Articlehybrid2022

Multimodel Integrated Enterprise Credit Evaluation Method Based on Attention Mechanism

16 citations0/0 files storedComputational Intelligence and Neuroscience

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Financial Distress and Bankruptcy Prediction Imbalanced Data Classification Techniques Evaluation and Optimization Models

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.

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References

2019Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach 2018Credit Risk Analysis Using Machine and Deep Learning Models 2020A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique 2019A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment 2018A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM 2020A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees 2018Enterprise credit risk evaluation based on neural network algorithm 2021A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment

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