The financial industry relies heavily on data-driven decision-making, yet concerns surrounding data privacy, security, and availability pose significant challenges. AI-powered synthetic data generation has emerged as a transformative solution, enabling financial institutions to create realistic, high-quality datasets without exposing sensitive information. This paper explores the role of AI agents in generating synthetic financial data, discussing key methodologies such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Differential Privacy techniques. It examines their impact on risk modeling, fraud detection, algorithmic trading, and regulatory compliance. Additionally, the paper highlights challenges such as data fidelity, bias mitigation, and ethical considerations. By leveraging AI-driven synthetic data, financial institutions can enhance predictive analytics, streamline model development, and ensure robust data privacy while maintaining compliance with stringent regulations.