2nd Workshop on Synthetic Data for AI in Finance

Synthetic data generation has emerged as a popular research area in both academic and industry research labs. The financial industry in particular has demonstrated strong interest due to the highly regulated nature of the business and sensitivity of individual financial information. The hope of synthetic data is enabling internal and external collaborations through the sharing of realistic, but privacy-preserving synthetic data, currently impossible due to legal requirements and internal policies. These collaborations open up possibilities of improved customer experiences and protections (e.g. against fraud) at financial institutions. Many questions surrounding synthetic data, however, remain: (i) privacy guarantees and their robustness to attacks, e.g. membership inference, (ii) fairness implications when utilizing synthetic data, (iii) how to assess quality, utility and diversity of synthetic data. Each must be interpreted in light of specific technical, legal and practical challenges when working with sensitive financial and healthcare information about individuals. The goal of this workshop is to bring together researchers from academia and practitioners and regulators from these industries to understand the evolving landscape and serve as a venue for cross-pollination between academic research and practical experience dealing with challenges of using synthetic data in industry. Our main goals are to develop understandings of the most important open problems, current methods and their limitations, and establish a series of cross-disciplinary good practices.