Abstract: There is risk or uncertainty that a predictive model, such as a machine learning model, that has been fitted to a dataset of known cases will not produce the same distribution of predictions or outcomes when applied to future cases. To assess risk, the dataset of known cases is statistically assessed using best-fit probability distributions and correlations for one or more features of the dataset, then new cases are generated to produce a synthetic dataset that has statistical characteristics similar to the known dataset. The predictive model can be applied to the synthetic dataset. A comparison of the distribution of predictions by the predictive model on the known dataset and on the synthetic dataset can be made. Significant variations in the distribution of predictions or outcomes can indicate that the model is not suitable for future cases. Lack of such variations can increase confidence and willingness to use the model.