Using Variational Auto Encoding in Credit Card Fraud Detection

Abstract

Machine learning approaches are widely used to analyze and detect the increasingly serious problem of credit card fraud. However, typical credit card datasets present imbalanced classification situations because of severely skewed class distributions. Although researchers have proposed some strategies to deal with these imbalances, disadvantages remain. We propose an oversampling method based on variational automatic coding (VAE), combined with classic deep learning techniques, to solve this problem. The VAE method is used to generate a large amount of diverse cases from minority groups in an imbalanced dataset, which are then used to train the classification network. The proposed method is tested on an open credit card fraud dataset, which contains transactions conducted by European cardholders over two days in September 2013. Experimental results show that the VAE method performs better than synthetic minority oversampling techniques and traditional deep neural network methods. In addition, it outperforms recent oversampling methods based on generative adversarial network (GAN) models. After submitting the extended dataset to the baseline for training, the test of the VAE model performs well on indicators including precision, F-measure, accuracy and specificity. These experimental results suggest that the VAE-based oversampling method can be effectively applied to imbalanced classification problems.