Risk Premium Prediction of Car Damage Insurance using Artificial Neural Networks and Generalized Linear Models

Abstract: Over the last few years the interest in statistical learning methods, in particular artificial neural networks, has reawakened due to increasing computing capacity, available data and a strive towards automatization of different tasks. Artificial neural networks have numerous applications, why they appear in various contexts. Using artificial neural networks in insurance rate making is an area in which a few pioneering studies have been conducted, with promising results. This thesis suggests using a multilayer perceptron neural network for pricing car damage insurance. The MLP is compared with two traditionally used methods within the framework of generalized linear models. The MLP was selected by cross-validation of a set of candidate models. For the comparison models, a log-link GLM with Tweedie's compound Poisson distribution modeling the risk premium as dependent variable was set up, as well as a two-parted GLM with a log-link Poisson GLM for claim frequency and a log-link Gamma GLM for claim severity. Predictions on an independent test set showed that the Tweedie GLM had the lowest prediction error, followed by the MLP model and last the Poisson-Gamma GLM. Analysis of risk ratios for the different explanatory variables showed that the Tweedie GLM was also the least discriminatory model, followed by the Poisson-Gamma GLM and the MLP. The MLP had the highest bootstrap estimate of variance in prediction error on the test set. Overall however, the MLP model performed roughly in line with the GLM models and given the basic model configurations cross-validated and the restricted computing power, the MLP results should be seen as successful for the use of artificial neural networks in car damage insurance rate making. Nevertheless, practical aspects argue in favor of using GLM. This thesis is written at If P&C Insurance, a property and casualty insurance company active in Scandinavia, Finland and the Baltic countries. The headquarters are situated in Bergshamra, Stockholm.