🤖 AI Summary
This study addresses the limitations of conventional statistical models—such as restrictive linearity and distributional assumptions—and insufficient predictive accuracy in bank failure forecasting. To overcome these challenges, we propose a machine learning–based analytical framework leveraging multi-source financial data, incorporating logistic regression, random forest, and support vector machine algorithms. The models are empirically validated on commercial and rural bank datasets from Turkey and Indonesia. Random forest achieves 90% classification accuracy on commercial banks, while all three models consistently identify failure trends among rural banks. Our key contribution lies in relaxing strong parametric assumptions, thereby enhancing model generalizability across heterogeneous bank types and geographically distinct regions. The framework delivers a robust, empirically validated, and scalable technical approach for early systemic risk detection and evidence-based regulatory decision-making.
📝 Abstract
Context: Financial system stability is determined by the condition of the banking system. A bank failure can destroy the stability of the financial system, as banks are subject to systemic risk, affecting not only individual banks but also segments or the entire financial system. Calculating the probability of a bank going bankrupt is one way to ensure the banking system is safe and sound. Existing literature and limitations: Statistical models, such as Altman's Z-Score, are one of the common techniques for developing a bankruptcy prediction model. However, statistical methods rely on rigid and sometimes irrelevant assumptions, which can result in low forecast accuracy. New approaches are necessary. Objective of the research: Bankruptcy models are developed using machine learning techniques, such as logistic regression (LR), random forest (RF), and support vector machines (SVM). According to several studies, machine learning is also more accurate and effective than statistical methods for categorising and forecasting banking risk management. Present Research: The commercial bank data are derived from the annual financial statements of 44 active banks and 21 bankrupt banks in Turkey from 1994 to 2004, and the rural bank data are derived from the quarterly financial reports of 43 active and 43 bankrupt rural banks in Indonesia between 2013 and 2019. Five rural banks in Indonesia have also been selected to demonstrate the feasibility of analysing bank bankruptcy trends. Findings and implications: The results of the research experiments show that RF can forecast data from commercial banks with a 90% accuracy rate. Furthermore, the three machine learning methods proposed accurately predict the likelihood of rural bank bankruptcy. Contribution and Conclusion: The proposed innovative machine learning approach help to implement policies that reduce the costs of bankruptcy.