Published : 2025-02-17

Clustering Overdue Receivables in the Insurance Sector: A Mixed Data Approach

Abstract

This study addresses the issue of overdue receivables from the secondary market. The main objective of the research is to evaluate the aplication of the Fast K-Prototypes algorithm to the overdue insurance receivables segmentation, considering how selected parameters and data quality influences obtained results. The article also addresses the repayment of receivables from the insurance sector and the assessment of the risks they generate. The research sample includes 2376 recourse claims which arose from motor insurance and have been acquired between 2012 - 2023 by a polish debt collection company.

The application of the Fast K-Prototypes method enabled the segmentation of overdue receivables into various credit risk groups, provided that specific parameters were applied, and the input data was of high quality thanks to preliminary analysis and appropriate preparation. The analysis confirms that these assets are associated with a significant level of credit risk. The results indicate that the application of the Fast K-Prototypes method supports the debt recovery process optimization. However, the effectiveness of this method depends on the research sample and suggests the importance of further research in the context of diverse data samples.

Keywords:

overdue receivables, insurance sector, recovery claims, credit risk, Fast K-Prototypes

JEL Codes

C38, G22


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Kuchno, J. (2025). Clustering Overdue Receivables in the Insurance Sector: A Mixed Data Approach. Safe Bank, 97(4), 65–82. https://doi.org/10.26354/bb.4A.4.97.2024

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