Essential Tools for Fraud Prevention

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.

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

Fraud fighters: What are your go-to resources to stay ahead of emerging threats? (... here's the top 5 ) In Fraud Fighting, staying informed isn't just important—it's critical. Here are the resources some swear by to keep their defenses sharp: 1️⃣ Industry forums - Platforms like the ACFE Community and LinkedIn groups offer real-time insights from peers. They offer game-changing tips that aren't published anywhere else. 2️⃣ Threat intelligence reports - Consider subscribing to reports from cybersecurity firms and Fraud technology vendors. They provide deep dives into emerging fraud trends and tactics. 3️⃣ Regulatory updates - Following publications from FinCEN, FCA, and other relevant bodies can help you stay compliant and anticipate new fraud risks before they hit. 4️⃣ Academic research - University studies often uncover fraud patterns months before they become widespread. Follow journals like the Journal of Financial Crime for cutting-edge insights. 5️⃣ Dark web monitoring - Carefully monitoring dark web forums (with proper precautions) can provide early warnings of new fraud schemes being discussed. These resources can form the backbone of a proactive fraud prevention strategy. What are your favorite sources for staying ahead? Share in the comments—let's learn from each other! What about Telegram groups? or communities like The House of Fraud or About Fraud

$1M in fraud protection starts with mapping. That’s the goal. Now break it down: • Map every user touchpoint • Assign risk levels to each interaction • Identify high-risk points before fraud does Here’s how to deconstruct your risk surface area: 1. Map the User Journey: • Outline each touchpoint from signup to checkout. • Identify data points where fraud could slip in. 2. Label Risk Levels: • Assign risk levels to each interaction. • Use past data to gauge potential threats. 3. Build Fraud Detection Points: • Integrate checks and controls along the journey. • Automate alerts for suspicious behaviors. Example framework: 1. Map out every single user interaction. 2. Rate each point by risk potential, high to low. 3. Place tailored fraud checks where they matter most. What does this give you? A roadmap of where fraud might hit, long before it does. No more guesswork, just a clear system.