Adversarial Fraud Generation for Improved Detection

Generative Adversarial Networks (GANs) are known for their ability to learn data distribution and hence exist as a suitable alternative to handle class imbalance through oversampling. However, it still fails to capture the diversity of the minority class owing to their limited representation, for example, frauds in our study. Particularly the fraudulent patterns closer to the class boundary get missed by the model. This paper proposes using GANs to simulate fraud transaction patterns conditioned on genuine transactions, thereby enabling the model to learn a translation function between both spaces. Further to synthesize fraudulent samples from the class boundary, we trained GANs using losses inspired by data poisoning attack literature and discussed their efficacy in improving fraud detection classifier performance. The efficacy of our proposed framework is demonstrated through experimental results on the publicly available European Credit-Card Dataset and CIS Fraud Dataset.