Enhancing Credit Card Fraud Detection Using Generative AI-Based Data Augmentation

Credit card fraud detection is challenging due to the extreme class imbalance and the limited number of fraudulent transactions. To address this problem, this paper proposes a fraud detection framework that combines generative AI-based data augmentation with machine learning classification models. We generated synthetic fraud samples at augmentation ratios of 1%, 5%, and 10% using three generative approaches: a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), a Variational Autoencoder (VAE), and a Large Language Model (LLM)-based data generator. The generated samples were evaluated using the Discriminator Test, Distance-to-Closest-Record (DCR) analysis, and t-SNE visualization to compare them to real fraud data and identify differences in their distributions. The augmented datasets were then used to train Random Forest, XGBoost, and LightGBM classifiers. The baseline models trained on the original dataset achieved F1-scores of 0.857 for Random Forest, 0.856 for XGBoost, and 0.877 for LightGBM. After adding synthetic fraud samples to the training data, the results showed improvements in fraud detection performance in some cases. LightGBM achieved the highest F1-score of 0.897 when trained with VAE-generated data at the 10% augmentation ratio. Similarly, XGBoost achieved an F1-score of 0.880 using VAE-generated data at the 5% ratio. Overall, the findings demonstrate the potential of generative AI-based augmentation to improve fraud detection performance in highly imbalanced financial datasets, particularly when combined with LightGBM and XGBoost classifiers.