud-prevention",[22,23],"prediction-and-scoring","anomaly-detection",[25],"api","back-office","autonomous","mainstream","middle-office","Fraud has moved to the fastest rails. Authorised push payment scams and account takeover often end in\ninstant account to account payments that settle in seconds and are hard to recall, and card not\npresent fraud has to be caught while the card authorization is still open. The decision to stop a\npayment is made inside that window, with no time for a human: Mastercard, for example, says\nDecision Intelligence Pro returns its improved score in less than 50 milliseconds.\n\nRule based engines struggle on both sides of that decision. Rules written for last quarter's\nattack miss the new one, and the rules that do fire decline many genuine customers, who then call\nthe contact centre or abandon the purchase. Scams are the hardest case: the customer is\nauthenticating the payment themselves, so strong authentication does not help, and only a change\nin their behaviour or the payee's profile gives the attack away.",[32,37],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"The US Federal Trade Commission reports that consumers reported losing more than USD 12.5 billion to fraud in 2024, a 25% increase over the prior year.","New FTC Data Show a Big Jump in Reported Losses to Fraud to $12.5 Billion in 2024","https://www.ftc.gov/news-events/news/press-releases/2025/03/new-ftc-data-show-big-jump-reported-losses-fraud-125-billion-2024",2025,{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":36},"In a Feedzai survey of 562 fraud and financial crime professionals, 90% of financial institutions said they use AI to expedite fraud investigations and detect new tactics in real time.","AI Fraud Trends 2025: Banks Fight Back","https://www.feedzai.com/pressrelease/ai-fraud-trends-2025/","1. **Enrich the event.** Each authorization or payment request is joined in real time with the\n   customer's profile, recent behaviour, device and session data, merchant or payee history and\n   any confirmation of payee result.\n2. **Score it.** One or more models (gradient boosted trees, behavioural sequence models, graph\n   features that link accounts, devices and payees) return a risk score and the top reasons, within\n   the latency budget of the rail.\n3. **Decide with a strategy layer.** Score bands and business rules turn the score into an action:\n   approve, approve and monitor, step up authentication, hold for review, warn the customer about a\n   likely scam, or decline.\n4. **Learn from outcomes.** Confirmed fraud, chargebacks, scam reports and customer confirmations\n   flow back as labels, and challenger models are trained and compared before promotion.\n5. **Hand over the grey zone.** Holds and scam warnings create cases for the fraud team and, where\n   the customer is involved, a short interaction in the app or by phone.",[43,44,45],"risk-reduction","customer-experience","cost-to-serve",[47,48,49,50,51],"fraud-loss-reduction","detection-rate-improvement","false-positive-reduction","automation-rate","interactions-handled",{"referenceOrg":53,"inputs":54,"formula":89,"currency":90,"period":91,"resultLabel":92,"caveat":93},"A retail bank with 1 million active card and payment customers",[55,62,69,76,82],{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"fraudLosses","Annual gross fraud losses on cards and payments",5000000,15000000,"USD per year","Editorial assumption for a bank of this size. Replace with your own gross fraud loss figure.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"lossReduction","Share of fraud losses avoided by better scoring",0.1,0.2,"fraction of losses","Conservative against the achieved results on this page (Stripe reports an over 30% reduction in fraud on eligible transactions for early users of its new Radar interventions; Commonwealth Bank reports fraud losses down by over 20% year on year, with its detection technology playing a role), because not all of a bank's losses sit in the segment where scoring improves.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"manualReviews","Transactions sent to manual review per year",50000,150000,"reviews per year","Editorial assumption. Replace with your own review queue volume.",{"key":77,"label":78,"low":65,"high":79,"unit":80,"note":81},"reviewReduction","Share of manual reviews avoided",0.25,"fraction of reviews","Capped at the figure Visa reports for active Decision Manager users (manual reviews reduced by 25% or more).",{"key":83,"label":84,"low":85,"high":86,"unit":87,"note":88},"costPerReview","Cost of one manual review",4,10,"USD per review","Editorial assumption for a few minutes of fully loaded analyst time per review. Replace with your own.","fraudLosses * lossReduction + manualReviews * reviewReduction * costPerReview","USD","per year","Fraud losses and review cost avoided","Leaves out the revenue recovered from fewer false declines, the effect on scam reimbursement liabilities, and the cost of the platform, data engineering and model validation.",[],{"complexity":96,"complexityNote":97,"dataPrerequisites":98,"integrations":103},"high","The model is the easy part. The work is streaming data at authorization latency, a clean label pipeline from chargebacks and scam reports, a strategy layer that the fraud team can tune, and model risk validation for a model that decides on customers' payments without a human.",[99,100,101,102],"Labelled history of fraud, chargebacks and scam reports linked to the original transactions","Real time customer, device and session data available within the latency budget","Payee and merchant history, including confirmation of payee results where available","Customer contact outcomes for held or challenged payments",[104,105,106,107,108],"Card authorization host or issuer processor","Instant payment and account to account payment hub","Digital banking channels for device and session signals and in app scam warnings","Case management for held payments and confirmed fraud","Network or consortium scores from card schemes and industry data sharing schemes",{"steps":110,"guardrails":129,"humanInTheLoop":135,"kpisToInstrument":136,"failureModes":142},[111,114,117,120,123,126],{"title":112,"detail":113},"Map the decision points and latency budgets","List every place a payment can be stopped (authorization, payment initiation, payee creation, login) and the time available at each. This decides which features and models are feasible.",{"title":115,"detail":116},"Build the label pipeline first","Link chargebacks, customer fraud claims and scam reports back to the transactions, with dates, so you can train on what really happened and measure detection honestly.",{"title":118,"detail":119},"Run champion and challenger in shadow mode","Score live traffic with the new model without acting on it, and compare detection and false positive rates against the current engine on the same transactions for several weeks.",{"title":121,"detail":122},"Design the strategy layer with the fraud team","Agree score bands and actions per segment and rail, including when to warn a customer about a likely scam instead of declining, and document who can change thresholds.",{"title":124,"detail":125},"Validate and inventory the model","Put the model through independent validation, record it in the model inventory with an owner, and set monitoring for drift, data quality and fairness across customer groups.",{"title":127,"detail":128},"Close the loop with customer contact","Make sure held or declined payments can be released quickly through the app or the contact centre, and feed those outcomes back as labels.",[130,131,132,133,134],"Every automated decline or hold returns reason codes that staff can explain to the customer","Threshold changes go through change control with a documented owner and a rollback plan","A fallback rule set takes over automatically if the model or its data feeds fail","Regular fairness testing so that false declines do not concentrate on particular customer groups","Card data handled only inside the PCI DSS scope, with tokenized identifiers elsewhere","The model decides autonomously within the latency window, so human control sits around it: the fraud strategy team owns thresholds and actions, analysts work the held payments and scam warnings, and model risk validates every material change before it goes live.",[137,138,139,140,141],"Fraud detection rate and value detection rate on confirmed fraud, by rail and segment","False positive ratio (genuine transactions declined or held per fraud caught)","Gross fraud and scam losses normalised for volume","Share of held payments released by the customer, and time to release","Model latency at the 99th percentile and fallback activations",[143,146,149,152],{"title":144,"detail":145},"Label leakage and optimistic backtests","Models trained on labels that were only known after the fact look excellent offline and disappoint live. Build features only from data available at decision time and trust shadow mode results over backtests.",{"title":147,"detail":148},"Fraud moves to the next rail","Tightening card controls pushes attackers to instant payments or account takeover. Score all rails and watch the loss mix, not one channel.",{"title":150,"detail":151},"False declines hidden in the dashboard","A model tuned only for detection quietly declines good customers. Track the false positive ratio and complaints as closely as losses.",{"title":153,"detail":154},"Silent model drift","Behaviour changes (a new wallet, a holiday season) degrade the model without an alert. Monitor feature distributions and score stability daily.",{"euAiAct":156,"regulations":159,"guidance":173,"controls":191,"incidents":197},{"tier":157,"basis":158},"minimal","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.",[160,161,162,163,164,165,166,167,168,169,170,171,172],"eu-ai-act","gdpr","dora","pci-dss","eu-psd2","uk-consumer-duty","uk-psr-app-reimbursement","pra-ss1-23","us-sr-11-7","nist-ai-rmf","mas-ai-risk-management","mas-shared-responsibility-framework","apra-cps-230",[174,180,185],{"title":175,"issuer":176,"region":177,"url":178,"note":179},"Annex III: High-Risk AI Systems Referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(b) excludes AI systems used for the purpose of detecting financial fraud from the high risk creditworthiness category.",{"title":181,"issuer":182,"region":177,"url":183,"note":184},"APP scams","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","The UK reimbursement requirement for authorised push payment scams over Faster Payments and CHAPS has sending and receiving firms split the cost of reimbursing victims 50:50, which puts the cost of missed scams on both sides of the payment.",{"title":186,"issuer":187,"region":188,"url":189,"note":190},"Guidelines on Shared Responsibility Framework","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/regulation/guidelines/guidelines-on-shared-responsibility-framework","Implemented from 16 December 2024, it assigns financial institutions and telcos duties to mitigate phishing scams and requires payouts to victims where those duties are breached, which raises the value of real time detection.",[192,193,194,195,196],"Model inventory entry with owner, validation report and monitoring plan","Reason codes stored with every automated decline or hold","Documented threshold governance with change control and rollback","Fairness and customer outcome monitoring on false declines","Tested fallback to a rule set when the model or data feeds are unavailable",[],{"howToBuild":199},"The scoring model itself runs in the bank's payment stack, next to the authorization host,\nbecause it has to answer within milliseconds. Blits.ai covers the parts around it that involve\npeople. When a customer gets in touch about a held payment, in the bank's app through the **REST\nor WebSocket API channel**, on **WhatsApp**, by **SMS** or on the phone, an **agent** confirms\nwhether they made the payment, gives the scam warnings the fraud team wrote in the **knowledge\nbase**, and calls **custom functions** to release the payment or keep the block. The bank's\nsystems can also trigger an **agentic workflow** through the API to prepare the case, with **human\nin the loop confirmation** so an analyst approves a release above a set amount.\n\nOn the phone, **voice** telephony uses real time streaming speech recognition and synthesis, and\nanything unusual goes to a fraud specialist through **human handover**, with the conversation\nhistory passed along. **Guardrails** and **PII masking** keep card and account data out of model\nprompts, **test suites** replay scam scenarios on every change, and the platform is **model\nagnostic**, with EU and UAE data residency options.",[201,204,207],{"question":202,"answer":203},"How much does machine learning improve fraud detection?","Published results come mostly from vendors and networks. Featurespace reports, citing NatWest data from 2025, 57% more value of fraud detected and 75% fewer false positives on scams, and Stripe says early users of its new Radar interventions saw fraud on eligible transactions fall by over 30%. Pre launch modelling figures, such as those Mastercard published for Decision Intelligence Pro, are not measured results, so measure your own gain in shadow mode on your own traffic.",{"question":205,"answer":206},"Is a fraud scoring model high risk under the EU AI Act?","Not by default. Annex III point 5(b) explicitly excludes AI used to detect financial fraud from the high risk creditworthiness category. GDPR rules on automated decisions and your model risk framework still apply, so keep reason codes and a route for customers to challenge a decline.",{"question":208,"answer":209},"Can real time scoring stop authorised push payment scams?","Partly. The customer authorises the payment, so the signal is in behaviour and in the payee: a new payee, an unusual amount, a remote access session or a mule account on the receiving side. Banks that report results pair scoring with an intervention: Revolut declines card payments its model judges likely to be scams and sends the customer through an in app intervention flow, and reports a 30% reduction in fraud losses from card scams where money was sent for investment opportunities. Commonwealth Bank sends more than 40,000 proactive warning alerts a day in its app.",[211,212,213,214,215,216],"scam-payment-interception","fraud-alert-triage","fraud-alert-confirmation","mule-network-detection","application-and-identity-fraud-detection","agentic-payment-initiation","2026-09-27",[219],{"date":217,"note":220},"First published","real-time-fraud-scoring",[223,279,305,327,355,373,402,435,455],{"title":224,"useCases":225,"organization":226,"vendors":230,"summary":234,"stage":235,"year":36,"channels":236,"languages":238,"metrics":240,"outcomeDisclosed":261,"sources":262,"verification":273,"grade":276,"id":277,"organizationSlug":278},"Commonwealth Bank: proactive scam warnings, in app transaction verification and a fraud detection agent",[211,213,221],{"name":227,"anonymized":228,"country":229,"region":188,"industry":17},"Commonwealth Bank of Australia",false,"AU",[231],{"name":232,"role":233},"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",[237,25],"mobile-app",[239],"en",[241,250,257],{"kpi":47,"value":242,"unit":243,"qualifier":244,"period":245,"baseline":246,"claimant":247,"quote":248,"sourceUrl":249},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":251,"unit":252,"qualifier":253,"period":254,"claimant":247,"quote":255,"sourceUrl":256},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":258,"unit":243,"qualifier":253,"period":259,"claimant":247,"quote":260,"sourceUrl":256},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,[263,266,269],{"url":249,"title":264,"publisher":227,"date":265},"CBA sees customer scam losses fall by 76% and adds two new forms of armour to help keep customers safe","2025-08-11",{"url":256,"title":267,"publisher":227,"date":268},"CommBank develops AI agent that spots new fraud and helps build defences","2026-04-24",{"url":270,"title":271,"publisher":227,"date":272},"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":274,"checkedAt":275},"source-verified","2026-09-26","B","commonwealth-bank-scam-and-fraud-interventions","commonwealth-bank-of-australia",{"title":280,"useCases":281,"organization":282,"vendors":286,"summary":289,"stage":235,"year":36,"channels":290,"languages":291,"metrics":292,"outcomeDisclosed":261,"sources":298,"verification":302,"grade":276,"id":303,"organizationSlug":304},"Stripe: Radar fraud scoring and the Payments Foundation Model",[221],{"name":283,"anonymized":228,"country":284,"region":285,"industry":18},"Stripe","US","global",[287],{"name":283,"role":288},"in-house","Stripe's Radar scores payments on its network for fraud in real time and has learned from more than a decade of Stripe data. In May 2025 Stripe described a Payments Foundation Model trained on tens of billions of transactions that turns each payment into an embedding used for real time predictions; on sophisticated card testing attacks against large users, Stripe says detection rose from 59% to 97% overnight. A new multihead model now triggers step up authentication for risky payments below the block threshold.",[25],[],[293],{"kpi":47,"value":294,"unit":243,"qualifier":253,"period":295,"claimant":247,"quote":296,"sourceUrl":297},30,"early users of intelligent 3DS interventions, eligible transactions","Backed by a new multihead model and decisioning layer, early users have seen an over 30% reduction in fraud on eligible transactions, representing one of the largest ever improvements to Radar.","https://stripe.com/blog/using-ai-optimize-payments-performance-payments-intelligence-suite",[299],{"url":297,"title":300,"publisher":283,"date":301},"Using AI to optimize payments performance with the Payments Intelligence Suite","2025-05-15",{"level":274,"checkedAt":275},"stripe-radar-payments-foundation-model",null,{"title":306,"useCases":307,"organization":308,"vendors":310,"summary":312,"stage":313,"year":314,"channels":315,"languages":316,"metrics":317,"outcomeDisclosed":228,"sources":318,"verification":324,"grade":276,"id":325,"organizationSlug":326},"Mastercard: Decision Intelligence Pro, generative AI in real time card transaction scoring",[221],{"name":309,"anonymized":228,"country":284,"region":285,"industry":18},"Mastercard",[311],{"name":309,"role":288},"Mastercard's Decision Intelligence scores card transactions for fraud risk in real time on behalf of issuing banks, and Mastercard says it already helps banks score and approve 143 billion transactions a year. Decision Intelligence Pro adds generative AI techniques that assess the relationships between entities around a transaction and return an improved score in less than 50 milliseconds. Mastercard also published detection and false positive figures from its initial modelling and own analysis before launch; these are not measured production results and are not recorded as metrics.","announced",2024,[25],[],[],[319],{"url":320,"title":321,"publisher":322,"date":323},"https://newsroom.mastercard.com/news/press/2024/february/mastercard-supercharges-consumer-protection-with-gen-ai/","Mastercard supercharges consumer protection with gen AI","Mastercard Newsroom","2024-02-01",{"level":274,"checkedAt":275},"mastercard-decision-intelligence-pro","mastercard",{"title":328,"useCases":329,"organization":330,"vendors":333,"summary":335,"stage":336,"year":314,"channels":337,"languages":338,"metrics":339,"outcomeDisclosed":261,"sources":344,"verification":353,"grade":276,"id":354,"organizationSlug":304},"Revolut: AI card scam detection with an in app intervention flow",[211,213,221],{"name":331,"anonymized":228,"country":332,"region":177,"industry":17},"Revolut","GB",[334],{"name":331,"role":288},"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",[237],[239],[340],{"kpi":47,"value":294,"unit":243,"qualifier":244,"period":341,"claimant":247,"quote":342,"sourceUrl":343},"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/",[345,349],{"url":343,"title":346,"publisher":331,"date":347,"archivedUrl":348},"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":350,"title":351,"publisher":352,"date":347},"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":274,"checkedAt":275},"revolut-card-scam-detection",{"title":356,"useCases":357,"organization":358,"vendors":359,"summary":361,"stage":336,"year":362,"channels":363,"languages":364,"metrics":365,"outcomeDisclosed":228,"sources":366,"verification":371,"grade":276,"id":372,"organizationSlug":326},"Mastercard: Consumer Fraud Risk scores for account to account payments in the UK",[211,221],{"name":309,"anonymized":228,"country":332,"region":177,"industry":18},[360],{"name":309,"role":288},"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,[25],[239],[],[367],{"url":368,"title":369,"publisher":309,"date":370},"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":274,"checkedAt":275},"mastercard-consumer-fraud-risk",{"title":374,"useCases":375,"organization":376,"vendors":378,"summary":380,"stage":235,"year":362,"channels":381,"languages":382,"metrics":383,"outcomeDisclosed":261,"sources":396,"verification":399,"grade":276,"id":400,"organizationSlug":401},"Visa: Decision Manager machine learning fraud screening for merchants and acquirers",[221],{"name":377,"anonymized":228,"country":284,"region":285,"industry":18},"Visa",[379],{"name":377,"role":288},"Decision Manager is Visa's machine learning fraud management platform for merchants and acquirers. It gives each transaction a risk score from 0 to 99 drawn from hundreds of real time data points and automates the accept, review or reject decision. Visa reports that almost all transactions it screened in 2023 were resolved automatically and that active users cut manual reviews, which is the triage workload for fraud teams.",[25],[],[384,389,391],{"kpi":50,"value":385,"unit":243,"qualifier":244,"period":386,"claimant":247,"quote":387,"sourceUrl":388},98.7,"2023","In 2023, Decision Manager screened 3.2 billion transactions and prevented an estimated $33 billion in potential fraud losses — with 98.7% of all transactions processed through Decision Manager resolved automatically by AI.","https://corporate.visa.com/en/solutions/visa-protect/insights/ai-fraud-detection.html",{"kpi":51,"value":390,"unit":252,"qualifier":244,"period":386,"claimant":247,"quote":387,"sourceUrl":388},3200000000,{"kpi":392,"value":393,"unit":243,"qualifier":253,"period":394,"claimant":247,"quote":395,"sourceUrl":388},"alert-volume-reduction",25,"active users, manual review reduction","For active users, Decision Manager has helped reduce manual reviews by 25% or more, freeing fraud teams to focus on complex or high-value cases rather than routine screening.",[397],{"url":388,"title":398,"publisher":377},"AI solutions for fraud prevention and detection",{"level":274,"checkedAt":275},"visa-decision-manager","visa",{"title":403,"useCases":404,"organization":405,"vendors":407,"summary":410,"stage":336,"year":36,"channels":411,"languages":412,"metrics":413,"outcomeDisclosed":261,"sources":420,"verification":432,"grade":433,"id":434,"organizationSlug":304},"BioCatch Trust Australia: behavioural intelligence sharing on receiving accounts across five banks",[214,221],{"name":406,"anonymized":228,"country":229,"region":188,"industry":17},"ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)",[408],{"name":409,"role":233},"BioCatch","In November 2024 ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac joined BioCatch Trust Australia, launched as a pilot of an interbank network that shares behavioural and device intelligence about receiving accounts, so the sending bank can review a payment to a likely mule account before money leaves. BioCatch reports that in the third quarter of 2025 the network analysed more than 180 million payments and revealed more than $60 million in attempted fraud (currency not stated in the release), and that two more institutions, including Macquarie Bank, have since joined.",[25],[239],[414],{"kpi":51,"value":415,"unit":252,"qualifier":253,"period":416,"claimant":417,"quote":418,"sourceUrl":419},180000000,"payments analysed in the third quarter of 2025","vendor","in the third quarter of 2025 alone, analyzed more than 180 million payments totaling more than $330 billion, revealing more than $60 million in attempted fraud.","https://www.biocatch.com/press-release/aussie-intelligence-sharing-exposes-more-than-60-million-in-fraud-attempts-in-three-months",[421,424,428],{"url":419,"title":422,"publisher":409,"date":423},"Aussie intelligence-sharing exposes more than $60 million in fraud attempts in three months","2025-11-26",{"url":425,"title":426,"publisher":409,"date":427},"https://www.biocatch.com/press-release/biocatch-partners-australian-banks-fraud-scams-intelligence-sharing-network","BioCatch partners with Australian banks on launch of fraud and scams intelligence-sharing network","2024-11-20",{"url":429,"title":430,"publisher":431,"date":427},"https://www.nab.com.au/news/technology-ai/nab-biocatch-trust","NAB joins BioCatch Trust Australia to protect customers from scams and fraud","NAB",{"level":274,"checkedAt":275},"C","biocatch-trust-australia-mule-intelligence",{"title":436,"useCases":437,"organization":438,"vendors":440,"summary":442,"stage":443,"year":314,"channels":444,"languages":445,"metrics":446,"outcomeDisclosed":228,"sources":447,"verification":453,"grade":433,"id":454,"organizationSlug":304},"Pay.UK and Visa: AI fraud scoring pilot on UK account to account payments",[221],{"name":439,"anonymized":228,"country":332,"region":177,"industry":18},"Pay.UK",[441],{"name":377,"role":233},"In a pilot with Pay.UK, which runs the UK's retail payment operations, Visa applied AI risk scoring to billions of historic UK account to account transactions covering 12 months and more than half of annual volume. It identified 54% of the fraudulent transactions that had already passed through the banks' own fraud detection systems. The pilot was retrospective, on historical data, and on the same day Visa made the capability available to UK banks as a real time service, Visa Protect for A2A Payments.","pilot",[25],[],[],[448],{"url":449,"title":450,"publisher":451,"date":452},"https://www.visa.co.uk/about-visa/newsroom/press-releases.3326480.html","Visa's new AI tool for Faster Payments could help save UK over £330m a year on fraud and APP scams","Visa UK","2024-05-30",{"level":274,"checkedAt":275},"pay-uk-visa-account-to-account-fraud-pilot",{"title":456,"useCases":457,"organization":458,"vendors":460,"summary":463,"stage":235,"year":464,"channels":465,"languages":467,"metrics":468,"outcomeDisclosed":261,"sources":482,"verification":485,"grade":433,"id":486,"organizationSlug":487},"NatWest: real time machine learning fraud and scam detection with Featurespace",[221],{"name":459,"anonymized":228,"country":332,"region":177,"industry":17},"NatWest Group",[461],{"name":462,"role":233},"Featurespace","NatWest began working with Featurespace in 2019, when its incumbent fraud detection system was struggling to identify fraud and scams, and moved to Featurespace's real time platform with adaptive machine learning models as the first line of defence, deployed enterprise wide. Building on its results in authorised push payment scam detection, the bank extended the platform to real time debit card fraud detection with deep behavioural models and ensembled risk scores, integrated with SMS alerts that let customers approve or decline transactions. The vendor reports, citing NatWest data from 2025, a higher value of fraud and scams detected and fewer false positives on scams.",2019,[25,466],"sms",[239],[469,474,478],{"kpi":48,"value":470,"unit":243,"qualifier":244,"period":471,"claimant":417,"quote":472,"sourceUrl":473},135,"value of scams detected (NatWest data, 2025)","135%Improved value of scams detected","https://www.featurespace.com/case-studies/natwest",{"kpi":48,"value":475,"unit":243,"qualifier":244,"period":476,"claimant":417,"quote":477,"sourceUrl":473},57,"value of fraud detected (NatWest data, 2025)","57%Improved value of fraud detected",{"kpi":49,"value":479,"unit":243,"qualifier":244,"period":480,"claimant":417,"quote":481,"sourceUrl":473},75,"scam detection (NatWest data, 2025)","75%Reduced false positives for scams",[483],{"url":473,"title":484,"publisher":462},"NatWest case study",{"level":274,"checkedAt":275},"natwest-featurespace-fraud-and-scam-detection","natwest-group",0,[490,498,507,512,517],{"kpi":47,"label":491,"unit":243,"aggregate":261,"higherIsBetter":261,"n":492,"nUpTo":488,"median":294,"min":294,"max":242,"byClaimant":493,"vendorOnly":228,"points":494},"Fraud loss reduction",3,{"organization":492,"vendor":488,"regulator":488,"independent":488},[495,496,497],{"evidenceId":277,"organization":227,"value":242,"qualifier":244,"claimant":247,"grade":276,"pooled":261},{"evidenceId":354,"organization":331,"value":294,"qualifier":244,"claimant":247,"grade":276,"pooled":261},{"evidenceId":303,"organization":283,"value":294,"qualifier":253,"claimant":247,"grade":276,"pooled":261},{"kpi":51,"label":499,"unit":252,"aggregate":228,"higherIsBetter":261,"n":492,"nUpTo":488,"median":415,"min":251,"max":390,"byClaimant":500,"vendorOnly":228,"points":503},"Interactions handled",{"organization":501,"vendor":502,"regulator":488,"independent":488},2,1,[504,505,506],{"evidenceId":400,"organization":377,"value":390,"qualifier":244,"claimant":247,"grade":276,"pooled":261},{"evidenceId":434,"organization":406,"value":415,"qualifier":253,"claimant":417,"grade":433,"pooled":261},{"evidenceId":277,"organization":227,"value":251,"qualifier":253,"claimant":247,"grade":276,"pooled":261},{"kpi":50,"label":508,"unit":243,"aggregate":261,"higherIsBetter":261,"n":502,"nUpTo":488,"median":385,"min":385,"max":385,"byClaimant":509,"vendorOnly":228,"points":510},"Automation rate",{"organization":502,"vendor":488,"regulator":488,"independent":488},[511],{"evidenceId":400,"organization":377,"value":385,"qualifier":244,"claimant":247,"grade":276,"pooled":261},{"kpi":48,"label":513,"unit":243,"aggregate":261,"higherIsBetter":261,"n":502,"nUpTo":488,"median":470,"min":470,"max":470,"byClaimant":514,"vendorOnly":261,"points":515},"Detection improvement",{"organization":488,"vendor":502,"regulator":488,"independent":488},[516],{"evidenceId":486,"organization":459,"value":470,"qualifier":244,"claimant":417,"grade":433,"pooled":261},{"kpi":49,"label":518,"unit":243,"aggregate":261,"higherIsBetter":261,"n":502,"nUpTo":488,"median":479,"min":479,"max":479,"byClaimant":519,"vendorOnly":261,"points":520},"False positive reduction",{"organization":488,"vendor":502,"regulator":488,"independent":488},[521],{"evidenceId":486,"organization":459,"value":479,"qualifier":244,"claimant":417,"grade":433,"pooled":261},{"low":523,"high":524},520000,3375000,[526,549,563,576,588,612],{"slug":211,"title":527,"shortTitle":528,"definition":529,"status":9,"industries":530,"functions":531,"patterns":533,"audience":537,"autonomy":538,"adoptionStage":539,"segment":540,"evidenceCount":541,"publicEvidenceCount":541,"organizations":542,"bestGrade":276,"headline":546,"lastVerified":275,"indexable":261},"AI scam intervention for instant payments","Scam payment interception","AI that talks to the customer when they are about to authorise an instant payment that looks like a scam: it combines the payee check and the risk score, asks targeted questions about the payment in plain language, explains the specific scam pattern, and holds, delays or escalates the payment to a human specialist when the risk stays high. Unlike fraud scoring, which stops payments the customer did not make, it protects customers from payments they are being manipulated into making.",[17,18],[20,532],"customer-service",[534,22,535,536],"conversational-agent","agentic-workflow","voice-agent","customer-facing","supervised-agent","early-adopters","front-office",6,[227,309,331,543,544,545],"Starling Bank","Vodafone","Westpac",{"kpi":48,"label":513,"unit":243,"n":501,"nUpTo":488,"kind":547,"value":548,"qualifier":244,"claimant":417,"organization":543,"vendorReported":261},"reported",300,{"slug":212,"title":550,"shortTitle":551,"definition":552,"status":9,"industries":553,"functions":554,"patterns":556,"audience":559,"autonomy":538,"adoptionStage":539,"segment":29,"evidenceCount":492,"publicEvidenceCount":501,"organizations":560,"bestGrade":433,"headline":304,"lastVerified":217,"indexable":261},"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.",[17,18],[20,555],"operations",[535,557,558,22],"classification-and-routing","summarization","employee-facing",[561,562],"Coast","SEB",{"slug":213,"title":564,"shortTitle":565,"definition":566,"status":9,"industries":567,"functions":568,"patterns":569,"audience":537,"autonomy":538,"adoptionStage":570,"segment":540,"evidenceCount":571,"publicEvidenceCount":571,"organizations":572,"bestGrade":276,"headline":575,"lastVerified":217,"indexable":261},"AI agent for fraud alert confirmation with cardholders","Fraud alert confirmation","A customer facing AI agent that contacts the cardholder as soon as the fraud engine flags a card transaction, in the channel they actually respond to, verifies them, asks whether they made the transaction and acts on the answer: releasing the block so a retry succeeds, or freezing the card and starting the fraud claim.",[17,18],[20,532],[534,536,535],"emerging",5,[573,227,574,331,545],"Capital One","Macquarie Bank",{"kpi":47,"label":491,"unit":243,"n":501,"nUpTo":488,"kind":547,"value":242,"qualifier":244,"claimant":247,"organization":227,"vendorReported":228},{"slug":214,"title":577,"shortTitle":578,"definition":579,"status":9,"industries":580,"functions":581,"patterns":583,"audience":26,"autonomy":584,"adoptionStage":539,"segment":29,"evidenceCount":492,"publicEvidenceCount":492,"organizations":585,"bestGrade":276,"headline":304,"lastVerified":217,"indexable":261},"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.",[17,18],[20,582],"financial-crime-compliance",[23,22,535,558],"copilot",[586,406,587],"BigPay","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":215,"title":589,"shortTitle":590,"definition":591,"status":9,"industries":592,"functions":596,"patterns":599,"audience":26,"autonomy":538,"adoptionStage":539,"segment":540,"evidenceCount":541,"publicEvidenceCount":541,"organizations":602,"bestGrade":276,"headline":609,"lastVerified":275,"indexable":261},"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.",[17,18,593,594,595],"cross-industry","government","telecommunications",[20,597,598],"onboarding-and-kyc","lending-and-credit",[600,23,601,22],"document-processing","computer-vision",[603,604,605,606,607,608],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":48,"label":513,"unit":610,"n":502,"nUpTo":488,"kind":547,"value":611,"qualifier":244,"claimant":247,"organization":606,"vendorReported":228},"multiplier",2.5,{"slug":216,"title":613,"shortTitle":614,"definition":615,"status":9,"industries":616,"functions":618,"patterns":620,"audience":537,"autonomy":538,"adoptionStage":570,"segment":540,"evidenceCount":621,"publicEvidenceCount":622,"organizations":623,"bestGrade":276,"headline":304,"lastVerified":217,"indexable":261},"AI agent for payment initiation within a customer mandate","Agentic payment initiation","An AI agent that initiates and completes payments or purchases on a customer's behalf, within a mandate the customer set in advance (spending caps, allowed merchants or categories, a tokenized credential and rules for when to ask for confirmation), and then confirms and reconciles every transaction it made.",[18,17,617],"retail-and-ecommerce",[532,619,555],"sales",[535,534],8,7,[624,625,626,627,628,629,377],"DBS Bank","ING","Majid Al Futtaim","PayPal","Banco Santander","Ulta Beauty",{"indexable":261,"reasons":631},[],[633,638,643,650,657,662,669,675,680,685,690,695,702,709,715,720,727,733,739,745,751,757,762,767,771,778,784,789,794,801,806,809,815,820],{"id":160,"label":634,"issuer":176,"region":177,"url":635,"description":636,"useCases":637,"indexable":261},"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":161,"label":639,"issuer":176,"region":177,"url":640,"description":641,"useCases":642,"indexable":261},"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":644,"label":645,"issuer":646,"region":285,"url":647,"description":648,"useCases":649,"indexable":261},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":169,"label":651,"issuer":652,"region":653,"url":654,"description":655,"useCases":656,"indexable":261},"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":162,"label":658,"issuer":176,"region":177,"url":659,"description":660,"useCases":661,"indexable":261},"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":663,"label":664,"issuer":665,"region":177,"url":666,"description":667,"useCases":668,"indexable":261},"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":165,"label":670,"issuer":671,"region":177,"url":672,"description":673,"useCases":674,"indexable":261},"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":170,"label":676,"issuer":187,"region":188,"url":677,"description":678,"useCases":679,"indexable":261},"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":172,"label":681,"issuer":682,"region":188,"url":683,"description":684,"useCases":393,"indexable":261},"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.",{"id":163,"label":686,"issuer":687,"region":285,"url":688,"description":689,"useCases":258,"indexable":261},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":168,"label":691,"issuer":692,"region":653,"url":693,"description":694,"useCases":258,"indexable":261},"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":696,"label":697,"issuer":698,"region":177,"url":699,"description":700,"useCases":701,"indexable":261},"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":703,"label":704,"issuer":705,"region":285,"url":706,"description":707,"useCases":708,"indexable":261},"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":710,"label":711,"issuer":176,"region":177,"url":712,"description":713,"useCases":714,"indexable":261},"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":716,"label":717,"issuer":176,"region":177,"url":718,"description":719,"useCases":714,"indexable":261},"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":721,"label":722,"issuer":723,"region":653,"url":724,"description":725,"useCases":726,"indexable":261},"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":728,"label":729,"issuer":176,"region":177,"url":730,"description":731,"useCases":732,"indexable":261},"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":734,"label":735,"issuer":736,"region":653,"url":737,"description":738,"useCases":732,"indexable":261},"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":740,"label":741,"issuer":742,"region":285,"url":743,"description":744,"useCases":732,"indexable":261},"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":746,"label":747,"issuer":176,"region":177,"url":748,"description":749,"useCases":750,"indexable":261},"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":752,"label":753,"issuer":754,"region":653,"url":755,"description":756,"useCases":750,"indexable":261},"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":758,"label":759,"issuer":187,"region":188,"url":760,"description":761,"useCases":86,"indexable":261},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":763,"label":764,"issuer":176,"region":177,"url":765,"description":766,"useCases":86,"indexable":261},"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":164,"label":768,"issuer":176,"region":177,"url":769,"description":770,"useCases":86,"indexable":261},"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":772,"label":773,"issuer":774,"region":177,"url":775,"description":776,"useCases":777,"indexable":261},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":779,"label":780,"issuer":781,"region":653,"url":782,"description":783,"useCases":621,"indexable":261},"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.",{"id":785,"label":786,"issuer":176,"region":177,"url":787,"description":788,"useCases":621,"indexable":261},"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":790,"label":791,"issuer":176,"region":177,"url":792,"description":793,"useCases":541,"indexable":261},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":795,"label":796,"issuer":797,"region":798,"url":799,"description":800,"useCases":571,"indexable":261},"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":167,"label":802,"issuer":803,"region":177,"url":804,"description":805,"useCases":85,"indexable":261},"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":166,"label":807,"issuer":182,"region":177,"url":183,"description":808,"useCases":85,"indexable":261},"UK APP scam reimbursement rules","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":810,"label":811,"issuer":812,"region":188,"url":813,"description":814,"useCases":492,"indexable":261},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":816,"label":817,"issuer":176,"region":177,"url":818,"description":819,"useCases":492,"indexable":261},"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":821,"label":822,"issuer":823,"region":653,"url":824,"description":825,"useCases":492,"indexable":261},"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.",1790598307466]