Mode Collapse Detection Strategies in Generative Adversarial Networks for Credit Card Fraud Detection

Abstract

A Generative Adversarial Network (GAN) is an artificial intelligence model developed specifically to produce synthetic data that resembles real data by training a generative model and a discriminative model simultaneously using adversarial training. A GAN can be extensively used for generating replicated data, however, it suffers from several issues, one of which is mode collapse. Mode collapse takes place when the generator is unable to capture the complete range of diversity in the target data distribution, resulting in the production of limited and repeating variations of samples. Multiple metrics exist to quantify mode collapse in GANs, although no individual metric is capable of consistently providing accurate results. This research focuses on the critical need for accurate mode collapse detection techniques in GANs, to strengthen the credit card fraud detection systems. In this work, we utilize a GAN to generate numerical data instead of image data. Our approach utilizes a wide range of measures, such as Generator and Discriminator Loss, Wasserstein Distance, precision, recall, and visualization tools, to provide a comprehensive framework for detecting mode collapse. In addition, we introduce an alert mechanism that identifies possible mode collapse at an early stage, allowing for earlier intervention and modifications to the training process. We have further proposed suggestions regarding monitoring and analyzing generator and discriminator loss values to identify potential instances of mode collapse to help the developer optimize GAN training and improve the quality of synthetic data.