  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.
