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

Abstract

Objective - The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. Design/methodology/approach - In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. Findings - 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. Practical implications - To increase the prediction capacity of BM level, psychological questionnaires could be used to measure policyholders' hidden characteristics. Contributions - The main contribution is that the methodology used to carry out research, the Rough Set Theory, has not been applied to this problem.