Cluster-Driven Risk Classification

Adapting Car Insurance Risk Models through Zip Code and License Plate Clustering

Master thesis (2024)

Authors

A.J. Wijker Electrical Engineering, Mathematics and Computer Science

Contributors

N. Parolya Statistics - EEMCS (mentor)
N.V. Budko Numerical Analysis - EEMCS (mentor)
Tim de Wit (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science
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Published Date

25-07-2024

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis aims to improve the current risk classification for (company) car insurance at Achmea, focusing on WAM and ARD coverages. By using cluster analysis, specifically K-prototypes and spectral clustering, policyholders are grouped based on zip codes and license plates to enhance the current claim frequency models (and thus premium pricing models). The application of spectral clustering (with U-SPEC as the observation reduction technique) led to significant improvement of the current claim frequency GLM for the ARD coverage. This thesis highlights the potential of cluster analysis in actuarial science, offering new methods for mixed data types and high-dimensional clustering, thus providing a foundation for more accurate claim frequency models. The time stabilities and stabilities with respect to the number of observations of the clusters are also investigated.