Performance Evaluation and Optimization of Cross-Border E-Commerce Fraud Detection Algorithms Based on Multi-Dimensional Feature Fusion

Abstract

Cross-border e-commerce transactions present unique challenges for fraud detection due to their complexity involving multiple currencies, jurisdictions, and payment systems. This research conducts comprehensive performance evaluation of various machine learning algorithms applied to fraud detection in cross-border e-commerce scenarios. The study analyzes algorithm effectiveness across different feature dimensions including transaction patterns, user behaviors, geographical indicators, and temporal characteristics. Through systematic experimentation on real-world transaction datasets, this work identifies key performance metrics and optimal feature combinations that enhance detection accuracy while minimizing false positive rates. The comparative analysis reveals significant performance variations among different algorithmic approaches, with ensemble methods demonstrating superior balance between precision and recall. Additionally, the research investigates computational efficiency considerations essential for real-time fraud prevention systems. The findings provide practical guidance for selecting and optimizing fraud detection algorithms in cross-border e-commerce environments, contributing to improved transaction security and reduced financial losses.