Harnessing Machine Learning to Combat E-commerce Fraud: Real-time Detection, Advanced Feature Engineering, and Scalable Systems

Abstract

With global e-commerce transactions projected to exceed $8.9 trillion by 2026, fraud rates have surged by 21.3% year-over-year, presenting a critical challenge for digital commerce ecosystems. This session explores the transformative role of machine learning (ML) in detecting and mitigating e-commerce fraud, offering a data-driven roadmap for future resilience. Traditional rule-based systems now average a 71.4% detection rate with a 28.7% false positive rate, leading to substantial revenue loss and customer friction. In contrast, ML-driven systems process 947,000 transactions per second and analyze 2,347 features per transaction, achieving pattern detection accuracy of 96.7% while reducing false positives by 81.4%. We delve into cutting-edge supervised models like Random Forests and Gradient Boosting Machines, which deliver up to 96.2% precision while maintaining sub-70 millisecond inference times across 127,000 concurrent sessions. Unsupervised approaches like Isolation Forests and Autoencoders complement these models by detecting 26.8% previously unknown fraud patterns with real-time adaptability. Advanced feature engineering across temporal (achieving 94.8% timing anomaly detection), network (with 95.2% accuracy in fraud ring detection), and behavioral domains (distinguishing bots with 93.8% accuracy) has further revolutionized fraud prevention. Scalability remains paramount—optimized distributed systems now handle 2.7 million transactions per minute with 99.992% uptime, achieving consistent sub-150 millisecond decision times. Moreover, hybrid systems integrating ML with rule-based logic have elevated detection accuracies to 97.4%, while smart manual review systems have improved reviewer efficiency by 57.2%.