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Related Experiment Videos

E-Commerce Credit Risk Assessment Based on Fuzzy Neural Network.

Lina Wang1, Hui Song2

  • 1School of Finacial Technology, Hebei Finance University, Baoding, Hebei 071051, China.

Computational Intelligence and Neuroscience
|January 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel self-organization mechanism for network reconstruction, enhancing fuzzy rule effectiveness and optimizing parameters for SC-IR2FNN. The research identifies key factors influencing online supply chain finance credit risk, improving risk assessment accuracy.

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Area of Science:

  • Network science and artificial intelligence
  • Financial risk management
  • Supply chain finance

Background:

  • Traditional network reconstruction methods often lack dynamic adaptability and efficient parameter optimization.
  • Accurate credit risk assessment is crucial for the stability and growth of online supply chain finance.

Purpose of the Study:

  • To propose a cooperative strategy-based self-organization mechanism for network reconstruction.
  • To develop an efficient parameter optimization strategy for SC-IR2FNN (Self-adaptive Credit Information Risk Fuzzy Neural Network).
  • To identify and analyze key factors influencing credit risk in online supply chain finance.

Main Methods:

  • A comprehensive evaluation algorithm and structure adjustment mechanism for self-organization.
  • A multilayer optimization engine for joint optimization of nonlinear and linear parameters in SC-IR2FNN.
  • Principal component factor analysis and logistic regression to identify significant credit risk factors.
  • An improved credit scoring system for online supply chain finance risk assessment.

Main Results:

  • The proposed self-organization mechanism enables simultaneous network reconstruction and parameter optimization without pre-set thresholds.
  • The multilayer optimization engine reduces computational complexity and improves the learning rate of SC-IR2FNN.
  • Profitability, solvency, core enterprise profitability, operational guarantee, growth ability, supply chain online degree, financing enterprise quality, and cooperation factors are identified as key indicators for credit risk.
  • Logistic model confirms financing company profitability, debt repayment, and core company profitability as significant impact factors.
  • The improved credit scoring system effectively evaluates credit risk and addresses speculative and circular credit fraud.

Conclusions:

  • The cooperative strategy-based self-organization mechanism offers an effective approach for adaptive network reconstruction.
  • The developed parameter optimization strategy enhances the performance and efficiency of SC-IR2FNN.
  • The study provides a robust framework for credit risk assessment in online supply chain finance, contributing to more secure financial ecosystems.