Introduction

Synthetic data—artificially generated data preserving statistical properties of real data—enables backtesting and model development without sharing sensitive data. However, regulators scrutinize synthetic data usage: Is it truly representative? Does it hide real limitations? Maintaining audit trails documents synthetic data provenance, generation process, and validation.

Synthetic Data Provenance

Document: real data sources, generation method (GAN, VAE, etc.), training procedures, validation against real data, limitations. For backtests using synthetic data, document: synthetic data version used, validation period, discrepancies from real market. Enables regulators and auditors to assess appropriateness of synthetic data for decision-making.

Compliance and Auditability

Maintain immutable audit trails of synthetic data usage. When regulator inquires about specific backtest result, can trace to exact synthetic data version and demonstrate validation. Transparency about synthetic data limitations and validation builds regulatory trust.

Conclusion

Audit trails for synthetic data ensure transparency and enable regulatory assessment of appropriateness for use in financial decisions.