A Data Mining Approach for Risk Assessment in Car Insurance: Evidence from Montenegro

A Data Mining Approach for Risk Assessment in Car Insurance: Evidence from Montenegro

Ljiljana Kašćelan (Faculty of Economics, University of Montenegro, Podgorica, Montenegro), Vladimir Kašćelan (Faculty of Economics, University of Montenegro, Podgorica, Montenegro) and Milijana Novović-Burić (Faculty of Economics, University of Montenegro, Podgorica, Montenegro)
Copyright: © 2014 |Pages: 18
DOI: 10.4018/ijbir.2014070102
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Abstract

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, 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 is the one in Montenegro.
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2. Review Of Literature

Aggregate claims for a homogeneous insurance portfolio have long been estimated using pure algorithmic methods (Chain-Ladder, Bornhuetter & Ferguson and Poisson) or simple stochastic methods (Generalized linear models, Bayesian, Distributional, Bootstrap method, and other) (Wuthrich & Merz, 2008; De Jong, & Heller 2008). Algorithmic, distribution-free methods use mechanical technics (run-off triangle) to predict claim reserves. This understanding does not allow for the quantification of the uncertainties in these predictions. Uncertainties can only be determined if we have an underlying stochastic model on which the prediction algorithms can be based. Some recent studies suggest improvements for the existing stochastic models (Björkwall, Hössjer, Ohlsson & Verrall, 2011; Brillinger, 2012; Zhang, Dukic & Guszcza, 2012).

For micro-level (level of individual claims), recent studies have perceived that a mixed discrete-continuous model may be appropriate to estimate claims and risk in insurance data (Christmann, 2004; Heller, Stasinopoulos & Rigby, 2006; Parnitzke, 2008; Bortoluzzo, Claro, Caetano & Artes, 2011; Huo,Wang, & Yang, 2013). According to Parnitzke (2008), the model explicitly specifies a logit-linear model for the occurrence of a claim (i.e. claim probability) and linear model for the mean claim size. Generalized linear models and more flexible Tweedie’s compound Poisson model are often used to construct insurance tariffs (Smyth & Jorgensen, 2002). However, even this more general models still can yield problems in modeling high-dimensional realtionships which is quite common for insurance data set. The best modeling in these circumstances is one which using methods from machine learning and data mining (Christmann, 2004). In recent years many papers deal with the application of data mining methods for loss cost estimation and risk analysis in insurance (Xiahou & Mu, 2010; Guelman, 2012; Thakur & Sing, 2013; Huo, Wang & Yang, 2013).

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