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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
PubMed
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.

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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.