ANALYTICAL SUPPORT FOR FORECASTING INSURANCE MARKET DEVELOPMENT BASED ON THE EVALUATION OF INSURANCE PORTFOLIOS AND CLUSTERING OF INSURERS
DOI:
https://doi.org/10.37332/Keywords:
insurance market, cluster analysis, financial stability, insurance portfolios, tariff policy, forecasting, compulsory motor third party liability insurance, risk, market structuringAbstract
Kozhan S.V. ANALYTICAL SUPPORT FOR FORECASTING INSURANCE MARKET DEVELOPMENT BASED ON THE EVALUATION OF INSURANCE PORTFOLIOS AND CLUSTERING OF INSURERS
Purpose. The aim of the article is to substantiate an approach for the analytical support of forecasting the development of the Ukrainian insurance market based on the assessment of the quality of insurance portfolios and the clustering of insurers, combining a theoretical generalization of market structuring approaches with practical recommendations for forecasting and risk management.
Methodology of research. The work uses a set of scientific methods: generalization of theoretical approaches (integration of domestic and foreign experience in analytical support for the development of the insurance market), statistical analysis (assessment of the scale of activity, risks and profitability of insurers), comparison of performance indicators (identification of market leaders and adequacy of tariff policy), correlation analysis (study of the relationship between the level of payments and profit margins of insurers), hierarchical clustering method (AHC) (allocation of homogeneous groups of non-life insurers and insurers of compulsory motor third party liability insurance according to insurance premiums, level of payments and profit margins).
Findings. A clear structure of the non-life insurers market and the segment of compulsory motor vehicle liability insurance were identified, and clusters of companies with different levels of risk and financial stability were highlighted. It was shown that companies’ financial results depend more on the efficiency of expense management and investment activities than on pure loss ratios. The combination of statistical analysis and clustering enables forecasting companies’ stability and determining effective tariff policies.
Originality. A comprehensive approach to analytical support for insurance market forecasting was developed, integrating the theoretical generalization of insurer performance assessment methods with practical tools for clustering and risk analysis. It was demonstrated that combining classical statistical methods with market structuring improves the accuracy of forecasting models and allows accounting for the internal heterogeneity of insurers.
Practical value. The proposed approach provides a basis for managerial decision-making: portfolio optimization, tariff and underwriting adjustments, forecasting financial stability, enhancing the efficiency of risk-oriented regulatory oversight, and market segmentation for investors. The results allow for combining an analytical framework with practical application under the real conditions of the Ukrainian insurance market.
Key words: insurance market, cluster analysis, financial stability, insurance portfolios, tariff policy, forecasting, compulsory motor third party liability insurance, risk, market structuring.
References
1. Sereda, O. (2025), “Modern trends in insurance development and methodology of insurance market research”, Ekonomika ta suspilstvo, Issue no. 80, DOI: https://doi.org/10.32782/2524-0072/2025-80-47.
2. Dluhopolskyi, O., Ivashchuk, Yu. and Herasymets, A. (2025), “Trends in the development of the global insurance market under uncertainty”, Svit finansiv, no. 1(82), pp. 147-163, available at: http://sf.wunu.edu.ua/index.php/sf/article/view/1791 (access date September 15, 2025).
3. “Global insurance market trends in 2025: tariffs and inflation as key concerns for insurers”, available at: https://forinsurer.com/news/25/04/21/44812 (access date September 15, 2025).
4. Metelenko, N., Silina, I. and Zhovnir-Vasylenko, K. (2025), “Adaptation of the insurance market of Ukraine to global challenges and European integration”, Ekonomika ta suspilstvo, Issue no. 71, DOI: https://doi.org/10.32782/2524-0072/2025-71-50.
5. Nikolchuk, Yu.M. and Snihur, V.M. (2025), “Modern aspects of insurance market development in Ukraine”, Transformatsiina ekonomika, no. 2(11), pp. 74-81, DOI: https://doi.org/10.32782/2786-8141/2025-11-12.
6. Panchenko, O. and Akinchyts, O. (2025), “Threats and challenges of digitalization of the insurance services market in Ukraine”. Problemy i perspektyvy ekonomiky ta upravlinnia, no. 1(41), pp. 386-395, DOI: https://doi.org/10.25140/2411-5215-2025-1(41)-386-395.
7. Miachyn, V.H. and Yavorska, O.B. (2018), “Modern methods and models of insurance market functioning”, Naukovyi visnyk Uzhhorodskoho natsionalnoho universytetu: seriia: Mizhnarodni ekonomichni vidnosyny ta svitove hospodarstvo, Vol. 21, Part 2, pp. 37-40, available at: http://www.visnyk-econom.uzhnu.uz.ua/archive/21_2_2018ua/9.pdf (access date September 10, 2025).
8. Sikhov, M.B., Beibitbekov, A.B. and Sapin, A.M. (2019), “Cluster analysis application in the compulsory insurance of civil-legal liability of vehicle owners”, Journal of Mathematics, Mechanics and Computer Science, no. 102(2), pp. 81-96, DOI: https://doi.org/10.26577/JMMCS-2019-2-28.
9. Artym-Drohomyretska, Z., Harmatii, N., Krytska, L. and Harmatii, S. (2022), “Statistical analysis of activity of insurance companies of Ukraine by cluster analysis tools”, Halytskyi ekonomichnyi visnyk, Vol. 74, no. 1, pp. 7-15.
10. Riznyk, N., Harmatii, S. and Makohon, A. (2019), “Analysis of insurance market dynamics of the national economy and identification of main clusters using economic-mathematical modeling tools”, Halytskyi ekonomichnyi visnyk, Vol. 60, no. 5, pp. 27-39.
11. Podoliak, O.L. (2025), “Evolution of the insurance market development of Ukraine in the digital economy”, Problemy i perspektyvy ekonomiky ta upravlinnia, no. 2(42), pp. 432-443, available at: http://ppeu.stu.cn.ua/article/view/335134 (access date September 20, 2025).
12. Skryl, V. and Skryl, I. (2025), “Inclusive innovations as a driver of insurance market development in Ukraine”, Ekonomika i rehion, no. 1(96), pp. 211-220, DOI: https://doi.org/10.26906/EiR.2025.1(96).3766.
13. Martseniuk, O.V., Ruda, O.L. and Fedorov, K.S. (2025), “Activities of insurance companies in the personal insurance market under wartime economy”, Efektyvna ekonomika, no. 10, DOI: https://doi.org/10.32702/2307-2105.2025.10.115.
14. Chvertko, L. and Vinnytska, O. (2025), “Trends in the development of the insurance market of Ukraine under economic uncertainty”, Ekonomichni horyzonty, no. 3(32), pp. 80-87, DOI: https://doi.org/10.31499/2616-5236.3(32).2025.334882.
15. Babenko-Levada, V.H. and Skorba, O.A. (2021), “Forecasting the development of the insurance market of Ukraine: an indeterministic approach”, Efektyvna ekonomika, no. 12, DOI: https://doi.org/10.32702/2307-2105-2021.12.101.
16. Avanzi, B., Li, Y., Wong, B., and Xian, A. (2024). Ensemble distributional forecasting for insurance loss reserving. Scandinavian Actuarial Journal, no. 9, pp. 971–1012, DOI: https://doi.org/10.1080/03461238.2024.2365392.
17. Akogul, S. and Erisoglu, M. (2017), “An approach for determining the number of clusters in a model-based cluster analysis”, Entropy, Vol. 19, Iss. 9, DOI: https://doi.org/10.3390/e19090452.
18. Jamotton, C., Hainaut, D. and Hames, T. (2024), “Insurance analytics with clustering techniques”, Risks, Vol. 12, Iss. 9, DOI: https://doi.org/10.3390/risks12090141.
19. Skrypnyk, M. (2025), “Development of the actuarial analysis concept in the insurance business”, Visnyk Chernivetskoho torhovelno-ekonomichnoho instytutu. Ekonomichni nauky, no. 1(97), pp. 43-58, DOI: http://doi.org/10.34025/2310-8185-2025-1.97.03.
20. National Bank of Ukraine (2025), “Supervisory statistics data. Key performance indicators of insurance companies (by institutions), in accordance with the Resolution of the Cabinet of Ministers of Ukraine no. 835”, available at: https://bank.gov.ua/files/stat/Indicators_Insurance_companies_2025-10-01.xlsx (access date October 10, 2025).
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