Financial regulators depend on increasingly granular data to identify market vulnerabilities and inform policy decisions. However, persistent data gaps in critical areas of the financial system, such as non-bank financial intermediation (NBFI), hinder comprehensive risk assessments. Synthetic data, generated using artificial intelligence (AI), offers a promising solution to address these gaps and enhance regulatory development and oversight. Yet this approach raises ethical and practical concerns, including questions of regulatory legitimacy, transparency, privacy, and sustainability. This study examines whether the advantages of synthetic data in financial regulation outweigh its challenges, providing an initial assessment of its feasibility and broader implications.