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

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.

Optimal arrangements of hyperplanes for SVM-based multiclass classification

“…The second set of constraints, namely (26), can be analogously justified in terms of the negative side of H r . The main difference of these constraints with respect to (23) and (24) is that (25) and (26) are active only if x i is wrong-classified. According to the above constraints, a misclassified observation x i is penalized in two ways with respect to each hyperplane H r .…”

“…Actually, one can project the original data out onto a higher dimensional space where the separation of the classes can be more adequately performed, and still keeping the same computational effort that was required in the original problem. This fact is the so-called kernel trick, and very likely this is one of the reasons that has motivated the successful use of this tool in a wide range of applications [4,19,23,30,38].…”

Gram-Schmidt Orthogonalization for Feature Ranking and Selection — A Case Study of Claim Prediction

“…This described research shows that basic methods, such as SVM can be used for problem claim prediction. For risk predictions in car insurance, Kašćelan uses nonparametric data mining techniques such as clustering, Support Vector Regression (SVR), and Kernel Logistic Regression (KLR) [16].…”

Estimating consumers’ behaviour in motor insurance using discrete choice models

“…Liu et al (2011) prove that risk preferences, along with income and education, are important predictors of voluntary insurance demand. The differences between the socio-demographic characteristics are correlated with risk perception, thus infl uencing the insurance demand and the risk-taking behaviour (Ioncică et al, 2012;Kašćelan et al, 2016).…”