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.
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Notes
In our approach, codebooks are randomly chosen. An alternative consists to use the initialization procedure of the k-means algorithm, presented in Sect. 2.2.
A variant of this algorithm consists to recompute immediately the new position of centroids after assignment of each records of the dataset.
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Acknowledgements
I gratefully acknowledges the BNP Cardif Chair “Data Analytics and Models for Insurance” for its financial support. I also thank Michel Denuit from the UCL for his constructive advices.
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Hainaut, D. A self-organizing predictive map for non-life insurance. Eur. Actuar. J. 9, 173–207 (2019). https://doi.org/10.1007/s13385-018-0189-z
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DOI: https://doi.org/10.1007/s13385-018-0189-z