Machine Learning-Driven Research Enhances Financial Credit Risk Assessment for China's Manufacturing Sector

New study introduces data-balanced AI framework for industry stress testing and policy evaluation.

New York, NY, United States, April 5, 2025 -- A new academic study is transforming how financial credit risk is assessed in China's manufacturing sector. Authored by Yunpeng Zhao, the research titled "Research on Financial Credit Risk of Manufacturing Enterprises under Heterogeneous Data Based on Machine Learning" introduces a scientifically grounded and data-enhanced framework for evaluating enterprise-level creditworthiness in complex and imbalanced data environments.

Focusing on Chinese manufacturing enterprises, the study integrates Principal Component Analysis (PCA) and K-means clustering to construct a multi-dimensional, quantitative credit scoring model. It systematically grades the financial health of enterprises while evaluating the importance of various credit risk indicators.

To address the common challenge of class imbalance in risk datasets, Zhao's research introduces the SMOTE (Synthetic Minority Oversampling Technique) method, improving the predictive capacity of machine learning models when assessing default risks. After comparative evaluation, the Multilayer Perceptron (MLP) model was selected as the most accurate and stable among several advanced algorithms, making it the centerpiece of a proposed stress-testing framework.

Key Findings:

• Solvency is the most decisive factor in enterprise credit risk, while operational efficiency carries relatively less weight.
• The MLP model demonstrates optimal performance in both accuracy and stability, outperforming other algorithms under incremental stress conditions.
• Subsector analysis reveals stark differences: general equipment manufacturers show strong resilience, whereas special equipment enterprises appear more vulnerable under external pressure.

"By applying machine learning to heterogeneous financial and operational data, we can construct more dynamic and precise models that reflect the real-time risk profile of industrial enterprises,” Zhao notes. "This contributes not only to academic progress, but also to practical financial governance.”  

The findings not only contribute to academic discourse but also provide actionable tools for banking institutions, regulatory bodies, and enterprise risk managers navigating today's uncertain economic environment. To read more about the study, you can visit here.

Contact Info:

Name: Yunpeng Zhao

Email: Send Email

Organization: Yunpeng Zhao

Website: https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=XVyWok4AAAAJ&citation_for_view=XVyWok4AAAAJ:u5HHmVD_uO8C

Release ID: 89156612

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