Using past experience to optimize audit sampling design

Optimal sampling designs for audit, minimizing the mean squared error of the estimated amount of the misstatement, are proposed. They are derived from a general statistical model that describes the error process with the help of available auxiliary information. We show that, if the model is adequate, these optimal designs based on balanced sampling with unequal probabilities are more efficient than monetary unit sampling. We discuss how to implement the optimal designs in practice. Monte Carlo simulations based on audit data from the Swiss hospital billing system confirms the benefits of the proposed method.