Leveraging Generative Adversarial Networks for Synthetic Financial Data Generation

This study focuses on adopting GANs to synthesize financial data a solution intended to address the challenges of data privacy and availability and limited access to actual financial data. To address such temporal dynamics involved in the datasets, and the advanced statistical properties, recent enhanced GAN structures including TimeGAN, Conditional GAN, and WGAN are used. This work also shows that the studied models excel compared to a conventional GANs while creating more realistic synthetic data for financial datasets. This data which is generated is checked to ensure that it conformed with the statistical measures such as mean, variance, auto correlation and volatility. Moreover, the applicability of synthetic data is tested based on the performance of using them in different financial roles, including algorithmic trading, risk analysis, fraud detection, and credit scoring with the small decline in comparison to real data. This research therefore posits that GAN-generated data can be used as a reliable and efficient replacement for real data in financial modeling and machine learning applications; thus offering a novel solution to ever increasing demand for secure high quality financial data.