{"id":4926,"date":"2025-11-16T14:12:13","date_gmt":"2025-11-16T14:12:13","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=4926"},"modified":"2026-04-13T08:08:54","modified_gmt":"2026-04-13T07:08:54","slug":"27-5-6-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-5-6-en\/","title":{"rendered":"27.5.6 En"},"content":{"rendered":"\n

Comparative analysis of class imbalance reduction methods in building machine learning models in the financial sector<\/strong><\/h1>\n\n\n\n

A.F. Konstantinov, L.P. Dyakonova<\/strong><\/p>\n\n\n\n

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Upload the full text<\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/strong><\/strong>: <\/em>Borrower default prediction is a pressing issue that underlies the financial stability of credit institutions.
Aim<\/strong>. This study is to develop and evaluate an integrated borrower default prediction method.<\/p>\n\n\n\n

Materials and methods<\/strong>. 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<\/strong>. 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<\/strong>. The development of integrated borrower default prediction methods will improve the accuracy and reliability of forecast models, which is of great practical importance.<\/p>\n\n\n\n

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Keywords<\/strong><\/em><\/strong>:<\/em> methods for reducing class imbalance, methods for isolating anomalies into a separate model, bagging method, integral method for predicting borrower default<\/p>\n\n\n\n

<\/p>\n\n\n\n

For citation<\/strong>.<\/strong> Konstantinov A.F., Dyakonova L.P. Comparative analysis of class imbalance reduction methods in building machine learning models in the financial sector. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. Vol. 27. No. 5. Pp. 68\u201379. DOI: 10.35330\/1991-6639-2025-27-5-68-79<\/p>\n\n\n\n

R<\/strong>eferences<\/summary>\n
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    Information about the author<\/strong>s<\/summary>\n
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    Alexey F. Konstantinov<\/strong>, Postgraduate Student, Department of Informatics, Plekhanov Russian University of Economics;
    36 Stremyannyy lane, Moscow, 115054, Russia;
    konstantinovaf@gmail.com, ORCID: https:\/\/orcid.org\/0009-0000-9591-3301, SPIN-code: 3088-3121
    Lyudmila P. Dyakonova<\/strong>, Candidate of Physical and Mathematical Sciences, Associate Professor, Department of Informatics, Plekhanov Russian University of Economics;
    36 Stremyannyy lane, Moscow, 115054, Russia;
    Dyakonova.LP@rea.ru, ORCID: https:\/\/orcid.org\/0000-0001-5229-8070, SPIN-code: 2513-8831<\/p>\n\n\n\n

    <\/p>\n\n\n\n

    <\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

    Comparative analysis of class imbalance reduction methods in building machine learning models in the financial sector A.F. Konstantinov, L.P. Dyakonova Upload the full text 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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-4926","page","type-page","status-publish","hentry"],"yoast_head":"\n27.5.6 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-5-6-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.5.6 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Comparative analysis of class imbalance reduction methods in building machine learning models in the financial sector A.F. 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