Synthetic data, that is, artificial data created to mimic real-world data, have recently gained popularity in various applications in the industry such as computer vision, healthcare and finance. In the latter, due to the highly regulated nature of the business, synthetic data is particularly useful. For instance, synthetic data can be more easily shared within and outside firms, as well as provide alternative data to problems where an extreme class imbalance is present, such as fraud detection and money laundering activity detection. However, many questions surrounding synthetic data have yet to be fully answered, including but not limited to privacy leakage in synthetic data generation, fairness implications when using synthetic data, or more fundamentally how one should assess quality and usefulness of synthetic data. We would like to bring together researchers from academia, practitioners from synthetic data providers as well as consumers of synthetic data in financial organizations to understand the evolving landscape around this important topic. This workshop would serve as a venue for cross-pollination between academic research and industry practical experience. Our main goals are to develop an understanding of the most important open problems, current methods and their limitations, and establish a series of cross-disciplinary good practices.Â