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

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Segovia Vargas, María Jesús and Camacho Miñano, María del Mar and Pascual Ezama, David (2015) Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies. Revista Brasileira de Gestao de Negocios, 15 (57). pp. 1228-1245. ISSN 1983-0807

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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.


Item Type:Article
Uncontrolled Keywords:Automobile insurance company; Risk factors; Bonus malus system; Rough set theory; Artificial intelligence
Subjects:Sciences > Computer science > Artificial intelligence
Social sciences > Economics > Insurance
ID Code:59976
Deposited On:22 Apr 2020 10:07
Last Modified:22 Apr 2020 11:47

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