A self-organizing predictive map for non-life insurance

Abstract

This article explores the capacity of self-organizing maps (SOMs) for analysing non-life insurance data. Contrary to feed forward neural networks, also called perceptron, a SOM does not need any a priori information on the relevancy of variables. During the learning procedure, the SOM algorithm selects the most relevant combination of explanatory variables and reduces by this way the collinearity bias. However, the specific features of insurance data require adapting the classic SOM framework to manage categorical variables and the low frequency of claims. This work proposes several extensions of SOMs in order to study the claims frequency of a portfolio of motorcycle insurances. Results are next compared to these computed with variants of the k-mean clustering algorithm.