Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments
This work addresses fraud detection for online payment systems, but it is incremental as it compares existing methods without introducing new techniques.
This study tackled fraud detection in online credit card payments by comparing anomaly detection methods with supervised learning, finding that LightGBM significantly outperformed others in metrics but was more affected by distribution shifts.
This study explores the application of anomaly detection (AD) methods in imbalanced learning tasks, focusing on fraud detection using real online credit card payment data. We assess the performance of several recent AD methods and compare their effectiveness against standard supervised learning methods. Offering evidence of distribution shift within our dataset, we analyze its impact on the tested models' performances. Our findings reveal that LightGBM exhibits significantly superior performance across all evaluated metrics but suffers more from distribution shifts than AD methods. Furthermore, our investigation reveals that LightGBM also captures the majority of frauds detected by AD methods. This observation challenges the potential benefits of ensemble methods to combine supervised, and AD approaches to enhance performance. In summary, this research provides practical insights into the utility of these techniques in real-world scenarios, showing LightGBM's superiority in fraud detection while highlighting challenges related to distribution shifts.