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A Path-Based Feature Selection Algorithm for Enterprise Credit Risk Evaluation.

Computational intelligence and neuroscience·2022
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A Meta-Path-Based Evaluation Method for Enterprise Credit Risk.

Marui Du1, Yue Ma2, Zuoquan Zhang1

  • 1School of Science, Beijing Jiaotong University, Beijing, China.

Computational Intelligence and Neuroscience
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

Evaluating small and medium-sized enterprises' (SMEs) credit risk is challenging due to limited data. This study introduces a novel meta-path feature using heterogeneous information networks to accurately assess SME financial status and credit risk from secondary data.

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Area of Science:

  • Business and Economics
  • Data Science
  • Network Science

Background:

  • Small and medium-sized enterprises (SMEs) are vital to national economies.
  • Assessing SME credit risk is difficult due to insufficient primary financial data.
  • Secondary data from enterprise relationships offers a potential solution for credit risk evaluation.

Purpose of the Study:

  • To develop an accurate method for evaluating the credit risk of SMEs.
  • To leverage secondary data from enterprise networks to overcome primary data limitations.
  • To propose a novel feature for enhanced credit risk assessment.

Main Methods:

  • Construction of a heterogeneous information network for SMEs.
  • Mining secondary information through enterprise entities and relationships.
  • Proposal and application of a novel 'meta-path feature' for risk measurement.

Main Results:

  • The heterogeneous information network effectively mines secondary SME data.
  • The proposed meta-path feature provides a multi-perspective evaluation of financial status.
  • Experimental results validate the effectiveness of the meta-path feature in identifying credit risks.

Conclusions:

  • The meta-path feature is a powerful tool for assessing SME credit risk.
  • Leveraging enterprise information networks improves the accuracy of SME financial evaluation.
  • This approach addresses the challenge of limited primary data in SME credit risk assessment.