Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline
Effective credit risk management is fundamental to financial decision-making, requiring robust models to predict default probabilities and classify financial entities. Traditional machine learning approaches face significant challenges when confronted with high-dimensional data, limited interpretability, rare-event detection, and multi-class risk imbalance. This research proposes a comprehensive multi-stage ensemble pipeline that synthesizes multiple complementary models: econometric models including Ordered logit and ordered probit, supervised learning algorithms, including XGBoost, Random Forest, Support Vector Machine, and Decision Tree; unsupervised methods such as K-Nearest Neighbors; deep learning architectures like Multilayer Perceptron; alongside LASSO regularization for feature selection and dimensionality reduction; and Error-Correcting Output Codes as an Ensemble classifier for handling imbalanced multi-class problems. We implement Permutation Feature Importance analysis for each prediction class across all constituent models to enhance model transparency. Our framework can optimize predictive performance while providing a more holistic approach to credit risk assessment. This research contributes to the development of more accurate and reliable computational models for strategic financial decision support by addressing three fundamental challenges in credit risk modeling. The empirical validation of our approach involves analyzing the Corporate Credit Ratings dataset, which contains credit ratings for 2,029 publicly listed US companies. Results demonstrate that our multi-stage ensemble pipeline significantly enhances the accuracy of financial entity classification regarding credit rating migrations (upgrades and downgrades) and default probability estimation.