Original scientific paper

https://doi.org/10.1080/1331677X.2016.1175729

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

Vladimir Kašćelan orcid.org/0000-0001-8763-7108
Ljiljana Kašćelan orcid.org/0000-0001-9831-7599
Milijana Novović Burić orcid.org/0000-0001-7671-6468

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.

Keywords

Car insurance; net risk premium; data mining; clustering; support vector regression (SVR); kernel logistic regression (KLR)

Hrčak ID:

171743

URI

https://hrcak.srce.hr/171743

Publication date:

22.12.2016.

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