Time-Aware Density-Based Generative Network for Imbalanced Data in Credit Card Fraud Detection

Credit card fraud detection (CCFD) is essential for the integrity of modern financial systems and is significantly challenged by class imbalances, where the volume of legitimate transactions substantially exceeds that of fraudulent ones. This imbalance severely limits the ability of CCFD models in accurately learning and detecting fraudulent patterns. Although existing techniques adeptly mitigate class imbalances, their application in CCFD is compromised by the adept concealment tactics of fraudsters, which enhance the behavioral overlap between legitimate and fraudulent transactions. This study introduces a novel time-aware density-based generative network model method (TDGN) incorporating a density-based wasserstein generative adversarial network (DWGAN) with a time-aware gated network (TaGN). The density-based wasserstein generative adversarial network minimizes the detrimental effects of behavioral overlap and producing high-quality synthetic samples. Subsequently, TaGN is employed to transform transactional features into new representations that more effectively delineate between fraudulent and legitimate transactions. Experimental results from a large-scale real-world credit card transaction dataset demonstrate the superior performance of our proposed method compared to its competitors, offering a robust solution to the persistent challenge of class imbalance in CCFD.