Preliminary Selection of Risk Factors in P&C Ratemaking

This paper proposes efficient statistical tools to detect which risk factors influence insurance losses before fitting a regres-sion model. The statistical procedures are nonparametric and designed according to the format of the variables commonly encountered in P&C ratemaking: continuous, integer-valued (or discrete) or categorical. The proposed approach improves the current practice favoring chi-square independence tests in contingency tables, avoiding the arbitrary preliminary banding of the variables under consideration. An example with motor insurance data illustrates the usefulness of the tools proposed in this paper. One of the conclusions of this numerical illustration is that zero-modified regression models are necessary to capture the impact of risk factors.