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 2024037, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for creating maps of dominant risks and unsupervised pricing grids. This research article aims to adapt well-established clustering methods to complex insurance datasets containing both categorical and numerical variables. To achieve this, we propose a novel approach based on Burt distance. We begin by reviewing the K-means algorithm to establish the foundation for our Burt distance-based framework. Next, we extend the scope of application of the mini-batch and fuzzy K-means variants to heterogeneous insurance data. Additionally, we adapt spectral clustering, a technique based on graph theory that accommodates non-convex cluster shapes. To mitigate the computational complexity associated with spectral clustering’s 𝑂(𝑛3) runtime, we introduce a data reduction method for large-scale datasets using our Burt distance-based approach.

Keywords: Clustering analysis; unsupervised learning; K-means; spectral clustering (search for similar items in EconPapers)
Pages: 28
Date: 2024-09-05
Note: In: Risks, 2024, vol. 12(9), 141
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2024037

DOI: 10.3390/risks12090141

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