Synthetic Data Meets Finance: Generative Models for Privacy Preserving Analytics

The financial industry faces increasing pressure from privacy regulations, including the General Data Protection Regulation (GDPR) and sector-specific compliance frameworks, which restrict access to sensitive transaction data critical for training machine learning (ML) models. Synthetic data generation, powered by advances in generative artificial intelligence (AI), has emerged as a technically promising solution that balances analytical utility with formal privacy guarantees. This review surveys the landscape of generative models—including generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models—applied to financial data synthesis encompassing tabular transaction records,…