Insurance analytics with clustering techniques

Charlotte Jamotton (), Donatien Hainaut () and Thomas Hames ()
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Charlotte Jamotton: Université catholique de Louvain, LIDAM/ISBA, Belgium
Donatien Hainaut: Université catholique de Louvain, LIDAM/ISBA, Belgium
Thomas Hames: Detralytics

No 2023002, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: The k-means algorithm and its variants are popular clustering techniques. Their purpose is to uncover group structures in a dataset. In actuarial applications, these partitioning methods detect clusters of policies with similar features and allow one to draw up a map of dominant risks. The main challenge lies in de􏰂ning a distance between two observations exclusively characterised by categorical variables. This research paper starts with a review of the k-means algorithm and develops an extension based on Burt's framework to manage categorical rating factors. We then focus on a mini-batch version that keeps computation time under control when analysing a large-scale dataset. We next broaden the scope of application of the fuzzy k-means to fully categorised datasets. Lastly, we conclude with a thorough introduction to spectral clustering and work around the dimensionality issue by reducing the size of the initial dataset with k-means.

Keywords: Clustering analysis; unsupervised learning; k-means; spectral clustering (search for similar items in EconPapers)
Pages: 27
Date: 2023-01-12
New Economics Papers: this item is included in nep-cmp
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Citations: View citations in EconPapers (1)

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