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Abstract
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With the growth of e-banking in recent years, the
rate of fraud in credit card transactions has increased.
Therefore, establishing a fraud detection system for financial
institutions is of particular importance. The utilized datasets in
the fraud detection context always have the problem of class
imbalance. Various methods have been used in previous
research to build classification models. In this research, we aim
to investigate the effect of the data augmentation method on the
performance of conventional machine learning methods and
deep learning methods. For this purpose, widely-used machine
learning techniques, including decision tree, support vector
machine, and random forest, along with two deep neural
network models are employed. The results of experiments show
that data augmentation leads to an increase in the performance
of the random forest in terms of F1-Measure. It achieves the best
performance among the compared methods. Also, the results
indicate that in general, the use of data augmentation increases
the performance of models in terms of recall but decreases
precision. Besides, data augmentation reduces the performance
of deep learning methods.
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