Early Detection of Financial Distress in Islamic Banking Using Explainable Artificial Intelligence
DOI:
https://doi.org/10.24235/jiesbi.v3i2.577Keywords:
Explainable AI Early, Financial Distress, Islamic Banking, Machine Learning, Warning SystemAbstract
This paper examines the effectiveness of an explainable machine learning–based Early Warning System (EWS) in detecting financial distress in Islamic banking. Following recurrent episodes of financial instability, regulators and bank managers increasingly require predictive tools that are not only accurate but also interpretable. This study aims to develop a transparent and reliable distress prediction framework for Indonesian Islamic banks. Conventional early warning models can signal which banks are at risk but fail to explain why distress occurs at the variable level, limiting their usefulness for supervisory intervention. This study introduces an integrated Explainable Artificial Intelligence (XAI) framework that combines ensemble tree-based machine learning with SHAP and LIME to uncover non-linear threshold effects in the risk–stability relationship an aspect largely overlooked in prior Islamic banking studies. Using panel data from Indonesian Islamic banks over the period 2014–2024, financial distress is modeled as a binary outcome based on asset quality and profitability thresholds. Predictive performance is evaluated using Random Forest algorithms and benchmarked against conventional logit models, while interpretability is achieved through SHAP and LIME analyses. The results show perfect predictive separation (AUC = 1.00), with operational inefficiency (BOPO), profitability (ROA), and asset quality (NPF Gross) emerging as dominant risk drivers, exhibiting clear non-linear threshold effects. The study concludes that explainable machine learning significantly enhances both predictive accuracy and supervisory interpretability, offering a robust and policy-relevant tool for early intervention in Islamic banking stability.
References
Abasimel, N. A. (2022). Islamic banking and economics: Concepts and instruments, features, advantages, differences from conventional banks, and contributions to economic growth. Journal of the Knowledge Economy, 14, 1923–1950.https://doi.org/10.1007/s13132-022-00940-z
Aljunaid, S. K., Almheiri, S. J., Dawood, H., & Khan, M. A. (2025). Secure and transparent banking: Explainable AI-driven federated learning model for financial fraud detection. Journal of Risk and Financial Management, 18(4). https://doi.org/10.3390/jrfm18040179
Alshareefi, A. J. A., & Alshamari, H. U. (2024). The early warning of financial failure for Iraqi banks based on robust adaptive lasso logistic regression. Iraqi Journal for Computer Science and Mathematics, 5(1). https://doi.org/10.52866/ijcsm.2024.05.01.008
Amirshahi, B., & Lahmiri, S. (2024). Bankruptcy prediction using optimal ensemble models under balanced and imbalanced data. Expert Systems, 41. https://doi.org/10.1111/exsy.13599
Arsenault, P.-D., Wang, S., & Patenaude, J.-M. (2024). A survey of explainable artificial intelligence (XAI) in financial time series forecasting. ACM Computing Surveys, 57, 1–37. https://doi.org/10.1145/3729531
Asutay, M., & Othman, J. (2020). Alternative measures for predicting financial distress in the case of Malaysian Islamic banks: Assessing the impact of global financial crisis. Journal of Islamic Accounting and Business Research, 11, 1827–1845. https://doi.org/10.1108/jiabr-12-2019-0223
Awosika, T., Shukla, R., & Pranggono, B. (2023). Transparency and privacy: The role of explainable AI and federated learning in financial fraud detection. IEEE Access, 12, 64551–64560. https://doi.org/10.1109/access.2024.3394528
Barra, C., & Zotti, R. (2021). Financial stability and local economic development: The experience of Italian labour market areas. Empirical Economics, 62, 1951–1979. https://doi.org/10.1007/s00181-021-02071-x
Bouheni, F. Ben, Obeid, H., & Margarint, E. (2021). Nonperforming loan of European Islamic banks over the economic cycle. Annals of Operations Research, 313, 773–808. https://doi.org/10.1007/s10479-021-04038-8
Butt, U., & Chamberlain, T. (2025). Performance of Islamic banks during the COVID-19 pandemic: An empirical analysis and comparison with conventional banking. Journal of Risk and Financial Management, 18(6). https://doi.org/10.3390/jrfm18060308
Carmona, P., Dwekat, A., & Mardawi, Z. (2022). No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure. Research in International Business and Finance. https://doi.org/10.1016/j.ribaf.2022.101649
Černevičienė, J., & Kabašinskas, A. (2024). Explainable artificial intelligence (XAI) in finance: A systematic literature review. Artificial Intelligence Review, 57. https://doi.org/10.1007/s10462-024-10854-8
Chen, H., Wang, T., & Yao, D. (2021). Financial network and systemic risk—A dynamic model. Production and Operations Management, 30, 2441–2466. https://doi.org/10.1111/poms.13384
De Lange, P., Melsom, B., Vennerød, C. B., & Westgaard, S. (2022). Explainable AI for credit assessment in banks. Journal of Risk and Financial Management, 15(12). https://doi.org/10.3390/jrfm15120556
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Rana, O., Patel, P., Qian, B., Wen, Z., Shah, T., Morgan, G., & Ranjan, R. (2022). Explainable AI (XAI): Core ideas, techniques, and solutions. ACM Computing Surveys, 55, 1–33. https://doi.org/10.1145/3561048
Faizulayev, A. (2025). Empirical examination of ESG and fintech factors on financial sustainability: A comparative study of Islamic vs conventional banks in Islamic finance-oriented countries. Asian Journal of Accounting Research. https://doi.org/10.1108/ajar-09-2024-0382
Filippopoulou, C., Galariotis, E., & Spyrou, S. (2020). An early warning system for predicting systemic banking crises in the Eurozone: A logit regression approach. Journal of Economic Behavior & Organization. https://doi.org/10.1016/j.jebo.2019.12.023
Gafsi, N. (2025). Machine learning approaches to credit risk: Comparative evidence from participation and conventional banks in the UK. Journal of Risk and Financial Management, 18(7). https://doi.org/10.3390/jrfm18070345
Gao, R., Cui, S., Wang, Y., & Xu, W. (2025). Predicting financial distress in high-dimensional imbalanced datasets: A multi-heterogeneous self-paced ensemble learning framework. Financial Innovation, 11. https://doi.org/10.1186/s40854-024-00745-w
Ghosh, S. (2025). A novel framework for financial cybersecurity and fraud detection using XAI-RNN-SGRU. IEEE Access, 13, 88134–88155. https://doi.org/10.1109/access.2025.3570216
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2023). Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, 16, 45–74. https://doi.org/10.1007/s12559-023-10179-8
Ijaz, S., Hassan, A., Tarazi, A., & Fraz, A. (2020). Linking bank competition, financial stability, and economic growth. Journal of Business Economics and Management. https://doi.org/10.3846/jbem.2020.11761
Kovalenko, Y., & Petruk, A. (2025). Early response system in ensuring financial stability of banks when carrying out transactions with derivative financial instruments. Social Development: Economic and Legal Issues. https://doi.org/10.70651/3083-6018/2025.5.04
Leelavathi, C., Malathi, K., Nithya, C., Gowd, A. B., & K.Gajendra. (2025). Federated learning and explainable AI: A transparent and secure approach to fraud detection. Power System Technology. https://doi.org/10.52783/pst.1645
Liu, J., Li, C., Peng, O., Liu, J., & Wu, C. (2022). Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach. Journal of Forecasting. https://doi.org/10.1002/for.2931
Liu, S. (2025). LTR-Net: A deep learning-based approach for financial data prediction and risk evaluation in enterprises. PLOS One, 20. https://doi.org/10.1371/journal.pone.0328013
Majid, N. H. A., Yusof, R. M., Wahab, N. A., Rahim, M. A. A. A., Wiranatakusuma, D., & Tasman, S. M. (2025). Understanding resilience of Islamic banking amid COVID-19: A systematic literature review of banking, macroeconomic, and risk factors. Journal of Islamic Accounting and Business Research. https://doi.org/10.1108/jiabr-10-2023-0356
Martins, T., De Almeida, A. M., Cardoso, E., & Nunes, L. (2024). Explainable artificial intelligence (XAI): A systematic literature review on taxonomies and applications in finance. IEEE Access, 12, 618–629. https://doi.org/10.1109/access.2023.3347028
Mehreen, M., Marimuthu, M., Karim, S. A. A., & Jan, A. (2020). Proposing a multidimensional bankruptcy prediction model: An approach for sustainable Islamic banking. Sustainability. https://doi.org/10.3390/su12083226
Mohammad, S., Asutay, M., Dixon, R., & Platonova, E. (2020). Liquidity risk exposure and its determinants in the banking sector: A comparative analysis between Islamic, conventional and hybrid banks. Journal of International Financial Markets, Institutions and Money. https://doi.org/10.1016/j.intfin.2020.101196
Nallakaruppan, M., Chaturvedi, H., Grover, V., Balusamy, B., Jaraut, P., Bahadur, J., Meena, V., & Hameed, I. (2024). Credit risk assessment and financial decision support using explainable artificial intelligence. Risks. https://doi.org/10.3390/risks12100164
Oino, I. (2021). Bank solvency: The role of credit and liquidity risks, regulatory capital and economic stability. Banks and Bank Systems. https://doi.org/10.21511/bbs.16(4).2021.08
Paltrinieri, A., Dreassi, A., Rossi, S., & Khan, A. (2020). Risk-adjusted profitability and stability of Islamic and conventional banks: Does revenue diversification matter?. Global Finance Journal. https://doi.org/10.1016/j.gfj.2020.100517
Purnell, D., Etemadi, A., & Kamp, J. (2024). Developing an early warning system for financial networks: An explainable machine learning approach. Entropy, 26. https://doi.org/10.3390/e26090796
Qian, H., Wang, B., Yuan, M., Gao, S., & Song, Y. (2021). Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree. Expert Syst. Appl., 190, 116202. https://doi.org/10.1016/j.eswa.2021.116202
Saeed, W., & Omlin, C. (2021). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. arXiv. https://doi.org/10.1016/j.knosys.2023.110273
Safdar, R., Fatima, A., & Sajid, M. (2024). Efficiency, liquidity risk and asset quality: A comparative analysis of Islamic and conventional banks in Pakistan. Journal of Islamic Accounting and Business Research. https://doi.org/10.1108/jiabr-03-2023-0079
Sun, B., & Liu, Y. (2025). A SEIQRS Model for Interbank Financial Risk Contagion and Rescue Strategies in Complex Networks. Mathematics. https://doi.org/10.3390/math13193059
Sun, T. (2025). Systemic risk of systemically important financial institutions in the post‐2008 global financial crisis era: A tail risk network analysis. Journal of Risk and Insurance. https://doi.org/10.1111/jori.70023
Ullah, S., Ullah, A., & Zaman, M. (2024). Nexus of governance, macroeconomic conditions, and financial stability of banks: a comparison of developed and emerging countries. Financial Innovation, 10. https://doi.org/10.1186/s40854-023-00542-x
Wang, T., Zhao, S., Zhu, G., & Zheng, H. (2021). A machine learning-based early warning system for systemic banking crises. Applied Economics, 53, 2974–2992. https://doi.org/10.1080/00036846.2020.1870657
Weber, P., Carl, K., & Hinz, O. (2023). Applications of explainable artificial intelligence in finance—A systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 74, 867–907. https://doi.org/10.1007/s11301-023-00320-0
Yin, S. (2025). Machine learning algorithms for early warning systems: Predicting systemic financial crises through non-linear econometric models. Computer Fraud and Security. https://doi.org/10.52710/cfs.613
Zhang, Z., Wu, C., Qu, S., & Chen, X. (2022). An explainable artificial intelligence approach for financial distress prediction. Information Processing & Management, 59(4), 102988.https://doi.org/https://doi.org/10.1016/j.ipm.2022.102988
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Iman Supriadi, Eva Wany, Nico Irawan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
