Strategies For Reducing Ecommerce Fraud

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

"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.

In my conversations with policymakers, I often hear concerns of how AI is making scams worse. Truth is, we don’t talk enough about how AI is used in combating scams. Often, the scale of the threat is hard to grasp as it's not about individual bad actors, but organized, sophisticated abuse - and that’s where AI can make a difference in taking the fight to scammers. 🔍 Search: Our AI-powered scam detection systems helped catch 20-times the number of scammy pages. For example, new protections decreased scams impersonating official sites by more than 70%. 🖼️ Ads: Thanks to 50+ LLM enhancements, AI significantly improved fraud detection at account setup. AI was key in combating a new challenge: AI-generated impersonation scams, contributing to a 90% drop in reports. 📍Maps: Our machine learning models are trained to find patterns that indicate fraudulent behaviors like a sudden surge in ratings. In 2024, we caught 12 million attempts from fraudsters trying to create entirely fake listings. I’m excited to see AI taking center stage in our fight against fraud and look forward to shifting the conversation in this space, and keeping our users safe online. 🛡️

Just had a call with a customer whose risk analysts are firmly stuck in the pre-AI world. Here's what they're doing manually that will be automated: (Disclaimer: I don't blame this team at all, and there are so many like them making the jump to AI workflows. We're here to help!) Their current (manual) merchant verification process: 1. Manually searching business names across multiple sources 🔍 2. Cross-checking Secretary of State registrations 📑 3. Comparing website domain creation dates with "in business since" claims 📅 4. Reviewing Google/Yelp business status and ratings ⭐ 5. Scanning for adverse media mentions 📰 6. Checking physical location via Google Maps 🏢 7. Verifying social media presence (Instagram/YouTube) 📱 8. Looking for suspicious website elements (stock images, template text) 🚩 9. Verifying the payout bank account with voided checks 🏦 10. Calculating potential credit exposure for risk assessment 💰 Every analyst does this, and I don't blame them. The problem is, it's time-consuming, inconsistent across analysts and teams, and doesn't scale 👎 What excites me is how AI agents 🧠 can transform this workflow: - Automated data collection: Connect to multiple sources simultaneously to gather all relevant data in seconds ⚡ - Pattern recognition: Flag discrepancies that matter (like a business claiming 20 years of history with a 6-month-old domain) 🧩 - Contextual intelligence: Understand industry norms (like towing companies typically having lower ratings) 🔄 - Risk summarization: Provide the "net net" with key findings and specific risk factors, not raw data dumps 📊 - Guided recommendations: "Pause payouts," "Request additional documentation," or "Approve with monitoring" based on risk patterns and the company's risk appetite 📋 - Continuous learning: Improve detection by incorporating feedback from confirmed fraud cases 📈 Transitions like this are difficult once. The outcome is a senior analyst team member for everyone on your risk team that never gets tired, and always delivers insights. Leaving the manual processes behind forever. Trust me, it's worth it 🚀