Tools For Monitoring Ecommerce Transactions For Fraud

🚨 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

Too many fraud solutions focus just on account opening. But risk evolves across the full user journey. Here's how we build the full picture at Sardine for dynamic scoring 👇 👉 When a user signs up, we create a baseline score based on identity, device, email, behavior signals 👉 As they transact, we update the score dynamically based on activity like login patterns, transaction details, behavior changes 👉 We build a holistic profile combining telco, email, device, merchant and more data into their risk score 👉 Machine learning models continuously monitor and flag anomalies to the baseline 👉 Granular data + models train on user's unique activity = precise risk scoring as they grow with your product Unlike legacy fraud tools, we don't just screen applicants. We provide ongoing monitoring across onboarding, transactions, account changes and more. This full picture reduces false positives and keeps fraud low across the user lifecycle.

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.