Machine Learning-Based Credit Risk Early Warning System for Small and Medium-Sized Financial Institutions: An Ensemble Learning Approach with Interpretable Risk Indicators

Abstract

Small and medium-sized financial institutions encounter distinct challenges in implementing effective credit risk management systems due to limited resources and technological infrastructure. This study develops an ensemble learning framework tailored for early warning detection in credit portfolios, addressing the urgent need for cost-efficient risk assessment solutions. The proposed methodology combines multiple machine learning algorithms through a hierarchical voting mechanism, effectively handling imbalanced financial datasets with specialized resampling techniques. Experimental validation using real-world credit data from regional banks demonstrates strong performance, achieving 87.3% accuracy in default prediction over a 12-month forecast horizon. The framework also incorporates interpretable feature importance analysis, enabling risk managers to identify key indicators of portfolio deterioration, including debt-to-equity ratios, cash flow volatility patterns, and industry-specific economic signals. Implementation analysis indicates potential cost reductions of 34% compared to traditional risk assessment methods while maintaining compliance with regulatory standards. Furthermore, the system's modular architecture supports incremental deployment, allowing institutions to adopt machine learning capabilities without extensive infrastructure overhaul.