AI in Risk7 min read

Synthetic Data for Risk Modelling: Uses & Limits

By Jonas Adam Mohamed Osman, known as Yonas Osman

CEO of Quantica Risk

60 Rue François Ier, 75008 Paris, France

Published 15 October 2026

How synthetic data supports risk model development and privacy compliance, and the validation steps needed before models trust it.

Executive answer

Synthetic data, artificially generated records that preserve the statistical properties of real data without exposing actual customer records, has become a practical tool for risk modelling where real data is scarce, sensitive, or imbalanced, such as rare fraud events or low-default credit portfolios. It also offers a route to privacy-compliant model development and testing, letting teams work with realistic data under GDPR or equivalent regimes without the exposure risk of handling actual personal data at every development stage.

The limitation firms must respect is that synthetic data is only as good as the generative process and the real data it was derived from; it can preserve existing biases, miss rare but important tail correlations, and create a false sense of security if validation teams treat it as a perfect substitute for real-world testing. Synthetic data works best as a complement to real data in development and stress testing, not as a wholesale replacement for final validation.

Generation methods and their trade-offs

Common generation approaches range from relatively simple statistical resampling and bootstrapping to generative adversarial networks and variational autoencoders capable of capturing complex, nonlinear relationships across many variables. The more sophisticated methods better preserve realistic correlations but require larger real datasets to train reliably and carry a higher risk of inadvertently memorising and leaking individual records if not properly constrained, an important consideration for privacy compliance claims.

Differential privacy techniques can be layered onto generation processes to give formal, quantifiable privacy guarantees rather than relying on the informal assumption that synthetic records cannot be traced back to individuals. Firms claiming privacy benefits from synthetic data should be able to articulate and, where possible, quantify the privacy guarantee rather than asserting it qualitatively, since supervisors and data protection authorities increasingly ask for specifics.

Validating synthetic data before relying on it

Fidelity testing compares statistical properties, marginal distributions, correlation structures, and rare-event frequencies, between synthetic and real data, flagging where the synthetic generation process has smoothed over or missed important tail behaviour that matters for risk modelling specifically. Utility testing goes further, training models on synthetic data and validating performance against real holdout data, since statistical similarity does not guarantee a model trained on synthetic data will generalise to real-world decisions.

Firms should maintain clear documentation of which models were developed, tested, or validated using synthetic data at each stage, since supervisors reviewing model documentation will ask about data provenance and may require evidence that final validation included real data, particularly for high-materiality models such as regulatory capital or pricing. Synthetic data governance should sit within the same data governance framework as real data, not as a separate, less scrutinised category.

Conclusion

Synthetic data is a genuinely useful addition to the risk modelling toolkit, particularly for rare-event modelling and privacy-constrained development environments, but it demands the same validation discipline as any other input into a regulated model. Firms that skip fidelity and utility testing on the assumption that synthetic data is automatically representative expose themselves to models that perform well in testing and poorly in production.

Jonas Adam Mohamed Osman (Yonas Osman), CEO of Quantica Risk, approaches synthetic data as a development accelerant that still requires real-world validation before any model built on it earns production trust.

Primary sources

synthetic datarisk modellingprivacyvalidation