MACHINE LEARNING AND FINANCIAL INCLUSION: EVIDENCE FROM CREDIT RISK ASSESSMENT OF SMALL-BUSINESS LOANS IN CHINA

This study evaluates whether Machine Learning (ML) techniques can improve default prediction for small-business loans, thereby enhancing the effectiveness and fairness of credit allocation. Using proprietary loan-level data from a city commercial bank in China, we compare eight classification models — Logistic Regression, Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) — under three sampling strategies to address class imbalance. Our findings reveal that undersampling significantly enhances model performance, and tree-based ML models, particularly XGBoost and Decision Tree, outperform traditional classifiers. Feature importance and misclassification analyses suggest that documentation completeness, demographic traits and credit utilization are critical predictors of default. By combining robust empirical validation with model interpretability, this study contributes to the growing literature at the intersection of ML, credit risk and financial development. Our findings offer actionable insights for policymakers, financial institutions and data scientists working to build fairer and more effective credit systems in emerging markets.