Selection of Informative Indicators for Assessing the Economic Security of Russian Companies

Lev A. Bulanov 1, Alexei V. Kalina 1,2, Vadim V. Krivorotov 1

1 Ural Federal University named after the First President of Russia B.N. Yeltsin, Yekaterinburg, Russia
2 Institute of Economics, The Ural Branch of Russian Academy of Sciences, Yekaterinburg, Russia

Abstract

Ensuring the economic security of enterprises and companies is one of the top priorities under the current economic and political conditions. Central to solving this problem is a comprehensive and objective assessment of the economic security of companies, which is usually based on the method of indicative analysis. This, in turn, necessitates the development and formation of indicators of economic security, which would most comprehensively and qualitatively reflect the state of companies both in the current and future periods. The purpose of this article is the development of an algorithm and its model implementation for selecting the most significant indicators of economic security of enterprises based on the use of machine learning technologies. To achieve this goal, the paper explores the interpretable multiclass classification of large manufacturing companies (“bad/normal/good”) according to their financial coefficients with the formation of target labels by K-Means++ clustering on a real dataset for 2023 (2,249 observations). One-dimensional filters (ANOVA/n2, one-vs-rest ROC-AUC, Mutual Information) and model SHAP values for CatBoost are compared; the comparison is carried out through correlation of ranks, intersection of top-k and ablation with a logloss estimate on a stratified k-fold CV with strict separation of train/val. Additionally, the compromise “compactness vs accuracy” at k is evaluated. {5, 10, 15, 20, 30} and the statistical significance of the differences is checked (the Wilcoxon criterion). The proposed approach and its implementation for Russian companies have revealed that their own working capital is the basis for the formation and evaluation of the most significant and informative indicators of the companies' economic security. At the same time, class profiles show asymmetric, economically consistent threshold: for “bad” companies, the decisive factor is a shortage of working capital and low liquidity; for “good” companies, high fast/current liquidity and coverage; “normal” companies are formed as a buffer with moderate values and serviced debt. The primary practical result of the research is a set of actionable recommendations as to the minimum set of indicators and a strategy for selecting features for monitoring the economic security and financial condition of companies and applied financial analytics.

Keywords

economic security indicators; machine learning; cross-validation; feature selection; ANOVA; ROC-AUC; Mutual Information; CatBoost; SHAP.

JEL classification

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About Authors

Lev Alexeevich Bulanov

Post-Graduate Student, Department of Economic Safety of Industrial Complexes, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID https://orcid.org/0009-0001-0242-0127 e-mail: levbulanov2013@yandex.ru

Alexei Vladimirovich Kalina

Candidate of Technical Sciences, Associate Professor, Department of Economic Safety of Industrial Complexes, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19), Senior Researcher, The Center of Economic Security, Institute of Economics, The Ural Branch of Russian Academy of Sciences, Yekaterinburg, Russia (620014, Yekaterinburg, Moskovskaya street, 29); ORCID https://orcid.org/0000-0003-0376-2505 e-mail: alexkalina74@yandex.ru

Vadim Vasilyevich Krivorotov

Doctor of Economics, Professor, Head of Department of Economic Safety of Indus-trial Complexes, Ural Federal University named after the first President of Russia B.N. Yeltsin, Yekaterinburg, Russia (620002, Yekaterinburg, Mira street, 19); ORCID https://orcid.org/0000-0002-7066-0325 e-mail: v_krivorotov@mail.ru

For citation

Bulanov, L.A., Kalina, A.V., Krivorotov, V.V. (2025). Selection of Informative Indicators for Assessing the Economic Security of Russian Companies. Journal of Applied Economic Research, Vol. 24, No. 4, 1371-1415. https://doi.org/10.15826/vestnik.2025.24.4.045

Article info

Received September 2, 2025; Revised October 9, 2025; Accepted October 23, 2025.

DOI: https://doi.org/10.15826/vestnik.2025.24.4.045

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