Machine Learning-Based Econometric Framework for Credit Risk Prediction in Industrial Enterprises

Abstract

Credit risk prediction for industrial enterprises is critical for financial stability and lending decisions. Traditional econometric models, particularly logistic regression, have long been used to estimate default probabilities due to their interpretability and solid statistical foundation. However, modern machine learning techniques like Random Forests (RF) and Gradient Boosting often achieve higher predictive accuracy by capturing complex non-linear patterns, albeit at the cost of interpretability. This paper proposes a theoretical machine learning-based econometric framework that integrates classical logistic regression with advanced tree-based machine learning methods for credit risk modeling. The framework leverages machine learning for feature engineering and selection, generating high-predictive power risk factors that are then incorporated into a logistic regression model. In this way, the hybrid approach aims to “get the best of both worlds” – preserving the interpretability and solid grounding of logistic regression while improving predictive performance with machine learning insights. We present the conceptual model architecture, including a modeling pipeline for data preprocessing, feature generation via Random Forest/Gradient Boosting, and logistic regression integration. The paper includes relevant formulas and schematic diagrams illustrating the integration strategy [Insert Figure 1 here]. We also provide tables categorizing financial risk features and comparing characteristics of the modeling techniques. Through a comprehensive literature review and methodological discussion, we highlight that such hybrid models can outperform standalone models in credit risk prediction while maintaining greater interpretability than “black-box” models. The paper concludes with a discussion on the implications of this integrated framework for practitioners and researchers, its advantages in an industrial enterprise context, and directions for future research.