Identifying Insurance Companies’ Business Models in Ukraine: Cluster Analysis and Machine Learning

Oleksandr Tarnavskyi () and Viktor Kolomiiets ()
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Oleksandr Tarnavskyi: National Bank of Ukraine
Viktor Kolomiiets: National Bank of Ukraine

Visnyk of the National Bank of Ukraine, 2021, issue 252, 37-55

Abstract: This study examines the performance of the nonlife insurance companies that operated in Ukraine in 2019– 2020. Specifically, we employ a set of clustering techniques, e.g. the classic k-means algorithm and Kohonen self-organizing maps, to investigate the characteristics of the Retail, Corporate, Universal (represented by two clusters), and Reinsurance business models. The clustering is validated with classic indicators and a migration ratio, which ensures the stability of the clusters over time. We analyze the migration of companies between the identified clusters (changes in business model) during the research period and find significant migration between the Reinsurance and Corporate models, and within the Universal model. Analysis of the data on the terminatio of the insurers’ ongoing activity allows us to conclude that companies following the Universal business model appear to be the most financially stable, while their peers grouped into the Reinsurance cluster are likely to be the least stable. The findings of this research will be valuable for insurance supervision and have considerable policy implications.

Keywords: neural networks; business model; cluster analysis; insurance (search for similar items in EconPapers)
JEL-codes: D22 G22 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ukb:journl:y:2021:i:252:p:37-55

DOI: 10.26531/vnbu2021.252.02

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