Multimodel Integrated Enterprise Credit Evaluation Method Based on Attention Mechanism

Author:

Zhang Lei12ORCID,Song Qiankun2

Affiliation:

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

2. School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China

Abstract

Due to the difficulty of credit risk assessment, the current financing and loan difficulties of small- and medium-sized enterprises (SMEs) are particularly prominent, which hinders the operation and development of enterprises. Based on the previous researches, this paper first screens out features by correlation coefficient method and gradient boosting decision tree (GBDT). Then, with the help of SE-Block, the attention mechanism is added to the feature tensor of the subset separated from metadata. On this foundation, two models, XGBoost and LightGBM, are used to train four subsets, respectively, and Bayesian ridge regression is used to fuse the training results of single models under different subsets. In the simulation experiment, the AUC value of the NN-ATT-Bayesian-Stacking model reaches 0.9675 and the distribution of prediction results is ideal. The model shows good robustness, which could make a reliable assessment for the financing and loans of SMEs.

Funder

Group Building Scientific Innovation Project for Universities in Chongqing

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Reference22 articles.

1. By the end of 2018 the number of Smes in China has exceeded 30 million;Ministry of Industry and Information Technology

2. A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees;G. Parisa;The North American Journal of Economics and Finance,2020

3. 2-stage modified random forest model for credit risk assessment of P2P network lending to “Three Rurals” borrowers

4. A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment

5. A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique

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