MODELING INSURANCE PRICING POLICIES USING CLUSTER AND REGRESSION ANALYSIS

Tat'yana R. Igonina; Vladimir Keselman; Mariya I. Dzhioeva; Gennadiy B. Voronov

doi:10.55186/2413046X_2026_11_5_67

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MODELING INSURANCE PRICING POLICIES USING CLUSTER AND REGRESSION ANALYSIS

Published in MOSCOW ECONOMIC JOURNAL · Volume 11, Issue 5 · Pages 125–145 · Rubric: Regional and branch economy

DOI: https://doi.org/10.55186/2413046X_2026_11_5_67 · EDN: YQVDUI

Received: 20.04.2026 Accepted: 08.06.2026 Published: 09.06.2026 Language of publication: ENG

Authors

Tat'yana R. Igonina

Vladimir Keselman

Mariya I. Dzhioeva

Gennadiy B. Voronov

Tat'yana R. Igonina

Academic degree: candidate of physical and mathematical sciences

Federal State Budgetary Educational Institution of Higher Education "MIREA – Russian Technological University" Associate Professor · Department of Higher Mathematics · Russian Federation · Moscow

Vladimir Keselman

Federal'noe gosudarstvennoe byudzhetnoe obrazovatel'noe uchrezhdenie vysshego obrazovaniya «MIREA – Rossiyskiy tehnologicheskiy universitet» docent · kafedra vysshey matematiki · Russian Federation · Москва

Mariya I. Dzhioeva

Academic degree: candidate of physical and mathematical sciences

Gennadiy B. Voronov

Academic degree: candidate of technical sciences

ABSTRACT

The study aims to develop a methodology for determining optimal insurance service pricing based on cluster analysis and regression modeling techniques. The relevance of the research is driven by the growing need to improve pricing accuracy in insurance markets characterized by increasing competition and uncertainty. The percentage of settled claims and the average claim payment amount were selected as the primary determinants of insurance policy cost. The methodological framework combines multivariate statistical analysis, hierarchical clustering, and econometric modeling. Insurance companies were classified using standardized data, the furthest-neighbor clustering method, and the block distance metric. Linear and nonlinear regression models were subsequently developed for each cluster to describe the relationship between policy cost and key insurance performance indicators. The empirical analysis identified three stable clusters of insurance companies with distinct claim settlement and payment characteristics. The results indicate that the relationship between policy cost and explanatory variables is heterogeneous across clusters. Linear specifications provide the best fit for certain groups, whereas nonlinear models demonstrate superior predictive performance for others. The developed models exhibit strong explanatory power and forecasting accuracy. The practical significance of the study lies in the applicability of the proposed cluster–regression framework for insurance pricing, policy cost forecasting, and portfolio management optimization. The methodology is universal in nature and can be adapted to various insurance segments provided that relevant statistical data are available.

KEYWORDS

insurance, insurance policy, pricing, cluster analysis, regression modeling, econometrics, tariff policy, forecasting

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Aleksandr Fomin

e-science@list.ru

Publishing editor

Ekaterina Syamina

e-science@list.ru

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Igonina T. R., Keselman V. M., Dzhioeva M. I., Voronov G. B. MODELING INSURANCE PRICING POLICIES USING CLUSTER AND REGRESSION ANALYSIS // MOSCOW ECONOMIC JOURNAL. 2026. no. 5. pp. 125-145. DOI: https://doi.org/10.55186/2413046X_2026_11_5_67. EDN: YQVDUI. (Date of access 08.10.2026).

MLA

Tat'yana R. Igonina, Keselman V. M., Dzhioeva M. I., Voronov G. B.. "MODELING INSURANCE PRICING POLICIES USING CLUSTER AND REGRESSION ANALYSIS." MOSCOW ECONOMIC JOURNAL 11.5 (2026): 125-145. DOI: https://doi.org/10.55186/2413046X_2026_11_5_67. EDN: YQVDUI.

APA

Igonina T. R., Keselman V. M., Dzhioeva M. I., Voronov G. B. (2026). MODELING INSURANCE PRICING POLICIES USING CLUSTER AND REGRESSION ANALYSIS. MOSCOW ECONOMIC JOURNAL, 11(5), 125-145. https://doi.org/10.55186/2413046X_2026_11_5_67 EDN: YQVDUI.

Chicago

Tat'yana R. Igonina, Keselman V. M., Dzhioeva M. I., Voronov G. B.. "MODELING INSURANCE PRICING POLICIES USING CLUSTER AND REGRESSION ANALYSIS." MOSCOW ECONOMIC JOURNAL 11, no. 5 (2026): 125-145. https://doi.org/10.55186/2413046X_2026_11_5_67. EDN: YQVDUI.

WIKI

{{cite journal | author = Igonina T. R., Keselman V. M., Dzhioeva M. I., Voronov G. B. | title = MODELING INSURANCE PRICING POLICIES USING CLUSTER AND REGRESSION ANALYSIS | journal = MOSCOW ECONOMIC JOURNAL | year = 2026 | volume = 11 | issue = 5 | pages = 125-145 | doi = 10.55186/2413046X_2026_11_5_67 }} EDN: YQVDUI

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