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Limitations and Applicability of GANs in Banking Domain

Threats due to payment-related frauds are always a primary concern for financial institutions (FIs), often leading to huge losses and impacting consumer experience. To combat emerging frauds and improve the system’s robustness, FIs need an efficient system to detect fraud while authorizing payments. The biggest challenge in developing a fraud detection system is a high degree of class imbalance between fraudulent and legitimate transactions. Recently, Generative Adversarial Networks (GANs) are employed as an oversampling technique to augment the dataset with synthetic minority samples. In this paper, we present a systematic study to train GANs for synthetic fraud generation, demonstrating improved classifier performance detecting fraud. Training of GANs is conducted in various settings, including min-max objective and with or without auxiliary loss discriminating synthetic fraud and real fraud from non-fraud samples. Auxiliary loss is obtained using contrastive loss or triplet loss. Quality of trained GANs is estimated by evaluating the lift in classifier performance when trained with dataset augmented with synthetic fraud. Further, the effect of Discriminator Rejection Sampling (DRS) is studied in synthetic sample selection used for training data augmentation. The performance comparison of different settings proposed in this study is evaluated using a publicly available Credit-Card dataset and showed an absolute improvement of up to 6% in Recall and 3% in precision. We hope this paper will help advance the applicability of GANs with a practical insight into the research that has been done on this topic so far and open doors to interesting future research direction.