Study of Generative Adversarial Networks for Simulating Synthetic Credit Data in Data-Scarce Banking Environments

In data-scarce banking environments, especially within developing financial systems and niche lending institutions, the paucity of historical credit data limits the effectiveness of traditional risk modeling and fraud detection. This study investigates the use of Generative Adversarial Networks (GANs) to synthesize high-fidelity, privacy-compliant credit data. We evaluate several GAN variants—including vanilla GANs, Conditional GANs (CGANs), and Wasserstein GANs (WGANs)—for their ability to replicate the statistical and behavioral properties of real-world credit datasets. Our methodology involves rigorous comparative analysis based on statistical similarity metrics, downstream model utility, and privacy preservation standards. We demonstrate that WGAN-GP models offer superior stability and realism in synthetic credit generation while minimizing overfitting and data leakage risks. Our findings suggest that GAN-generated data can effectively supplement real data for modeling creditworthiness, especially in low-resource banking sectors.