https://iaeme.com/Home/journal/IJFDS 1 editor@iaeme.com International Journal of Financial Data Science (IJFDS) Volume 2, Issue 1, January-June 2024, pp. 1-10, Article ID: IJFDS_02_01_001 Available online at https://iaeme.com/Home/issue/IJFDS?Volume=2&Issue=1 Journal ID: 1233-1259 © IAEME Publication A REVIEW OF DEEP GENERATIVE MODELS FOR SYNTHETIC FINANCIAL DATA GENERATION Dr. N.Kannan Professor, School of Management Studies, Sathyabama Institute of Science and Technology, Rajiv Gandhi Road, Chennai-600119 ABSTRACT In today's financial landscape, the availability of high-quality data is essential for decision-making, risk management, and innovation. However, accessing real-world financial data can be challenging due to privacy concerns, data access restrictions, and cost barriers. Synthetic financial data generation has emerged as a promising solution to address these challenges, with deep generative models offering a powerful framework for creating realistic and privacy-preserving synthetic data. This paper provides an overview of deep generative models for synthetic financial data generation, comparing them to traditional methods and examining their applications, challenges, and future directions in finance. We conducted a comprehensive review of the literature on deep generative models in finance, exploring their underlying principles, applications, and empirical results. Our review highlights the advantages of deep generative models in capturing complex data distributions, temporal dynamics, and inter-variable dependencies present in financial datasets. We identified challenges such as model interpretability, scalability, and robustness, while also recognizing opportunities for future research and development in this rapidly evolving field. Ultimately, deep generative models hold tremendous potential for reshaping synthetic financial data generation, offering stakeholders more accurate, reliable, and privacy-preserving data for decision-making, risk management, and innovation in financial markets. However, addressing challenges such as model interpretability, scalability, and ethical considerations will be crucial for realizing the full benefits of these models in finance. Keywords: Synthetic Data, Financial Data, Deep Generative Models, Finance, Machine Learning, Deep Learning, Artificial Intelligence, Data Generation, Privacy-Preserving, Decision-Making, Risk Management