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A Convolutional Neural Network-Based Model for Supply Chain Financial Risk Early Warning
Li-Li Yin1, Yi-Wen Qin2, Yuan Hou1
1Beijing Technology and Business University, Beijing, China.
Computational Intelligence and Neuroscience
|April 25, 2022
Summary
This study develops a supply chain finance risk early warning model for China's trade sector. It helps small and medium enterprises manage credit risks effectively in supply chain finance.
Area of Science:
- Economics
- Finance
- Data Science
Background:
- China's trade circulation industry faces significant financing challenges.
- Supply chain finance offers solutions for SMEs but introduces credit risks.
Purpose of the Study:
- To analyze causes and measure risks in trade circulation supply chain finance.
- To establish a credit risk assessment system for early warning.
Main Methods:
- Developed a supply chain financial risk early warning index system (4 first-level, 29 third-level indicators).
- Constructed a risk early warning model using convolution neural networks.
- Applied principal component analysis for evaluating risk indicators.
Main Results:
- Empirical analysis using trade circulation enterprise data validated the model.
- The model demonstrated effectiveness in identifying and measuring supply chain financial risks.
Conclusions:
- The proposed model and risk control measures offer valuable guidance for the commercial circulation industry.
- Provides effective strategies for trade circulation enterprises to manage supply chain financial risks.
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