Optimization of Fraud Detection in E-Commerce: A CGAN Data Augmentation Approach to Address Class Imbalance

Abstract

The rapid growth of e-commerce has increased the risk of fraud in online transactions, resulting in significant financial losses and decreased consumer trust. One of the main challenges in fraud detection is data imbalance, where the number of legitimate transactions far exceeds fraudulent transactions. This imbalance causes machine learning models to fail in accurately identifying fraudulent transactions. This study aims to evaluate the effectiveness of Conditional Generative Adversarial Network (CGAN) in improving fraud detection performance in e-commerce through data augmentation. Two machine learning algorithms, Random Forest (RF) and XGBoost, were used to classify transactions in both the original imbalanced dataset and the dataset augmented with CGAN. The study uses key evaluation metrics, including accuracy, precision, recall, and F1-score, to measure the model's performance. The results show that data augmentation using CGAN significantly improved the performance of both models. RF on the augmented dataset achieved an accuracy of 99.96%, precision of 99.93%, recall of 99.99%, and F1-score of 99.96%. Meanwhile, XGBoost achieved an accuracy of 99.93%, precision of 99.91%, recall of 99.94%, and F1-score of 99.92%. The main contribution of this study is to demonstrate that CGAN can effectively address the challenge of data imbalance and improve the reliability of fraud detection systems in e-commerce. This approach has the potential to be applied in various sectors facing similar issues, such as anomaly detection in finance and cybersecurity.