Current Science Research Bulletin

Bank liquidation is a process of resolving the obligations of banks whose licenses have been revoked and plays a crucial role in maintaining financial system stability. The duration of Rural Bank liquidation varies depending on financial conditions and asset recovery performance, requiring robust analytical methods for accurate classification. This study aims to classify the liquidation duration of Rural Banks using the Random Forest algorithm with class imbalance handling through the Synthetic Minority Oversampling Technique (SMOTE) and hyperparameter optimization using GridSearchCV. The dataset consists of secondary data of Rural Bank liquidations handled by the Indonesia Deposit Insurance Corporation  during the period 2006–2024, comprising 120 observations and 22 financial variables. Data preprocessing includes median imputation for missing values, winsorizing for outlier treatment, and data standardization. The results indicate that the best-performing model is Random Forest with SMOTE and hyperparameter tuning, achieving an accuracy of 83.3%, precision of 75.0%, recall of 93.8%, and F1-score of 83.3% for the problematic class. Feature importance and SHapley Additive exPlanations (SHAP) analysis reveal that Asset Liquidation Estimate, Total Gross Assets, and Guarantee Claim Value are the most influential variables. These findings provide important implications for data driven decision making in bank resolution policies.

Liquidation Duration, Random Forest, SMOTE, Hyperparameter Tuning, Feature Importance, SHAP.

1. Azel F. Azmi, & Apriade Voutama. (2024). Prediksi churn nasabah bank menggunakan klasifikasi Random Forest dan Decision Tree dengan evaluasi confusion matrix. Komputa: Jurnal Ilmiah Komputer dan Informatika, 13(1).
2. Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417.
3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
4. Brown, C. O., & Dinc, I. S. (2011). Too many to fail? Evidence of regulatory forbearance when the banking sector is weak. Review of Financial Studies, 24(4), 1378–1405.
5. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
6. DeYoung, R., & Torna, G. (2013). Nontraditional banking activities and bank failures during the financial crisis. Journal of Financial Intermediation, 22(3), 397–421.
7. Fisher, A., Rudin, C., & Dominici, F. (2019). All models are wrong but many are useful: Variable importance for black-box models. Journal of Machine Learning Research, 20(177), 1–81.
8. Granja, J., Matvos, G., & Seru, A. (2017). Selling failed banks. Journal of Finance, 72(4), 1723–1784.
9. Hadad, M. D., Santoso, W., Mardanugraha, E., & Ilyas, D. (2011). Analisis risiko dan kinerja Bank Perkreditan Rakyat di Indonesia. Jakarta: Bank Indonesia.
10. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
11. He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
12. Husna Afanyn Khoirunissa, Amanda Rizky Widyaningrum, & Annisa Priliya Ayu Maharani. (2021). Comparison of Random Forest, Logistic Regression, and Multilayer Perceptron Methods on Classification of Bank Customer Account Closure. Indonesian Journal of Applied Statistics, 4(1).
13. Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.
14. Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of classification algorithms for financial risk prediction. Knowledge-Based Systems, 54, 243–256.
15. Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38–48.
16. Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer.
17. Laeven, L., & Valencia, F. (2018). Systemic banking crises revisited (IMF Working Paper No. 18/206). International Monetary Fund.
18. Lembaga Penjamin Simpanan. (2024). Laporan kelembagaan Lembaga Penjamin Simpanan Triwulan IV Tahun 2024. Jakarta: Lembaga Penjamin Simpanan.
19. Lembaga Penjamin Simpanan. (2024). Laporan tahunan Lembaga Penjamin Simpanan tahun 2024. Jakarta: Lembaga Penjamin Simpanan.
20. Lembaga Penjamin Simpanan. (2024). Peraturan Lembaga Penjamin Simpanan Nomor 2 Tahun 2024 tentang rencana resolusi bagi bank umum. Jakarta: Lembaga Penjamin Simpanan.
21. Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring. European Journal of Operational Research, 247(1), 124–136.
22. Philipp Probst, Marvin N. Wright, & Anne-Laure Boulesteix. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery, 9(3), e1301. 
23. Zhang, J., Li, X., & Wang, Y. (2024). Impact of an improved random forest-based financial risk model on corporate decision-making. Decision Analytics Journal, 12, 100–118.