Russian Federation
The paper presents a comparative analysis of modern bankruptcy prediction models based on international studies conducted in 2023–2025. Logistic regression, ensemble methods (Random Forest, XGBoost, Gradient Boosting), neural networks, and AutoML approaches are examined. nsemble algorithms consistently outperform logistic regression in terms of AUC on high-dimensional financial data, whereas neural and LLM-based approaches remain sensitive to dataset size and structure. No universal model is identified; model selection depends on data characteristics, class imbalance, and interpretability requirements.
corporate bankruptcy, prediction, ROC-AUC, Accuracy, XGBoost, Random Forest, Gradient Boosting, logistic regression, neural networks, ensemble methods; SMOTE, AutoML
1. Lokanan M.E., Ramzan S. Prognozirovanie finansovyh problem kompaniy, kotiruyuschihsya na birzhe TSX, s ispol'zovaniem algoritmov mashinnogo obucheniya // Front Artif Intell v. 7. 2024. DOI:https://doi.org/10.3389/frai.2024.1466321.
2. Hamdi M., Mestiri S., Arbi A. Metody iskusstvennogo intellekta dlya prognozirovaniya bankrotstva tunisskih kompaniy: primenenie modeley mashinnogo i glubokogo obucheniya // Journal of Risk and Financial Management. – 2024. – T. 17. – № 4. – St. 132. DOI: https://doi.org/10.3390/jrfm17040132
3. Maktum T., Pulgram N., Chandgradkar V., Pathak P. Solanki A. Mashinnoe obuchenie dlya prognozirovaniya bankrotstva v sfere korporativnyh finansov s ispol'zovaniem ponyatnyy metodov II // Informatica 49. C. 15-26. 2025. DOI:https://doi.org/10.31449/inf.v49i15.6745.
4. Mohtashami Zade M. Modelirovanie veroyatnosti bankrotstva listingovyh kompaniy s ispol'zovaniem algoritmov klassifikacii (Random Forest, XGBoost, SVM) // Journal of Resource Management and Decision Engineering. – 2026. – T. 5. – № 2. – S. 1–9. DOI:https://doi.org/10.61838/kman.jrmde.5.2.239
5. Kostreva M., Furman O., Furman R., Tomchak S., Zieba M. Polezny li bazovye modeli dlya prognozirovaniya bankrostva? // 39-ya konferenciya po neyronnym sistemam obrabotki informacii. NeurlPS 2025. Seminar: Generativnyy II v finansah. 2025. DOI:https://doi.org/10.48550/arXiv.2511.16375.
6. Sumin, V. I. Ispol'zovanie situacionnogo modelirovaniya v razrabotke sistem prinyatiya resheniy dlya slozhnyh organizacionnyh sistem / V. I. Sumin, A. S. Kravchenko, A. V. Tolkachev // Modelirovanie sistem i processov. – 2024. – T. 17, № 3. – S. 71–79. – DOIhttps://doi.org/10.12737/2219-0767-2024-69-77. – EDN FBBHJO.
