Machine learning and financial inclusion: Evidence from credit risk assessment of small-business loans in China

Yang Zhang, Jianxiong Lin, Yihe Qian () and Lianjie Shu
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Yang Zhang: Department of Finance and Business Economics, Faculty of Business Administration / Asia-Pacific Academy of Economics and Management, University of Macau
Jianxiong Lin: QIFU Technology, China
Yihe Qian: Department of Finance and Business Economics, Faculty of Business Administration, University of Macau
Lianjie Shu: Faculty of Business Administration , University of Macau

No 202532, Working Papers from University of Macau, Faculty of Business Administration

Abstract: MachiAs a key enabler of poverty alleviation and equitable growth, financial inclusion aims to expand access to credit and financial services for underserved individuals and small businesses. However, the elevated default risk and data scarcity in inclusive lending present major challenges to traditional credit assessment tools. 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, XGBoost, and 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 machine learning, 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.

Keywords: machine learning; financial inclusion; small business; China; credit risk assessment (search for similar items in EconPapers)
JEL-codes: C53 G21 G32 O16 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2025-06
New Economics Papers: this item is included in nep-fdg and nep-pay
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