Ensemble Machine Learning Approaches to Identify the Drivers of Non-Performing Loans

Abstract

This study investigates the drivers of borrower default by leveraging Ensemble machine learning algorithms trained on personal credit datasets. We aim to develop an interpretable and high-precision detection system capable of deciphering the root causes of bad debt at the point of application. Through a rigorous analysis of salient financial metrics influencing creditworthiness, the model's performance is validated using real-world data to guarantee its industrial applicability. Our results indicate that Ensemble algorithm approaches substantially improve the accuracy of repayment capacity evaluations, empowering financial institutions to optimize credit workflows and reduce operational exposure. Additionally, the model drives cost-efficiency by streamlining redundant processes and drastically shortening the loan processing cycle. This work offers significant theoretical insights into AI-driven risk governance and provides actionable strategies for optimizing credit management in volatile market environments