Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies

Abstract

The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders' policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors.