Fraud Detection in Ecommerce Platforms

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.

Threat Intelligence is widely utilized in cybersecurity applications like DFIR, threat hunting, and red teaming, etc. However, one often-overlooked but critical application lies beyond traditional cybersecurity: anti-fraud operations. In many organizations, anti-fraud teams operate separately from cybersecurity teams, often with minimal information sharing about cyber threats. This siloed approach can lead to missed opportunities for identifying and mitigating fraud. Ideally, there should be a fusion of fraud prevention and Threat Intelligence to strengthen defenses and improve outcomes. A great starting point to integrate these efforts is by extending your Priority Intelligence Requirements (PIRs) to include the needs of your anti-fraud team. Collaborating with them to understand their requirements is essential for a seamless fusion of efforts. Fraud isn't limited to the banking industry. If you’re in retail, aviation, travel, e-commerce, food services, or any industry with a loyalty program, you’re a potential target for fraud. Fraud has emerged as one of the most critical challenges for businesses worldwide, causing billions of dollars in losses annually in developed economies. Threat Intelligence can bring value to anti-fraud teams by: - Understand which actors are most active and the fraud schemes they commonly employ. - Detect and analyze unique behaviors of threat actors to fine-tune fraud detection systems. - Identify and mitigate risks associated with breached consumer accounts. - Link fraudulent activity to specific groups or actors for better context and response. - Pinpoint merchants whose systems may have been breached and used in fraudulent transactions. - Track and research new platforms used for scams or phishing to preempt attacks. - Study malware to enhance detection capabilities within end-user applications and devices. Fraud detection and prevention should not operate in isolation from cybersecurity. A unified approach that blends Threat Intelligence with anti-fraud strategies can significantly enhance your organization's ability to combat fraud effectively. Start by fostering collaboration between teams and expanding your intelligence priorities to align with the specific threats your industry faces.

🚨 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

One small mistake cost this eCommerce brand on Salesforce Commerce Cloud thousands - without them even realizing it. I noticed their gift card balance lookup form had no protections: - No CAPTCHA - No rate limiting - Nothing. In just 48 hours, bots ran over 100,000 gift card balance checks, uncovering thousands of dollars in active funds. Customers had no idea their money was disappearing. Neither did the company - until it was too late. Here's the thing: 𝗛𝗮𝗰𝗸𝗲𝗿𝘀 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘀𝘁𝗲𝗮𝗹 𝗰𝗿𝗲𝗱𝗶𝘁 𝗰𝗮𝗿𝗱𝘀. 𝗧𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗮 𝗺𝘂𝗰𝗵 𝗲𝗮𝘀𝗶𝗲𝗿 𝘄𝗮𝘆 𝘁𝗼 𝗴𝗲𝘁 𝗳𝗿𝗲𝗲 𝗺𝗼𝗻𝗲𝘆. This is how they do it, and why most businesses never see it coming. 𝟭. 𝗚𝗶𝗳𝘁 𝗖𝗮𝗿𝗱 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗟𝗼𝗼𝗸𝘂𝗽𝘀 𝗔𝗿𝗲 𝗮 𝗣𝗿𝗶𝗺𝗲 𝗧𝗮𝗿𝗴𝗲𝘁 𝗳𝗼𝗿 𝗕𝗼𝘁𝘀 When was the last time you checked how many times your gift card lookup was used? Because if you don't know, hackers do. In short, bots can rapidly cycle through random card numbers and check balances. If your lookup tool has no protection, fraudsters can: - Test thousands of numbers in minutes - Find active balances - Cash them out or sell them on the dark web And the worst part? Most companies don't even realize it's happening. 𝟮. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻𝘀, 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗜𝘀 𝗟𝗼𝘀𝗶𝗻𝗴 𝗠𝗼𝗻𝗲𝘆 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗥𝗲𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗜𝘁) Most eCommerce businesses 𝗱𝗼𝗻'𝘁 𝘁𝗿𝗮𝗰𝗸 how often their gift card lookup tool is being used, and that means they don't notice when bots are running thousands of automated checks. 𝘏𝘢𝘤𝘬𝘦𝘳𝘴 𝘭𝘰𝘷𝘦 𝘵𝘩𝘪𝘴, 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘪𝘵 𝘮𝘦𝘢𝘯𝘴 𝘵𝘩𝘦𝘺 𝘤𝘢𝘯 𝘴𝘵𝘦𝘢𝘭 𝘧𝘳𝘰𝘮 𝘺𝘰𝘶 𝘪𝘯 𝘱𝘭𝘢𝘪𝘯 𝘴𝘪𝘨𝘩𝘵. Attackers don't just check balances... they 𝗱𝗿𝗮𝗶𝗻 𝘁𝗵𝗲𝗺 before customers even realize their funds are gone. And by the time fraud complaints roll in, the company is already dealing with: - Chargebacks - Lost revenue - Angry customers How much money are you losing right now? 𝟯. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗕𝗮𝘀𝗶𝗰 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗖𝗮𝗻 𝗦𝘁𝗼𝗽 𝗠𝗼𝘀𝘁 𝗔𝘁𝘁𝗮𝗰𝗸𝘀 The good news? Blocking these attacks doesn't require an expensive security overhaul. Simple protections like: - Invisible reCAPTCHA - Rate limiting - Email verification can stop automated bots quickly. Even small changes, like monitoring for traffic spikes, can help identify fraud early. One eCommerce brand I worked with saw bot attacks drop significantly overnight after I set up invisible reCAPTCHA for them. A single fix saved them 𝘁𝗵𝗼𝘂𝘀𝗮𝗻𝗱𝘀. Think of it this way: 𝗛𝗮𝗰𝗸𝗲𝗿𝘀 𝗮𝗿𝗲 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗲𝗮𝘀𝗶𝗲𝘀𝘁 𝘄𝗮𝘆 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗶𝘁 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀, 𝗮𝗻𝗱 𝘂𝗻𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝗲𝗱 𝗴𝗶𝗳𝘁 𝗰𝗮𝗿𝗱 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝗹𝗼𝗼𝗸𝘂𝗽 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗮𝗿𝗲 𝗮𝗻 𝗼𝗽𝗲𝗻 𝗶𝗻𝘃𝗶𝘁𝗮𝘁𝗶𝗼𝗻. And they aren't waiting. They're testing your system right now. The only question is: will you let them win?

I never thought it would happen to me. One day, I noticed a spike in chargebacks. I knew something was wrong, but I didn’t know what. I started by investigating the types of fraud we were experiencing. From fake accounts to transaction fraud, it was overwhelming. Here’s how to detect and prevent fraud at every stage of the customer journey: Stage 1: Data Collection Data is your first line of defense. • Gather as much user data as possible. • Track device information, IP addresses, and user behavior. • Monitor changes in user activity. Understanding user patterns helps in identifying anomalies early. Stage 2: Basic Risk Scoring Identify low-hanging fruit. • Use simple rules to score transactions. • Look for mismatched billing and shipping addresses. • Flag unusual purchasing behaviors. This stage catches the most obvious fraud attempts. Stage 3: Dynamic Friction Balance security and user experience. • Implement step-up authentication for suspicious activities. • Use dynamic risk based routing • Introduce verification processes at critical points. Dynamic friction helps reduce fraud without hurting conversion rates. Stage 4: Advanced Analytics Deep dive into data for insights. • Use machine learning to detect patterns. • Analyze transaction histories and behaviors. • Integrate third-party data sources for enhanced detection. Advanced analytics provide a comprehensive view of potential threats. Stage 5: Continuous Optimization Stay ahead of evolving threats. • Regularly update your fraud detection rules. • A/B Test and refine your strategies. • Stay informed about new fraud techniques and trends. Continuous testing ensures your not two steps behind fraudsters. A comprehensive fraud strategy requires a layered approach.

Fraudulent activities pose a significant threat to many businesses, making it crucial to detect and prevent them to protect both the company's reputation and bottom line. In a blog post by the engineering team from Booking.com, they share their innovative approach to combating fraud using graph technology. The rationale behind leveraging graph technology for fraud detection is straightforward: often, there are hidden links between various actors, identifiers, and transactions. For example, if an email address has been previously associated with fraudulent activity, it provides valuable context for future detection. This interconnected nature makes graph-based features highly effective for identifying fraud. The team at Booking built a graph using historical data, such as reservation requests. In this graph, nodes represent transaction identifiers like account numbers and credit card details, while edges connect identifiers that have been observed together before. When assessing fraud risk, they query the graph database to build a local graph centered around the request identifier, which helps to evaluate the likelihood of fraudulent behavior. One aspect that stands out is the dynamic visual representation of how the graph evolves with customer interactions, making it easier to understand the benefits of graph technology in fraud detection. It serves as a nice introduction to the potential of graph technology in combating fraudulent activities. #machinelearning #graph #datascience #analytics #fraud #detection – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gQAwSz7D

Imagine this--you order a product from an e-commerce platform, receive it, and then initiate a return claiming "damaged item" or "wrong product received." 𝐒𝐨𝐦𝐞𝐨𝐧𝐞 𝐟𝐨𝐮𝐧𝐝 𝐚 𝐰𝐚𝐲 𝐭𝐨 𝐬𝐜𝐚𝐥𝐞 𝐭𝐡𝐢𝐬 𝐢𝐧𝐭𝐨 𝐚 ₹1.1 𝐜𝐫𝐨𝐫𝐞 𝐬𝐜𝐚𝐦? That is exactly what happened when Myntra became a victim of one of the most well-orchestrated refund frauds in Indian e-commerce history. A network of fraudsters placed over 5,000 fake orders. In Bengaluru alone, approximately 5,529 fraudulent orders were found. The loophole they exploited? Myntra’s refund policy. This is what they did: 1️⃣ Placed an order for premium products. 2️⃣ Filed a refund claim--citing “wrong product received” or “damaged item.” 3️⃣ Received a refund without returning the original item. 4️⃣ Repeated the process at scale with multiple fake accounts. Since refunds were processed before verification, Myntra was bleeding losses before realising something was off. ₹1.1 crore was gone before the fraud was caught. This has woken up the whole industry to have better processes in place by creating: ✅ Stricter Refund Verification – Refunds for high-value orders now require mandatory product verification before processing. ✅ Quicker and 100% reliable Reconciliation - That is what we are building at FAB MAVEN with our AutoReco tool. ✅ AI-Driven Fraud Detection – Patterns like excessive returns from a single account are now being auto-flagged for review. ✅ Stronger Buyer History Checks – Platforms now track return abuse patterns, leading to account bans for serial offenders. ✅ Logistics Enhancements – Some platforms have introduced tamper-proof return packaging and doorstep quality checks before refunds are approved. Have you seen return policies change on platforms you shop from? #ecommerce #shopping #myntra #amazon #refunds #reconciliation #fpa #cfo

Feeling stuck in the Build Trap while tackling platform fraud? Here's a fresh take on discovery. The core of discovery is pinpointing the true problem. Your task is to reduce fraud: a clear issue not only for the business but for the customer, too. High levels of fraud can restrict transactions, hurting your service delivery and user trust. With fraud, traditional discovery methods, like customer interviews, often fall short. You can’t exactly go talk to fraudsters. So what do you do? Start by understanding how fraud enters your system. Where does it happen? What patterns keep showing up? This kind of discovery requires empathy (not sympathy) for fraudsters. You need to understand their behavior to anticipate and stop it. Consider these steps to improve your fraud discovery process: 1. Map Fraud Points: Identify where and how fraud occurs. Look for patterns in data that might suggest vulnerabilities. 2. Analyze User Behavior: Understand genuine user actions that might be exploited. This helps predict and mitigate potential fraud tactics. 3. Test Interventions: Experiment with small-scale fraud prevention measures. Use data to see what works and iterate quickly. 4. Balance Controls and Experience: Too strict, and you alienate users. Too lenient, and you're at risk. Find that sweet spot. Discovery in this case is about getting to the root of how fraud works on your platform, so you can stop it. Treat it like any product challenge: understand the behavior, test ways to address it, and measure the impact. How do you approach discovery with fraud in mind? Let me know your strategies in the comments.