Insurance Analytics with Clustering Techniques

Charlotte Jamotton (), Donatien Hainaut and Thomas Hames
Additional contact information
Charlotte Jamotton: Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium
Donatien Hainaut: Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), Université Catholique de Louvain (UCLouvain), 1348 Louvain-la-Neuve, Belgium
Thomas Hames: Detralytics, Rue Belliard 2-B, 1040 Brussels, Belgium

Risks, 2024, vol. 12, issue 9, 1-28

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 ( n 3 ) 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)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-9091/12/9/141/pdf (application/pdf)
https://www.mdpi.com/2227-9091/12/9/141/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:12:y:2024:i:9:p:141-:d:1472255

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().