The increasing reliance on AI-driven financial models raises significant concerns about data privacy, security, and compliance. Traditional methods of data collection and centralized AI training expose sensitive financial information to cyber threats and regulatory risks. To address these challenges, synthetic data generation and federated learning (FL) have emerged as promising privacy-preserving techniques. Synthetic data replicates real-world financial patterns without exposing actual user information, while federated learning allows decentralized model training across multiple financial institutions without sharing raw data. This chapter explores how these two techniques work together to enhance privacy, improve model robustness, and ensure regulatory compliance in financial AI applications. It discusses the methodologies, challenges, and advantages of combining synthetic data with FL for tasks such as fraud detection, credit risk assessment, and algorithmic trading. Through case studies and quantitative analysis, we demonstrate how these innovations contribute to secure, scalable, and efficient financial AI models. Finally, the chapter outlines future research directions and potential advancements in privacy-preserving AI for the financial sector.