A multi-stage integrated model based on deep neural network for credit risk assessment with unbalanced data
Abstract
Purpose
With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.
Design/methodology/approach
In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.
Findings
On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.
Originality/value
In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.
Keywords
Acknowledgements
Funding: Lu Wang reports financial support was provided by National Natural Science Foundation of China. Lu Wang, Jiahao Zheng, Yuangao Chen, Jianrong Yao reports a relationship with Zhejiang University of Finance and Economics that includes: employment. Lu Wang, Jiahao Zheng has patent pending to Lu Wang, Jiahao Zheng.
Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Citation
Wang, L., Zheng, J., Yao, J. and Chen, Y. (2024), "A multi-stage integrated model based on deep neural network for credit risk assessment with unbalanced data", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-11-2023-2501
Publisher
:Emerald Publishing Limited
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