Synthetic data can simulate a wide range of hypothetical or extreme scenarios that may not be present in historical datasets. This makes it invaluable for stress-testing financial risk models, validating their robustness, and assessing their behavior under rare or adverse conditions (e.g., market crashes, interest rate shocks). By exposing models to diverse and challenging situations, synthetic data helps ensure they perform reliably in real-world applications.

A is incorrect: While synthetic data can help in certain cybersecurity applications, such as detecting anomalies, this is not its primary purpose in financial risk management.

C is incorrect: Generating synthetic data can be computationally intensive, especially when high-quality or complex datasets are needed, so it does not directly reduce computational resource demands.

D is incorrect: Synthetic data does not inherently make AI models more explainable. Explainability depends more on model architecture and interpretability techniques.