Online Payment Fraud Prevention

If you think Stripe Radar is enough, you're covering maybe a third of the fraud vectors that matter. Modern fraud isn't just stolen cards. It's account takeovers, synthetic identities, bot attacks, friendly fraud, and money laundering each requiring its own defense layer. That's why fraud prevention has split into a full stack of specialized tools. Card fraud is still massive, but its share of total losses keeps shrinking. In many verticals, transactional fraud is no longer the biggest threat. Sift is a good illustration. Often seen as a generic fraud tool, it processes signals far beyond payments: ~70% of its detections relate to non-payment events (logins, signups, content abuse) ~1 trillion events analyzed per year ~34,000 sites and apps protected globally And here's the shift almost nobody talks about: the card networks and bureaus are quietly buying up the entire stack. Visa now owns Featurespace and Verifi. Mastercard owns Ethoca and NuData. Equifax owns Kount and Midigator. LexisNexis owns ThreatMetrix. Entrust absorbed Onfido. "Beyond Stripe Radar" increasingly means "beyond a handful of giants." The full stack today: - End-to-End Fraud Platforms: Sift, Forter, Riskified, Signifyd, Sardine, SEON, ClearSale, NoFraud, Ravelin Technology - Device Intelligence & Behavioral Biometrics: Fingerprint, Incognia, BioCatch, ThreatMetrix, Castle, SHIELD, Callsign, NuData Security, a Mastercard company, Darwinium, Trustfull - Identity Verification & KYC: Persona, Alloy, Sumsub, Socure, Onfido, Veriff, Jumio Corporation, Incode, iProov, Trulioo, Mitek Systems, IDnow, GBG - AML & Transaction Monitoring: ComplyAdvantage, Hawk AI, Unit21, Feedzai, NICE Actimize, Quantexa, SAS, FICO, Nasdaq Verafin, Chainalysis, ACI Worldwide, DataVisor - Bot Protection & Account Takeover: Arkose Labs, HUMAN, DataDome, Cloudflare, Kasada, Imperva, Akamai, Netacea, Trusona - Chargeback & Dispute Management: justt, Chargeflow, Ethoca, Verifi Inc., Kount, Midigator, Chargebacks911 Attacker behavior explains the split. AI-generated synthetic identities, credential stuffing at scale, and organized fraud rings have made single-layer defenses obsolete. A rough 2026 picture of where losses sit: ~45% account takeovers and identity fraud ~30% transactional and card fraud ~25% chargebacks and friendly fraud Real-time decisioning, shared fraud networks, and AI-driven risk scoring keep accelerating the trend. Fraud prevention is no longer a feature. It's becoming critical infrastructure for every digital business. PS: I post about payments with Suby, stablecoins & the reality of building a payment startup, every week. Follow for more!

Here's how Stripe detects frauds with a 99.9% accuracy in 100 milliseconds (that too by checking over 1000 parameters for one transaction) Fraud detection in online payments isn’t just about stopping bad transactions it’s about doing it fast, at scale, and without blocking legitimate users. Stripe’s fraud prevention system, Radar, evaluates 1,000+ signals within 100 milliseconds to make decisions. Here’s how it works and why it’s so effective: 1. ML Models That Learn and Scale Stripe started with simple ML models (logistic regression) but quickly scaled to hybrid architectures combining: –XGBoost for memorization (catching known patterns). –Deep Neural Networks (DNNs) for generalization (handling unseen patterns). –Key Problem: XGBoost couldn’t scale or integrate modern ML techniques like transfer learning and embeddings. –The Solution: Stripe moved to a multi-branch DNN-only architecture inspired by ResNeXt. This setup allowed it to memorize patterns while staying scalable. It reduced training times by 85%, enabling multiple experiments in a single day instead of overnight runs. 2. Learning From Real Fraud Patterns Radar doesn’t just rely on static rules, it learns from data across Stripe’s network. –Engineers analyze fraud attacks in detail, e.g., patterns of disposable emails or repeated card testing. –Features like IP clustering and velocity checks were added to detect suspicious activity. –Fraud insights are shared across the network, so lessons learned from one business protect others automatically. Example: Analyzing IP patterns helped detect high-volume attacks where fraudsters used multiple stolen cards from the same source. 3. Scaling With More Data, Not Just Smarter Models Stripe realized that more training data could unlock better performance, similar to modern LLMs like GPT models. It tested scaling datasets by 10x and 100x. Result? Performance kept improving, confirming that larger datasets and faster training cycles work better than complex rules alone. Key Insight: Bigger datasets help uncover rare fraud cases, even if they occur in only 0.1% of transactions. 4. Explaining Fraud Decisions Clearly Fraud systems often act like black boxes, leaving businesses guessing why a payment failed. Stripe built Risk Insights to provide clear explanations: –Shows features contributing to fraud scores like mismatched billing and shipping addresses. –Displays maps and transaction histories for visual context. –Enables custom rules to fine-tune fraud checks for specific business needs. Result: Businesses trust Radar’s decisions because they can see why a payment was flagged. 5. Constant Adaptation to Stay Ahead Fraud patterns evolve, so Stripe built Radar to adapt in real time: Uses transfer learning and multi-task learning to generalize better. Incorporates insights from the dark web and emerging fraud tactics. Continuously retrains models without disrupting performance.

Being in the fraud prevention industry gives me an insider’s view of how fraud attacks work - including seeing new patterns emerge. Here are recent insights on how fraudsters are increasingly targeting people to take control of their bank accounts and initiate unauthorized wire transfers. 📞 The Phone Call Scam: Scammers exploit the vulnerability in PSTN to spoof caller IDs, making it seem like the call is coming from a trusted bank. A number of well-known VoIP providers make this possible. 🔓 Remote Access: Once they establish contact, scammers mention there is some suspicious activity or other important reason behind their call. They then persuade victims to install remote desktop applications like AnyDesk, or to turn on WhatsApp or Skype's screen sharing. This allows them to access banking apps and initiate transfers. This helps them to intercept login data and one-time passcodes. Banks also don't insure against such scams, leaving victims exposed. 🤖 AI in Voice Scams: Imagine combining voice recognition with GPT-based text-to-speech technology. Scammers scale their operations massively, this is a future risk we must prepare for now. So what proactive measures can banks and digital wallets take? 1. Customer Education: Many banks already do this; keeping their customers informed about official communication channels and the importance of calling back through their verified numbers. 2. One-Time Passcodes for Payments: OTPs aren’t just for logins but also useful for transactions, with detailed payment information included. 3. Being On a Call During Transactions: The top FinTechs are already looking into, or developing technology to detect if a customer is on a call (phone, WhatsApp, Skype) during banking activities. 4. Detect Remote Access: Implement detection mechanisms for any remote access protocol usage during banking sessions. 5. Behavior and Velocity-Based Rules: Sophisticated monitoring should be used to flag activities in real-time based on unusual behaviour and transaction speed. 6. Device, Browser, and Proxy Monitoring: This is a quick win, as there are many technologies available to flag unusual devices, browsers, and proxy usage that deviates from the customer's norm. 7. Multiple Users on Same Device/IP: Ability to identify and flag multiple customers who are using the same device or IP address in one way to detect bots. 8. Monitoring Bank Drops and Crypto Exchanges: Pay special attention to transactions involving neobanks, crypto exchanges, or other out-of-norm receiving parties, to identify potential fraud. Some of them might not ask for ID and even if they do, it can be easily faked with photoshopped templates. Hope you find that useful, and in the meantime, I’d love to hear what other emerging threats you’ve seen or heard of. Fostering these open conversations is what enables us all to unite together against combating fraud 👊 #FraudPrevention #CyberSecurity #DigitalBanking #ScamAwareness #AIinFraudDetection

5 Fraud Prevention Strategies Treasury Leaders Must Prioritize in 2026 Fraud is evolving faster than most control frameworks and Treasury sits right at the center of that risk. As more payments move to API rails, as ISO 20022 introduces richer data, and as attackers shift toward credential compromise and beneficiary manipulation, the controls that worked 5 years ago no longer hold. Here are 5 strategies Treasury and Finance leaders should advance in 2026 to strengthen protection without slowing down operations: 1. Modernize Payment Controls for API Treasury Flows Many organizations have upgraded to APIs for speed but haven’t updated their fraud controls. Treasury needs: • IP allow-listing • API key rotation • Transaction-level authentication • Real-time integrity checks API connectivity must be treated as a payment channel, not an IT feature. 2. Apply Zero-Trust Access Across All Treasury Systems The fastest-growing threat is credential compromise which targets TMS, ERP, and bank portals. Treasury must eliminate single points of failure through: • Role-based access • MFA/SSO • Quarterly access certification • Device/location-based restrictions Zero-Trust isn’t optional. It’s important. 3. Centralize Beneficiary & Vendor Master Governance Most fraud losses begin with beneficiary manipulation, not payment file tampering. Treasury teams should enforce: • Segregation of duties • Mandatory callbacks for changes • Bank-side name matching (where available) • Real-time alerts for edits If you secure the master data layer, you shut down the majority of payment fraud attempts. 4. Utilize ISO 20022 Data to Strengthen Detection ISO 20022 gives treasury structured, high-quality data that improves fraud analytics. Use cases include: • Purpose codes to identify abnormal payment types • UETR tracking to flag unusual routing patterns • Structured remittance fields to validate payment intent Better data = better detection and faster exception handling. 5. Use Intelligent Anomaly Detection Across All Payment Channels Volume, speed, and complexity make manual monitoring ineffective. Treasury needs anomaly detection that identifies: • Deviations from historical behavior • Unusual timing or amounts • Suspicious user activity These tools identify risks humans simply cannot catch early enough. Fraud evolves when controls are ineffective. Treasury teams that modernize payment governance, strengthen access, secure beneficiary data, and utilize ISO 20022 and AI-driven analytics will be the ones that stay ahead of emerging threats in 2026. Which fraud control is becoming a priority for your organization?

🎯 Why a Full 3DSecure Strategy Can Backfire for Merchants. Especially Outside the EU Since March 2022, Strong Customer Authentication has been mandatory for most online card transactions in the EU. While exemptions exist, many merchants defaulted to 3DSecure as their primary fraud prevention tool. But is relying solely on a full 3DS strategy the right move? From my experience as Head of Payments for a flash sales group with €4B GMV, the answer is often no. Here’s why. 💡 A Real-Life Example Before SCA, 3DSecure was mostly reserved for “risky” transactions, think superstar concert tickets or last-minute travel bookings. While it helped secure these transactions, we noticed a troubling pattern: 👉 Even for loyal, engaged customers, some 3DS-authenticated transactions were declined with a code 59 (Fraud Suspicion). What we uncovered: • Most declines were tied to a major EU issuer. • Affected transactions fell in the €50–€150 range, the sweet spot of our average basket. 📞 The Fix? Switching From "Force 3DS" to "No 3DS" After discussions with the issuer, we found the root cause: 3DS authentication triggered issuer-side risk algorithms, including factors like outdated methods (e.g., SMS OTP), untrusted devices, or the bank’s internal chargeback rates. By retrying the same transactions without 3DS, +99% were approved. 🌍 Outside the EU: The Challenge Remains Globally, 3DS introduces a liability shift to issuers—but it also activates issuer risk algorithms that can result in declines due to: • The merchant’s country. • The acquirer’s risk profile. • The bank’s chargeback history. 🛑 Why 3DSecure Isn’t a Silver Bullet While 3DS can reduce fraud liability, it comes with significant trade-offs: 1. Friction: Increased cart abandonment from added authentication steps. 2. Declines: Legitimate customers being blocked, eroding loyalty. 3. Fraud Adaptability: Fraudsters quickly adjust to exploit vulnerabilities. 🚀 The Smarter Fraud Strategy Merchants need a dynamic fraud prevention approach to balance security and conversion. Here’s how: • Leverage risk-based exemptions under SCA. • Use machine learning to detect fraud intelligently. • Employ smart retries, dynamically based on transaction risk profiles. The real goal: Prevent fraud, maximize approvals, and minimize customer friction. 💬 What’s Your Take? Have you encountered similar issues with 3DS, inside or outside the EU? Let’s discuss how merchants can navigate the complexity of fraud prevention without sacrificing customer experience. #Payments #3DSecure #FraudPrevention #ECommerce #CustomerExperience #SCA -------- 𝘛𝘩𝘦 𝘰𝘱𝘪𝘯𝘪𝘰𝘯𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘮𝘺 𝘰𝘸𝘯 𝘢𝘯𝘥 𝘥𝘰 𝘯𝘰𝘵 𝘯𝘦𝘤𝘦𝘴𝘴𝘢𝘳𝘪𝘭𝘺 𝘳𝘦𝘧𝘭𝘦𝘤𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘰𝘮𝘱𝘢𝘯𝘺.

Digital payment frauds are perhaps the single biggest crime in the country today. In FY 2024 alone, Indians have lost ₹1,400 crore to digital payment fraud - a five-fold surge in just one year!! Scammers are evolving beyond simple OTP phishing into sophisticated high-value deep fakes and synthetic identity theft that traditional security can’t catch. SIM-swap attacks. OTP interception. Phishing for authentication codes. These are systemic vulnerabilities baked into SMS-based authentication, and for years the industry response was simply more OTP retries. To secure India's digital future, we must shift from reactive alerts to proactive biometrics and AI-driven behaviour monitoring that verify the person, not just the password. It’s great that Flipkart, Axis Bank & PayU have taken a fundamentally different approach. Their new biometric authentication replaces the SMS OTP entirely with fingerprint or Face ID, bound to the user's device rather than their phone number. The article says PayU manages merchant-side security and authentication flows, while Axis Bank uses Wibmo for issuer-level biometric verification. Two protection layers working in tandem. What stands out most is that they chose to lead here rather than wait. Bringing this to one of India's largest checkout experiences means the impact reaches millions of consumers immediately, rather than sitting as a niche feature for a small audience. Addressing the root cause and building security that consumers actually enjoy using that combination is rare and worth celebrating. #CyberSecurity #DigitalPaymentsFraud #FraudPrevention | Sunainaa Chadha | Business Standard | https://lnkd.in/gvY6p46m

Fraud wasn’t supposed to be a core product challenge. But for most businesses operating online today, it has staunchly become one. In 2024, Indian businesses lost ₹22,842 crore to cybercrime. That’s a 206% increase over the previous year. The first few months of 2025 have already added another ₹7,000 crore in losses. This isn't just a compliance or security concern anymore. It shows up as frozen accounts, locked working capital, rising chargebacks, and misuse through stolen cards, fake UPI payments, and promo abuse. What surprised us most was how quickly chargebacks became part of the everyday reality for merchants: 1. More than half involve deliberate abuse 2. Smaller businesses aren’t spared - around 30 percent of Indian SMEs now report direct losses from fraud, with revenue hits of up to 5 percent. The nature of fraud has changed. Attacks are faster, more coordinated, and more sophisticated. The usual playbook of reacting after the damage doesn't hold up anymore. We decided to rebuild our approach from first principles. RiskShield is what came out of it. It’s a fraud detection engine that runs within the payment flow. It scores every transaction in real time using machine learning, detects fraud rings using graph intelligence, syncs with government risk data like I4C, DoT blacklist, NCRB, and blocks bad actors mid-transaction. It also flags early signs of promo abuse, card testing, and UPI manipulation. So far, RiskShield has helped block over ₹1,700 crore in fraud attempts. It has flagged 2 crore high-risk signals and protected more than 6,600 merchants. The system operates quietly in the background, with an F1 score of 87 percent which is a measure that balances precision (how often fraud alerts are correct) and recall (how much fraud we actually catch) and recall close to 95 percent. Most issues are prevented before anyone files a complaint. There’s still more work to do, but one thing is clear to us now: Fraud cannot be treated as an after-effect. It has to be designed against from the beginning. PS. Here's the flow we have built ⬇️

Subscription fraud is often invisible - but its impact is significant. Fake free trials and recurring payment abuse rarely appear fraudulent at the start. They typically mimic legitimate user behavior, making detection challenging. Common fraud patterns in subscription businesses • Multiple accounts created by the same user • Use of temporary emails and shared or stolen cards • Abnormal usage during trial periods • Intentional chargebacks after extensive consumption Business impact • Revenue leakage • Increased chargeback ratios • Payment gateway penalties • Distorted growth and retention metrics • Higher customer acquisition costs How fraud is detected effectively • Device and IP intelligence • Behavioral signal analysis • Payment reuse and failure patterns • Usage anomalies during trials and renewals Prevention strategies that scale • Limit free trials per device and payment method • Apply step-up verification for high-risk users • Monitor usage prior to renewals • Block bots and high-risk IP ranges • Leverage AI models to identify evolving fraud patterns Outcomes of a strong fraud strategy • Reduced fake users • Lower chargebacks • Accurate business metrics • Protected recurring revenue • Improved trust with genuine customers Fraud prevention is not friction. It is a safeguard for legitimate users and sustainable growth.

If you know what to look for, you can catch fraudsters before they run the same playbook again. Here’s what we’ve seen work across marketplaces, delivery, gig platforms - anywhere repeat abuse is a problem: ✅ Look beyond the signup Email, phone, device - those are just the wrappers. Start by tracking how new accounts behave in their first few sessions. ✅ Watch for recycled patterns Same sequence of events. Same order flow. Same timing. Bad actors reuse what works - often down to the click. ✅ Link accounts through behavior, not just static signals Did this user skip the same steps as a banned one? Visit the same pages, in the same order, from the same geography? That’s a fingerprint most tools can’t see. ✅ Don’t rely on manual review to connect the dots If it takes your team hours to flag a ban evader, they’re already on to the next account. Use automation to prevent their return. ✅ Use one central view across login, payment, and post-transaction abuse Fraud lives between silos. If your data is stuck in separate systems, you’re always two steps behind. Stopping fraud once isn’t enough. The real win is keeping them out for good.

🚨 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐌𝐨𝐭𝐢𝐨𝐧 — 𝐅𝐫𝐚𝐮𝐝 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧 by DEUNA Traditional, static fraud rules often fall short — tightening controls so much that they block good customers, or leaving gaps that allow fraud to slip through. Agentic intelligence changes this paradigm. By leveraging historic transaction data and strategic signals (PSPs, payment methods, geographies, behavioral trends), it dynamically recommends risk controls tailored to each scenario. — 𝐃𝐞𝐞𝐩 𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 Historic transaction patterns and behavioral signals are integrated with granular specifics like BIN, card franchise, and geography. This allows the system to distinguish between legitimate customers and potential fraud with precision. → The Walt Disney Company leverages historical subscription behavior data to differentiate genuine recurring payments from suspicious account takeovers, reducing false declines. — 𝐋𝐨𝐰 𝐑𝐢𝐬𝐤 𝐯𝐬 𝐇𝐢𝐠𝐡 𝐑𝐢𝐬𝐤 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 Low-risk transactions flow seamlessly with minimal friction, boosting conversion and improving customer satisfaction. High-risk transactions are dynamically routed through targeted fraud prevention layers — activating the most relevant PSPs and antifraud providers at the right moment. → Uber adapts fraud checks by geography, applying stronger measures in regions with high fraud incidence while keeping repeat riders’ payments frictionless. — 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐫 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐅𝐫𝐚𝐮𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 Risk scoring is factored into provider and PSP selection to balance approval rates, cost efficiency, and security. → Airbnb leverages intelligence to dynamically adjust fraud controls by market and traveler profile — applying stronger authentication in high-risk regions or for first-time guests, while allowing frictionless payments for trusted, repeat customers. — 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 Fraud tools are embedded directly into the orchestration layer, enabling smarter allocation: fraud detection where it is most impactful, and seamless flows where customers have already proven trustworthy. → Worldline merchants leverage adaptive authentication, activating 3DS selectively when intelligence identifies elevated risk — enabling smoother experiences for low-risk customers. — The Result → Intelligent Growth with Protection ✅ Higher approval rates without compromising safety ✅ Smarter allocation of fraud tools where they matter most ✅ Frictionless checkout experiences for trusted customers — This is proactive fraud prevention in motion — moving beyond rigid rules into an era of intelligent orchestration, where every payment decision optimizes both security and customer satisfaction at scale. — Source: DEUNA ► Subscribe to The Payments Brews: https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... | Marcel van Oost