Comparative analysis of class imbalance reduction methods in building machine learning models in the financial sector
- Authors: Konstantinov A.F.1, Dyakonova L.P.1
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Affiliations:
- Plekhanov Russian University of Economics
- Issue: Vol 27, No 5 (2025)
- Pages: 68-79
- Section: System analysis, management and information processing, statistics
- Submitted: 13.11.2025
- Published: 20.11.2025
- URL: https://journals.rcsi.science/1991-6639/article/view/351245
- DOI: https://doi.org/10.35330/1991-6639-2025-27-5-68-79
- EDN: https://elibrary.ru/LPRGUP
- ID: 351245
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Abstract
Borrower default prediction is a pressing issue that underlies the financial stability of credit institutions.
Aim. This study is to develop and evaluate an integrated borrower default prediction method.
Materials and methods. The study was conducted by simulating the integrated borrower default prediction method, analyzing and comparing the results with the baseline AI model, and drawing conclusions.
Results. Based on the analysis of dependencies, an integrated borrower default prediction methods developed and calculated. It demonstrated a significant improvement in quality metrics (an increase in average accuracy of 0.383, an increase in f1-score of 0.509, and an increase in accuracy of 0.792) relative to the baseline model. This article presents the results of experiments aimed at improving the quality metrics of machine learning models used to predict borrower default.
Conclusion. The development of integrated borrower default prediction methods will improve the accuracy and reliability of forecast models, which is of great practical importance.
About the authors
A. F. Konstantinov
Plekhanov Russian University of Economics
Email: konstantinovaf@gmail.com
ORCID iD: 0009-0000-9591-3301
SPIN-code: 3088-3121
Postgraduate Student, Department of Informatics
Russian Federation, 36, Stremyannyy lane, Moscow, 115054, RussiaL. P. Dyakonova
Plekhanov Russian University of Economics
Author for correspondence.
Email: Dyakonova.LP@rea.ru
ORCID iD: 0000-0001-5229-8070
SPIN-code: 2513-8831
Candidate of Physical and Mathematical Sciences, Associate Professor,
Department of Informatics
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