An innovative approach to ensemble learning in bankruptcy prediction using support vector machines and meta fuzzy functions

Karadayı Ataş P., TAK N., Özöğür-Akyüz S., EYGİ ERDOĞAN B.

Information Sciences, cilt.719, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 719
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.ins.2025.122450
  • Dergi Adı: Information Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Library, Information Science & Technology Abstracts (LISTA), Metadex, MLA - Modern Language Association Database, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Bank bankruptcy, Ensemble learning, Meta fuzzy functions, Support vector machines (SVMs)
  • Marmara Üniversitesi Adresli: Evet

Özet

The categorization of banks into successful and unsuccessful is essential for ensuring financial stability, effective risk management, and appropriate regulatory oversight. This study introduces a new ensemble modeling method for bank classification that combines meta fuzzy functions (MFFs) with support vector machines (SVMs). We predict bank status (failed or successful) by analyzing financial ratios, such as liquidity, profitability, and solvency metrics, using a dataset of Turkish commercial banks. Gaussian kernel-based SVMs, known for their strong classification performance, serve as the ensemble's base classifiers. Linear kernel SVMs are employed for comparison with previous studies. Because the data structure is a panel data, the proposed approach is compared with a single panel logistic regression model and a previously proposed ensemble approach. The results show that the MFF-based ensemble outperforms both baseline models, achieving an accuracy of [85.4%] and an AUC-ROC score of [87%]. This work demonstrates how ensemble learning using MFFs can enhance bank classification, providing a strong tool for financial analysts and policymakers in times of economic instability.