Strategies For Reducing Ecommerce Fraud

𝟮𝟬 𝗧𝗼𝗽 𝗔𝗣𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗧𝗶𝗽𝘀 1. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗿𝗼𝗻𝗴 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Make sure only authorized users can access your APIs. Use strong authentication methods, such as OAuth or OpenID Connect, and grant users the least privilege necessary to perform their tasks. 2. 𝗨𝘀𝗲 𝗛𝗧𝗧𝗣𝗦 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻: Encrypt all traffic between your APIs and clients to protect sensitive data from being intercepted by attackers. 3. 𝗟𝗶𝗺𝗶𝘁 𝗗𝗮𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴: APIs should only expose the data that clients need to function. Avoid exposing sensitive data, such as personally identifiable information (PII). 4. 𝗦𝘁𝗼𝗿𝗲 𝗣𝗮𝘀𝘀𝘄𝗼𝗿𝗱𝘀 𝗦𝗲𝗰𝘂𝗿𝗲𝗹𝘆: Hash passwords before storing them in a database. This will help to prevent attackers from stealing passwords if they breach your database. 5. 𝗨𝘀𝗲 𝘁𝗵𝗲 '𝗟𝗲𝗮𝘀𝘁 𝗣𝗿𝗶𝘃𝗶𝗹𝗲𝗴𝗲' 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: Give users and applications only the permissions they need to perform their tasks. This will help to minimize the damage if an attacker gains access to an API. 6. 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗨𝗽𝗱𝗮𝘁𝗲𝘀: Keep your API software up to date with the latest security patches. 7. 𝗗𝗶𝘀𝗮𝗯𝗹𝗲 𝗗𝗲𝗳𝗮𝘂𝗹𝘁 𝗘𝗿𝗿𝗼𝗿𝘀: Default error messages can sometimes reveal sensitive information about your API. Configure your API to return generic error messages instead. 8. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Use secure methods for managing user sessions, such as using secure cookies with the HttpOnly flag set. 9. 𝗖𝗦𝗥𝗙 𝗧𝗼𝗸𝗲𝗻𝘀: Use CSRF tokens to prevent cross-site request forgery attacks. 10. 𝗦𝗮𝗳𝗲 𝗔𝗣𝗜 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Your API documentation should not contain any sensitive information. 11. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: Regularly conduct security testing of your APIs to identify and fix vulnerabilities. 12. 𝗧𝗼𝗸𝗲𝗻 𝗘𝘅𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻: Implement token expiration to prevent attackers from using stolen tokens for extended periods. 13. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Validate all user input to prevent injection attacks. 14. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗛𝗲𝗮𝗱𝗲𝗿𝘀: Use security headers to protect your API from common attacks, such as XSS and clickjacking. 15. 𝗖𝗢𝗥𝗦 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Configure Cross-Origin Resource Sharing (CORS) to restrict access to your API from unauthorized origins. 16. 𝗧𝗵𝗿𝗼𝘁𝘁𝗹𝗲 𝗟𝗼𝗴𝗶𝗻 𝗔𝘁𝘁𝗲𝗺𝗽𝘁𝘀: Throttle login attempts to prevent brute-force attacks. 17. 𝗔𝗣𝗜 𝗩𝗲𝗿𝘀𝗶𝗼𝗻𝗶𝗻𝗴: Use API versioning to allow you to make changes to your API without breaking existing clients. 18. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻: Encrypt data at rest and in transit to protect it from unauthorized access. 19. 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝘂𝗱𝗶𝘁𝗶𝗻𝗴: Log all API access and activity to help you detect and investigate security incidents. 20. 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴: Implement rate limiting to prevent API abuse and overload.

"AI will replace fraud analysts" is the wrong conversation. Every fraud leader I talk to knows this. But they're still asking: "What can I actually do with AI today that won't freak out my team?" And the pressure is real. Here's what I'm hearing: • Boards want "AI strategy" yesterday • Teams fear being replaced • Leaders stuck in the middle • Everyone pretending they have it figured out Let's be honest... Nobody has this figured out yet. But the smartest fraud leaders I'm talking to share one approach: Small. Specific. Human-in-the-loop. That's it. That's the entire strategy that's actually working. Opportunity 1: Start with investigation summaries Don't automate decisions. Automate documentation. • Feed transaction details into your tool • Generate investigation summaries • Save 2 hours per analyst per day One team reduced case notes from 20 minutes to 2 minutes. That's 18 minutes back to catch actual fraud. Opportunity 2: Pattern detection assistant Not replacing analysis. Augmenting it. • Upload daily fraud cases • Ask: "What patterns do you see?" • Use AI to spot trends humans might miss One team found 3 new fraud patterns their rules missed. Opportunity 3: Rule writing helper The most underrated AI use case. • Describe the fraud pattern in plain English • AI drafts the rule logic • Human reviews, tests, deploys What took 3 hours now takes 30 minutes. Stop thinking: AI vs. Humans Start thinking: AI + Humans vs. Fraudsters Your people know fraud. AI knows patterns. Together, they're stronger.

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.

🚨 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

Passwords can be stolen. Devices can be spoofed. But your digital body language? That’s much harder to fake. 🧠 As fraud gets more sophisticated, behavioral biometrics is finally having its moment. We’ve relied on static credentials for years: passwords, 2FA, even facial recognition. But attackers have caught up. They’re using AI to mimic voices, hijack sessions, and bypass traditional defenses. 🤖 The real shift isn’t adding more checks. It’s moving from one-time verification to continuous context. Behavioral biometrics analyzes how you type, swipe, scroll, and navigate, how long they spend on each page, are they on a call, are they being accessed remotely, and would work here. They are building a unique, persistent profile that’s nearly impossible to replicate. It doesn’t just ask, “Are you who you say you are?” It asks, “Are you behaving like you?” This kind of signal is becoming critical: • It detects bots and synthetic identities at onboarding • Flags account takeovers as they happen • And reduces friction for the legitimate users you actually want to keep It’s especially valuable as phishing, vishing, and social engineering attacks grow more targeted, especially in financial services, where the real challenge is protecting existing wallets, not just detecting bad onboarding attempts. Passive. Adaptive. Always on. Exactly what modern fraud prevention needs. ✅ The future of authentication isn’t about adding more steps. It’s about making security invisible and intelligent. Agree?

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.

🚨 ALERT: $1.6 Mn Stolen Through Payment Gateway Manipulation - Is Your Fintech Vulnerable? 🚨 A recent case involving Navi Technologies highlights the critical need for robust fraud prevention measures. The company was defrauded of ₹14.26 crore due to a loophole in a third-party payment gateway, which allowed users to edit transaction amounts post-payment. This incident underscores the importance of both technical and operational safeguards. How can such frauds be prevented? 🔍 Technical Measures: 👉 Implement anomaly detection systems to flag unusual transaction patterns in real-time. 👉 Enforce immutable transaction records to prevent unauthorized edits. 👉 Regularly audit and test payment gateways for vulnerabilities. 🛡️ Operational Measures: 👉 Establish multi-layered approval processes for high-risk transactions. 👉 Conduct employee and customer awareness programs to identify and report suspicious activities. 👉 Collaborate with law enforcement and cybersecurity experts to stay ahead of emerging threats. Fraud prevention is not just a technical challenge but also an operational one. By combining advanced technology with the right processes, businesses can safeguard themselves against such incidents. Let's learn from this and build a more secure digital ecosystem! 💡 #FraudPrevention #CyberSecurity #AnomalyDetection #BusinessResilience https://lnkd.in/dW96ejyR

Many people think being overly stringent with upfront identity checks will reduce fraud. After a decade of building advanced fraud prevention strategies, I'm here to tell you that many people are wrong. I know it's counterintuitive, but let me explain: When institutions make their onboarding processes overly strict (i.e. requiring extensive documentation and multiple verifications), they gain a false sense of security. They assume these rigorous checks eliminate fraud risks and often let their guard down once users are onboarded. Fraudsters exploit this confidence by learning and bypassing the rules upfront, gaining access, and wreaking havoc from the inside. The better approach? ▪️ Don’t front-load all your checks. ▪️ Create the least amount of friction for each stage of risk. ▪️ Continuously monitor user behavior throughout their lifecycle. ▪️ Add step-ups as risk scores dictate. It forces you to stay vigilant, and it produces better outcomes.

👁 Soon enterprise solution architects will be able to design complex System-Knowledge-Human-AI system for any existing enterprise use cases. In the below fraud detection & prevention use case in financial services, we designed four AI Agents interacting with human, systems, and knowledge : 1️⃣ AI Agent #1: Pattern Recognition Agent Role: Accelerate fraudulent activity identification for analysts. Knowledge & Memory: Fraud patterns and user behavior knowledge. Integrated Systems: Interfaces with transaction monitoring systems. Specificities: Specializes in real-time pattern and anomaly detection. 2️⃣ AI Agent #2: Investigation Assistant Agent Role: Supports analysts in verifying flagged transactions. Knowledge & Memory: Transaction history and known fraud methods. Integrated Systems: Taps into core banking and digital platforms. Specificities: Extracts data, provides context, and assesses risk. 3️⃣ AI Agent #3: Resolution Suggestion Agent Role: Proposes solutions for confirmed fraud cases. Knowledge & Memory: Past resolutions and related outcomes. Integrated Systems: Connects to incident management and customer platforms. Specificities: Analyzes scenarios, assesses impacts, and recommends actions. 4️⃣ AI Agent #4: Fraud Prevention Education Agent Role: Educates on fraud prevention. Knowledge & Memory: Fraud tactics and effective prevention methods. Integrated Systems: Works with customer channels, internal learning systems, or knowledge management systems. Specificities: Curates personalized content, updates dynamically, and nudges behavior. #llm #generativeai #multiagents

Recently worked on an issue where an account was taken over, even though the account had MFA enabled. Ultimately MFA fatigue caused a user to automatically approve an MFA request when it wasn't valid. Multi-Factor Authentication (MFA) fatigue is a security risk that arises when users are overwhelmed by frequent authentication prompts, potentially leading to carelessness or susceptibility to social engineering attacks. Here are several strategies to prevent MFA fatigue: 1. Implement Adaptive Authentication: Risk-Based Authentication: Use contextual information to assess the risk level of an authentication attempt. For example, consider the user's location, device, and behavior. Only prompt for additional authentication factors when the risk is high. 2. Optimize MFA Frequency Session Duration: Extend the duration of authenticated sessions where appropriate (based on location, app, and other controls), reducing the need for repeated MFA prompts within a short period. Device Trust: Allow users to mark personal devices as trusted, requiring MFA only on new or untrusted devices. 3. Enhance User Experience Single Sign-On (SSO): Implement SSO solutions to reduce the number of logins and MFA prompts by allowing users to authenticate once and gain access to multiple applications. Biometric Authentication: Integrate biometric factors (e.g., fingerprint, facial recognition) to make the authentication process quicker and more user-friendly. 4. Educate Users Security Awareness Training: Regularly educate users about the importance of MFA and the risks associated with MFA fatigue. Teach them how to recognize and respond to social engineering attacks. Clear Communication: Provide clear instructions and support for users experiencing MFA fatigue, ensuring they understand the security measures in place. 5. Continuous Monitoring and Improvement Monitor Authentication Logs: Regularly review authentication logs to identify patterns of MFA fatigue and adjust policies accordingly. User Feedback: Gather feedback from users on their MFA experiences and use this information to improve the process. 6. Leverage Push Notifications and Modern MFA Methods Push Notifications: Use push notifications through a secure app instead of traditional SMS or email-based MFA, reducing friction and improving security. These are just some controls and each environment should be analyzed and appropriate controls be used based on each security context and risks.