This book offers a comprehensive guide to leveraging machine learning and predictive analytics for managing the full spectrum of risks inherent in modern e-commerce platforms from payment fraud and chargebacks to platform-specific abuses like voucher manipulation, collusion rings, and promotional gaming. Grounded in real-world practice, it shows how to transform raw transaction logs, user clickstreams, and promotional datasets into actionable risk scores that power both real-time interdiction and strategic oversight. Readers discover a suite of methods ranging from gradient-boosted trees and deep sequence models to graph neural networks and unsupervised anomaly detectors each chosen for its ability to detect subtle, evolving patterns of misuse.