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Multimodel Integrated Enterprise Credit Evaluation Method Based on Attention Mechanism.

Lei Zhang1,2, Qiankun Song2

  • 1School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China.

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Summary

This study introduces a novel NN-ATT-Bayesian-Stacking model to improve credit risk assessment for small and medium-sized enterprises (SMEs). The model offers a reliable solution for SME financing and loan evaluations, enhancing operational development.

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Area of Science:

  • Financial Technology
  • Machine Learning in Finance
  • Credit Risk Management

Background:

  • Small and medium-sized enterprises (SMEs) face significant challenges in obtaining financing and loans due to difficulties in credit risk assessment.
  • Existing methods hinder SME operations and development.

Purpose of the Study:

  • To develop a robust and reliable model for assessing credit risk in SMEs.
  • To improve the accuracy and efficiency of financing and loan assessments for SMEs.

Main Methods:

  • Feature selection using correlation coefficient and Gradient Boosting Decision Tree (GBDT).
  • Integration of an attention mechanism (SE-Block) into feature tensors.
  • Training XGBoost and LightGBM models on data subsets.
  • Fusion of model results using Bayesian ridge regression.

Main Results:

  • The proposed NN-ATT-Bayesian-Stacking model achieved an AUC value of 0.9675 in simulation experiments.
  • The model demonstrated ideal prediction result distribution and good robustness.
  • The model provides a reliable assessment for SME financing and loans.

Conclusions:

  • The developed model significantly enhances credit risk assessment for SMEs.
  • This approach offers a reliable framework for supporting SME financing and loan accessibility.
  • Improved credit risk assessment can foster the growth and development of SMEs.