One-Class Adversarial Fraud Detection Nets With Class Specific Representations

Haipeng Peng, Jie Zhao, Lixiang Li, Yeqing Ren, Shanshan Zhao

Abstract: Most fraud detection algorithms identify the anomaly by learning from a small number of existing fraud samples, therefore they are often ineffective when deal with complex and unknown situations. This article proposes a new one-class classification model called one-class adversarial fraud detection nets with class specific representations (CS-OCAN), which consists of modified autoencoders and Complementary generative adversarial networks (GAN). Firstly, the two-iteration framework is designed to make reasonable use of the reference data generated by Complementary GAN. Secondly, an additional loss function is added in the latent space of the autoencoder, which transforms the semi-supervised problem into a supervised problem that aims to maximize inter-class distances between two classes and minimize intra-class variances. We have conducted experiments on UMD Wikipedia dataset and Credit card fraud detection dataset. Experimental results show that our method CS-OCAN has significantly improved the detection accuracy and stability compared with the state-of-the-art one-class classification models.