Explainable Machine Learning Framework to Predict Corporate Bankruptcy

Abstract

Timely bankruptcy of the corporations is a major issue that investors, financial institutions, and regulatory bodies need to know in order to reduce economic losses and enhance decisions on risk management. Nevertheless, bankruptcy forecasting is difficult because of extreme imbalance in classes and nonlinear correlation between financial data. This paper suggests a machine learning model of corporate bankruptcy prediction, which is explainable and statistically justified through advanced ensemble learning methods. A comparative study was conducted on a financial dataset based on Logistic Regression, Random Forest, XGBoost, LightGBM, Tuned LightGBM, and Stacking Ensemble models with $\mathbf{6, 8 1 9}$ firms and $\mathbf{9 5}$ attributes. In order to solve the problem of data imbalance, threshold optimization was used, and the optimal decision threshold was obtained (0.13). Accuracy, Precision, Recall, F1-score, ROC-AUC, PR- AUC, Matthews Correlation Coefficient, and Brier Score were used to measure model performance. The optimized LightGBM model had a better performance with the following parameters: F1-score of 0.5124, MCC of 0.4999, ROC-AUC of 0.9549 and a Brier Score of 0.0234, which showed high discrimination and good probability calibration. The explainability of the proposed framework with the help of SHAP and the statistical test developed by McNemar additionally confirmed the strength and interpretability of the proposed framework, which is why it can be applied to real-world financial risk assessment.