Generative adversarial network-based credit card fraud detection

Abstract

With the development of the financial industry, the number of credit cards has greatly increased. However, credit card fraud is still a major concern for financial security. Credit card fraud detection is a typical imbalanced classification problem, in which fraudulent cardholders are far less than non-fraudulent cardholders. The training on imbalanced samples will cause that the classifier ignores the minor fraudulent samples. To solve this problem, a generative adversarial network (GAN) based classification method is proposed for credit card fraud detection. GAN consists of a generative model and a discriminative model, and the two models are trained in a competitive way to get the Nash equilibrium. Specifically, the generative model tries to fit the real distribution of the non-fraudulent samples. The discriminative model determines the probability of a sample belongs to the distribution of non-fraudulent samples. To improve the discrimination performance, the fraudulent samples are also used in the training of discriminative model, which is different from the traditional GAN training scheme. The experimental results show that the recall reaches 82.7%.