Modeling Hidden Exposures in Claim Severity via the EM Algorithm

We consider the issue of modeling the so-called hidden severity exposure occurring through either incomplete data or an unobserved underlying risk factor. We use the celebrated EM algorithm as a convenient tool in detecting latent (unobserved) risks in finite mixture models of claim severity and in problems where data imputation is needed. We provide examples of applicability of the methodology based on real-life auto injury claim data and compare, when possible, the accuracy of our methods with that of standard techniques.