- Uluslararası Yönetim İktisat ve İşletme Dergisi
- Cilt: 21 Sayı: 4
- PREDICTING BANK FAILURE RISK IN FINANCIAL CRISES: ANALYSIS OF RISK FACTORS WITH MACHINE LEARNING
PREDICTING BANK FAILURE RISK IN FINANCIAL CRISES: ANALYSIS OF RISK FACTORS WITH MACHINE LEARNING
Authors : Ersin Kanat
Pages : 1797-1811
Doi:10.17130/ijmeb.1688787
View : 146 | Download : 312
Publication Date : 2025-12-25
Article Type : Research Paper
Abstract :This study aims to predict the bankruptcy risk of banks during financial crises by analyzing their financial structure using machine learning methods. Taking the 2001 Turkish economic crisis as a case study, the financial ratios of failed banks are compared with those that remained operational. Financial indicators such as profitability, liquidity, and capital adequacy are evaluated through traditional ratio analysis and advanced machine learning models, including C5.0 Decision Tree, CART, and XGBoost. The performance of these models is assessed using evaluation metrics such as the ROC curve and AUC value. The findings show that financial ratios like Net Working Capital/Total Assets, Net Interest Income after Non-performing Loans/Average Total Assets, and Interest Income/Interest Expense play a critical role in bankruptcy prediction. Among the models, CART and XGBoost performed with perfect classification accuracy (AUC = 1), while the C5.0 model also achieved a high level of success (AUC = 0.9318). The results provide practical insights for banks in managing financial sustainability and improving early warning systems. They emphasize the importance of maintaining a strong capital base and monitoring interest income and liquidity indicators, especially during periods of economic instability. This research contributes to financial risk management literature by integrating interpretable machine learning methods with financial ratio analysis.Keywords : İflas Tahmini, Finansal Krizler, Bankacılık Sektörü, Makine Öğrenimi, Finansal Sürdürülebilirlik.
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