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

Abstract

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.