Machine Learning-Driven Risk Scoring Systems for Improved Fraud Prevention in E-Commerce

Abstract

The rapid expansion of e-commerce has increased the operational complexity of fraud prevention by creating high-volume, high-velocity, and behaviorally diverse transaction environments. Conventional rule-based controls and static machine-learning classifiers often struggle when fraudulent behavior evolves faster than predefined detection logic or historical training patterns. This study investigates a machine learning-driven risk scoring framework designed to estimate transaction-level fraud propensity while incorporating behavioral, transactional, temporal, device, and account-level signals. The central research gap concerns the limited integration of calibrated risk scores with cost-sensitive fraud intervention, particularly in environments characterized by severe class imbalance and concept drift.
The proposed framework emphasizes probabilistic scoring rather than simple fraud-versus-legitimate classification, enabling transactions to be categorized into differentiated intervention bands. Its design combines ensemble learning, temporal feature construction, probability calibration, and threshold optimization to support adaptive fraud screening while reducing unnecessary rejection of legitimate customers.
The study further proposes an experimental protocol that evaluates both predictive discrimination and decision quality through calibration error, expected fraud loss, false-positive burden, and ranking-oriented measures.