Semi-supervised GANs for Fraud Detection

Over the years the online gambling industry has evolved into one of the most profitable industries on the Internet. At the same time, new stringent regulations have required the online industry to become a lot more vigilant. Although standards have improved, the methods used to process finance from illicit activities also evolved and became more sophisticated. Detecting these fraudulent activities in real life with high accuracy requires a learning system to be trained with balanced data sets of fraudulent and normal transactions. However, in the real-world, the number of fraudulent cases is significantly lower than normal cases. In this paper, to deal with data imbalance, we propose a novel generative adversarial framework based on semi-supervised learning of sparse auto-encoders for the detection of fraud in online gambling. Experimental results show that the proposed framework outperforms mainstream discriminative techniques such as logistic regression, random forest and multi-layer perceptron. We validate further the approach by applying it to other domains that suffer from the problem of class imbalance obtaining promising results.