A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market

Vladimir Kašćelan, Ljiljana Kašćelan and Milijana Novović Burić

Economic Research-Ekonomska Istraživanja, 2016, vol. 29, issue 1, 545-558

Abstract: For prediction of risk in car insurance we used the nonparametric data mining techniques such as clustering, support vector regression (SVR) and kernel logistic regression (KLR). The goal of these techniques is to classify risk and predict claim size based on data, thus helping the insurer to assess the risk and calculate actual premiums. We proved that used data mining techniques can predict claim sizes and their occurrence, based on the case study data, with better accuracy than the standard methods. This represents the basis for calculation of net risk premium. Also, the article discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such as Montenegrin.

Date: 2016
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DOI: 10.1080/1331677X.2016.1175729

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