Synthetic Data Meets Finance: Generative Models for Privacy Preserving Analytics

Abstract

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, time series price data, and sequential event streams. The integration of differential privacy (DP) mechanisms, federated learning (FL) compatibility, and downstream evaluation methodologies is examined in depth. Applications spanning fraud detection, credit risk modeling, anti-money laundering compliance, algorithmic trading simulation, and regulatory stress testing are reviewed against a backdrop of evolving privacy-preserving standards. Critical gaps in temporal fidelity, fairness-aware synthesis, and model interpretability are identified, and high-priority future research directions are charted. This synthesis demonstrates that no single generative paradigm dominates across all financial use cases, and that robust evaluation frameworks combining statistical fidelity with task-specific utility remain an open research priority of considerable practical urgency.