This research examines the utilization of Generative Adversarial Networks (GANs) to produce synthetic financial data that ensures privacy while adhering to stringent regulatory frameworks, such as the General Data Protection Regulation (GDPR) (European Union, 2016) [4] and the California Consumer Privacy Act (CCPA). Financial institutions handle extensive sensitive data, necessitating stringent privacy safeguards. Conventional anonymization techniques frequently reduce data utility, thereby limiting their effectiveness for machine learning, research, and analysis. Conversely, GANs offer an innovative alternative by generating realistic synthetic datasets that maintain the statistical properties of original data without containing personally identifiable information. This paper introduces a GAN-based framework designed specifically for financial data, enhanced with differential privacy (Dwork et al., 2006) [3] to provide robust privacy assurances. Through evaluation on real-world financial datasets, the framework demonstrates its capability to generate high-quality synthetic data applicable in areas such as fraud detection and customer segmentation. Findings suggest that this approach effectively maintains a balance between privacy and utility, presenting a scalable solution for financial institutions to leverage data while ensuring compliance with legal mandates. This study advances privacy-preserving data generation and delivers actionable insights for the financial sector.