Strategies For Reducing Ecommerce Fraud

Stop trying to cheat the #Amazon price algorithm. It won't work and hurt your vendor business. 🚩👇 Many vendors still look for shortcuts: ❌ Launching different pack sizes ❌ Bundling existing ASINs ❌ Creating 'fake' UPCs If your teams are proposing any of these strategies, stop them immediately. Amazon will quickly identify that these aren’t exclusive SKUs and trigger its similarity price-matching. Your bundles will then be compared against the single units, or matched by price per gram/oz or per ml. 𝗪𝗼𝗿𝘀𝗲, 𝗔𝗺𝗮𝘇𝗼𝗻 𝗺𝗮𝘆 𝘀𝘂𝗽𝗽𝗿𝗲𝘀𝘀 𝘆𝗼𝘂𝗿 '𝗲𝘅𝗰𝗹𝘂𝘀𝗶𝘃𝗲' 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘀𝘁 𝘁𝗵𝗲𝗺 𝗳𝗿𝗼𝗺 𝗶𝘁𝘀 𝗺𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲. The better way? ✅ Develop truly exclusive product lines ✅ List bundles that don't exist in retail ✅ Launch differentiated SKUs (25%+ ingredient change) ✅ Innovate the USP (advent calendars, gifting hampers, etc.) ✅ Build entry offers (assorted packs) Because if you sell the same products everywhere online, the only differentiating factor left is price. Which Amazon will match – whether you like it or not. --- How do you protect your Amazon margins? Let me know in the comments! #amazonvendor #amazonstrategy

🛍️ Black Friday–Cyber Monday was forecast to shatter records again this year with over $150bn in global eCommerce volume. But there is a side of Black Friday–Cyber Monday we do not talk about enough. It is also the biggest fraud window of the year. What surprised me most is that “digital shoplifting” is now costing online businesses more than $125bn annually, and a lot of it does not look like traditional fraud. Here is what that includes: ▪️ "Friendly" fraud: customers filing “Not Received” or “Not as Described” chargebacks for items they have received.  ▪️ Stolen card fraud: transactions made with stolen cards or synthetic identities. ▪️ Return and refund abuse: serial returners, wardrobers, and repeat refund claims. The timing makes it even harder. Most disputes show up 30 to 90 days after Black Friday–Cyber Monday, which means they hit businesses in January when margins are already tightening. Some large retailers like ASOS and Zalando have started blocking serial returners entirely. But for most brands, that level of manual policing is not scalable. So what is the alternative? There is a growing set of tools that use AI and network level data to help brands stay ahead of disputes instead of only reacting to them. Platforms like Chargeflow help with: ▪️ Automatically contesting chargebacks ▪️ Identifying risky orders before fulfillment. ▪️ Alerting merchants before a chargeback is filed so they can issue a refund and avoid the dispute. ▪️ Scoring transactions after checkout to flag potential fraud patterns. I did also see that Chargeflow are offering $10,000 in free chargeback protection. I added the link in the comments for anyone who wants to take a look 👇 

Fraud detection at scale is less about finding bad actors and more about handling volume without breaking your team. When thousands of cases require manual review, even simple checks could become bottlenecks. In this tech blog, the engineering team at Razorpay shares how they rebuilt their fraud detection workflow with an AI system called Bumblebee. What started as a manual review process consuming thousands of hours each month was transformed into an automated system that evaluates merchants in seconds, with higher consistency and accuracy.  - Early attempts from the team relied on a single agent that sequentially gathered data, reasoned through it, and made decisions. It worked in principle but ran into real-world limits: token constraints, slow execution, and fragile scaling.   - The breakthrough was to move toward a multi-agent design, where specialized components handle distinct tasks in parallel. Instead of passing around raw, unstructured data, each component extracts only the relevant signals and produces compact summaries, keeping the system efficient and focused. This shift mirrors how strong human teams operate. Different specialists handle different parts of the problem, then combine their insights into a final decision. By structuring the system this way, they reduced latency, improved accuracy, and made it easier to extend the system over time without rewriting everything. #DataScience #MachineLearning #AI #FraudDetection #MLSystems #MultiAgentSystems #SnacksWeeklyonDataScience – – –  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/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gT4tZJ5S

🚀 𝐁𝐨𝐨𝐬𝐭𝐢𝐧𝐠 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐀𝐈 🚀 This marks the first use case of the 50 use cases in a series where we explore how AI is transforming business. Starting with Fraud detection as I have spent a decent amount of time in this field during the initial phase of my career 😁 In today’s digital world, fraudsters are evolving faster than ever, creating significant challenges for businesses. Traditional fraud detection methods like rule-based systems, statistical models, and human analysis are increasingly ineffective. High false positives, limited adaptability, and difficulty in scaling make these methods fall short. That’s where AI comes in, completely changing fraud detection with machine learning (ML), deep learning (DL), and natural language processing (NLP). AI offers real-time detection with greater accuracy, 𝒔𝒍𝒂𝒔𝒉𝒊𝒏𝒈 𝒇𝒂𝒍𝒔𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒗𝒆𝒔 𝒃𝒚 85% 𝒂𝒏𝒅 𝒓𝒆𝒅𝒖𝒄𝒊𝒏𝒈 𝒅𝒆𝒕𝒆𝒄𝒕𝒊𝒐𝒏 𝒕𝒊𝒎𝒆 𝒃𝒚 30%. Its adaptability and scalability are essential for handling today’s complex fraud tactics. ❗ 𝐊𝐞𝐲 𝐀𝐈 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐢𝐧 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: 1️⃣ Supervised & Unsupervised Learning: Trains systems to detect both known and new fraud patterns. 2️⃣ Neural Networks, CNNs, & RNNs: These deep learning models excel at recognizing complex fraud tactics from vast data. CNNs are great for structured data, while RNNs shine in time-sensitive analysis like transaction histories. 3️⃣ Text Analytics & Sentiment Analysis: Especially useful in industries like e-commerce, analyzing text data can expose signs of fraud. 4️⃣ Regression & Time-Series Forecasting: Helps predict fraudulent activity based on historical data. ❗ 𝐓𝐞𝐜𝐡 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 Deploying AI-based solutions requires a solid tech stack, often built with 𝑷𝒚𝒕𝒉𝒐𝒏, 𝑹, 𝒐𝒓 𝑱𝒂𝒗𝒂, and leveraging frameworks like 𝑻𝒆𝒏𝒔𝒐𝒓𝑭𝒍𝒐𝒘 𝒂𝒏𝒅 𝑷𝒚𝑻𝒐𝒓𝒄𝒉. Cloud platforms like 𝑨𝑾𝑺, 𝑮𝒐𝒐𝒈𝒍𝒆 𝑪𝒍𝒐𝒖𝒅, 𝒂𝒏𝒅 𝑨𝒛𝒖𝒓𝒆 provide the scalable infrastructure necessary to support these solutions. Financially, businesses should be prepared for an initial investment, depending on system complexity. Ongoing maintenance typically costs 10% to 20% of the initial investment. ❗ 𝐓𝐡𝐞 𝐂𝐨𝐬𝐭 𝐨𝐟 𝐈𝐧𝐚𝐜𝐭𝐢𝐨𝐧 Failing to implement AI solutions can lead to significant losses—𝒐𝒏 𝒂𝒗𝒆𝒓𝒂𝒈𝒆, 5% 𝒐𝒇 𝒂 𝒄𝒐𝒎𝒑𝒂𝒏𝒚’𝒔 𝒓𝒆𝒗𝒆𝒏𝒖𝒆 𝒊𝒔 𝒍𝒐𝒔𝒕 𝒕𝒐 𝒇𝒓𝒂𝒖𝒅 𝒂𝒏𝒏𝒖𝒂𝒍𝒍𝒚. Beyond that, reputational damage and regulatory penalties can have long-lasting effects. Stay tuned for more! PS: Vaidyanath R., I would love to hear more from you on this topic hashtag #AI #FraudDetection #MachineLearning #AIForGood #TechSolutions #BusinessGrowth #AIUseCases

𝗨𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝘁𝗼 𝗖𝗼𝗺𝗯𝗮𝘁 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝘂𝗱 The rise of instant payments has made AI-powered fraud detection a necessity. Unlike traditional rules-based systems, AI can spot subtle behavioral patterns across vast datasets in real time—vital for detecting complex, fast-moving fraud. Yet, as AI becomes central to fraud prevention, its responsible and transparent use is just as important. Consumers must be protected not only from fraud but also from the unintended harm of biased or opaque AI models. The stakes are high: an estimated 42.5% of fraud attempts now use AI, and nearly a third are successful. Criminals are evolving too, leveraging deepfakes and generative AI to bypass controls. The global market for deepfake detection is projected to grow 42% annually, from €4.73B in 2023 to €13.5B by 2026. Businesses are responding—three-quarters plan to adopt AI-driven fraud prevention tools—but fewer than a quarter have begun implementation, exposing a gap between awareness and action. At its core, AI’s strength lies in pattern recognition—automatically identifying relationships and anomalies in data. Just as a human analyst might, AI detects shifts such as unusual geolocation, new devices, or behavioral changes. In money-laundering cases, for example, mule accounts often move funds in chains; AI’s ability to view the network as a whole helps uncover these linked transactions. Fraud doesn’t appear in isolation—it often comes in waves and trends. Machine-learning models can evolve as new behaviors emerge, unlike static rules-based systems that require post-loss analysis to update their logic. This adaptability is especially crucial in an era of instant payments, where funds move within seconds. 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝘂𝗱 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗡𝗲𝗲𝗱 𝗳𝗼𝗿 𝗦𝗽𝗲𝗲𝗱 Speed is the main challenge. Instant payments typically settle within 10 seconds, leaving almost no time for manual fraud checks. While some transactions can be delayed if flagged as suspicious, decisions must be made instantly. Rules-based systems struggle here—they tend to generate too many false positives, draining resources and delaying legitimate payments. In contrast, AI-enhanced systems evaluate transactions in real time, combining models and rules to minimize friction. This enables fraud teams to focus their attention on the truly risky cases. Ultimately, AI doesn’t replace human judgment—it amplifies it. By providing real-time intelligence and adapting to new fraud patterns, AI helps businesses strike the balance between security and customer experience. As instant payments continue to expand globally, this balance will define the winners in the next phase of fraud prevention Source Visa #fintech #ai

#FraudPrevention is one of the clearest examples of why real-time #DataStreaming is mission-critical. Companies like PayPal, Capital One, ING Bank, Grab, and Kakao Games use #ApacheKafka and #ApacheFlink to detect and stop fraud before it impacts customers. From preventing payment fraud to stopping gaming abuse in milliseconds, these companies process billions of events, correlate real-time and historical data, and act instantly. Whether it’s #banking transactions, mobility services, or #gaming telemetry, the pattern is the same: - Kafka ingests and distributes events at scale - Flink enriches, correlates, and scores events in real time - Action is taken immediately to prevent losses and protect customers with #AI and #MachineLearning models In a world where waiting even a few minutes can mean losing millions, real-time #StreamProcessing is no longer optional for fraud detection. How is your organization leveraging Kafka and Flink for real-time risk detection or prevention? https://lnkd.in/eftjdUFB

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 ⬇️

One wrong word can suppress your listing, or even flag your account. Most Amazon sellers focus on SEO and conversions when optimizing their listings. But here’s what many overlook: Amazon has hundreds of restricted keywords, phrases, and claims that can instantly trigger suppressions, warnings, or even account suspensions. This isn’t just about obvious things like “CBD” or “THC.” Words like “guaranteed,” “safe,” “eco-friendly,” “anti-bacterial,” “relief,” “detox,” and even holiday references like “Labor Day” can all get your listing flagged if they’re not properly substantiated. Amazon’s compliance models are getting smarter and more aggressive: ➡️ Product titles now have stricter formatting rules and keyword limits. ➡️ Health, safety, and eco-friendly claims must be supported by third-party certifications. ➡️ Promotional terms, trust claims, and subjective language are automatically scanned and suppressed. ➡️ High-risk categories like supplements, cosmetics, and pesticides have zero tolerance for unsupported claims. And here’s the problem: most sellers don’t know which words are forbidden until they get flagged. That’s why I put together a detailed list with hundreds of prohibited and high-risk keywords, along with explanations for why they’re risky, so you can audit your listings before Amazon does. If you'd like a copy, simply drop a “LIST” in the comments and I’ll send it over. #Amazon #Compliance #ListingOptimization #EcommerceStrategy

Welcome to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐀𝐜𝐚𝐝𝐞𝐦𝐲 by Checkout.com — Episode 6 👋 𝐓𝐡𝐞 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐅𝐫𝐚𝐮𝐝 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬: ► Fraud in payments is a growing challenge for merchants, issuers, and payment processors. Fraudulent transactions not only cause financial losses but also damage a merchant’s reputation ► To combat fraud effectively, businesses must leverage fraud detection tools, authentication techniques, and dispute management strategies to stay ahead of bad actors while maintaining a seamless customer experience — 𝐓𝐡𝐞 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐅𝐫𝐚𝐮𝐝 & 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 ► 3-𝐏𝐚𝐫𝐭𝐲 𝐅𝐫𝐚𝐮𝐝 – This occurs when a fraudster uses stolen card details to make purchases. ► 𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲 𝐅𝐫𝐚𝐮𝐝 – A cardholder disputes a legitimate transaction, either by mistake or to reverse a purchase. ► 𝐆𝐨𝐨𝐝 𝐅𝐚𝐢𝐭𝐡 𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐃𝐢𝐬𝐩𝐮𝐭𝐞𝐬 – The customer disputes a payment due to issues with product quality or fulfillment. Fraud prevention strategies must be tailored to identify, assess, and respond to these types of fraud in real time. — 𝐓𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬: 𝐂𝐮𝐭𝐭𝐢𝐧𝐠 𝐃𝐨𝐰𝐧 𝐨𝐧 𝐂𝐚𝐫𝐝 𝐅𝐫𝐚𝐮𝐝 1️⃣ 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐬 – These tools analyze transaction data (e.g., IP addresses, device data...) to assess fraud risks. 2️⃣ 3𝐃 𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧 – Adds an extra layer of protection by requiring customer verification for high-risk transactions. 3️⃣ 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐀𝐈 – Predicts fraud patterns based on historical transactions and behavioral analytics. 4️⃣ 𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 – Converts sensitive payment data into tokens, reducing the risk of stolen card details being misused. 5️⃣ 𝐂𝐡𝐚𝐫𝐠𝐞𝐛𝐚𝐜𝐤 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧 – Strategies like real-time alerts and clear billing descriptors — 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚: 𝐊𝐞𝐲 𝐃𝐚𝐭𝐚 𝐏𝐨𝐢𝐧𝐭𝐬 𝐭𝐨 𝐑𝐞𝐝𝐮𝐜𝐞 𝐅𝐫𝐚𝐮𝐝 Fraud detection relies on rich transaction data to identify suspicious activity and block fraudulent payments: ► Customer Name – Verifies the cardholder’s identity and checks for patterns of fraudulent behavior (e.g., fake names...). ► IP Address – Flags transactions from high-risk regions or locations inconsistent with the customer’s normal behavior. ► Billing Address – Used for Address Verification System (AVS) checks to confirm that the billing address matches the cardholder’s bank records. ► Delivery Address – Helps detect fraudulent transactions by assessing mismatched shipping details. ► Email Address – Identifies fraud patterns, such as disposable email addresses or emails associated with prior chargebacks. Providing complete and accurate data in payment requests enhances fraud detection and reduces false declines, improving both security and conversion rates. —— Source: Checkout.com x Connecting the dots in payments... ► Sign up to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬 : https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... and Marcel van Oost

When branded search costs start climbing, it often traces back to the same thing. Lookalikes are showing up next to the real listing, and if those sellers are also bidding on brand terms, the auction on your own name gets more crowded and the cost per click ticks up. The reflex is to bid harder. That can hold the placement without fixing why the shopper hesitated in the first place. Amazon Ads has a few tools for exactly this: Get your Brand Registry enrollment sorted. It verifies your brand with Amazon and unlocks the brand-focused ad options plus the protection tools. Most of what follows relies on it. Set up Sponsored Brands. Your logo and a custom headline show up in search results, and you choose where the click lands. Send to the Brand Store when you want to show range, product pages when one item is carrying most of the volume. That logo and headline can help shoppers recognize your brand and identify your official products. Sponsored Display is for the recovery play. It can bring back shoppers who viewed your product and left to compare alternatives, so they're less likely to drift over to a knockoff while they're still deciding. Reinforce with social proof in the creative. Star rating, review count, verified purchase cues, packaging that matches across every listing and every ad. When someone is comparing your listing to a knockoff on price, reviews are usually the tiebreaker. Make the official brand easier to recognize before you pay more to defend it. Four setup steps are in the image if it's worth a screenshot. If you've dealt with a lookalike on your brand terms, what worked for you? Learn more about Amazon Ads and get the full how-to guide: https://lnkd.in/g2MKdg_Y #Ad #AmazonAds #Sponsored