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.
If a paper is approved for publication, its copyright has to be transferred by the author(s) to the Review of Business Management – RBGN.
Accordingly, authors are REQUIRED to send RBGN a duly completed and signed Copyright Transfer Form. Please refer to the following template: [Copyright Transfer]
The conditions set out by the Copyright Transfer Form state that the Review of Business Management – RBGN owns, free of charge and permanently, the copyright of the papers it publishes. Although the authors are required to sign the Copyright Transfer Form, RBGN allows authors to hold and use their own copyright without restrictions.
The texts published by RBGN are the sole responsibility of their authors.
The review has adopted the CC-BY Creative Commons Attribution 4.0 allowing redistribution and reuse of papers on condition that the authorship is properly credited.