AI-Powered Dynamic Risk Scoring for E-commerce Transactions

Abstract

This article presents a comprehensive framework for dynamic risk assessment in e-commerce fraud detection using advanced machine learning techniques. The article introduces an innovative approach combining reinforcement learning, Bayesian networks, and real-time processing architectures to address the challenges of modern fraud detection. The implementation demonstrates significant improvements in fraud detection accuracy, reduction in false positives, and enhanced customer experience through adaptive risk scoring mechanisms. The system incorporates sophisticated feature engineering, network-aware processing, and continuous model optimization to maintain performance across transaction patterns and network conditions. The article provides detailed analysis of system architecture, performance metrics, and implementation challenges, offering solutions for data imbalance, real-time processing, and model drift management in large-scale e-commerce environments.