Generative adversarial networks (GANs) show promise for addressing data imbalance issues in insurance modeling. GANs were originally developed for computer vision tasks but have also been applied to tabular data. Conditional GANs and CycleGANs can generate synthetic minority class examples to balance datasets. In a case study on insurance fraud detection, GANs outperformed traditional resampling techniques like SMOTE in improving precision, recall, and F1-score. However, GANs require dense feature representations and consistency over time to be effective for tabular data imbalance problems.