[2509.22381] Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline

Computer Science > Machine Learning arXiv:2509.22381 (cs) [Submitted on 26 Sep 2025 (v1), last revised 27 Mar 2026 (this version, v2)] Title:Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline Authors:Haibo Wang, Jun Huang, Lutfu S. Sua, Figen Balo, Burak Dolar View a PDF of the paper titled Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline, by Haibo Wang and 4 other authors View PDF Abstract: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 t...