Author(s)

Yu Cheng ¹, Qin Yang ², Liyang Wang ³, Ao Xiang ⁴, Jingyu Zhang ⁵

Affiliation(s)

¹ The Fu Foundation School of Engineering and Applied Science, Columbia University, Operations Research, New York, NY, USA
² School of Integrated Circuit Science and Engineering (Exemplary School of Microelectronics), University of Electronic Science and Technology of China, Microelectronics Science and Engineering, Chengdu, Sichuan, China
³ Washington University in St. Louis, Olin Business School, Finance, St. Louis, MO, USA
⁴ School of Computer Science & Engineering (School of Cybersecurity), University of Electronic Science and Technology of China, Digital Media Technology, Chengdu, Sichuan, China
⁵ The Division of the Physical Sciences, Analytics, The University of Chicago, Chicago, IL, USA

Corresponding Author

Yu Cheng

ABSTRACT

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management.

KEYWORDS

Neural network algorithms; commercial banks; credit risk; early warning models

CITE THIS PAPER

Yu Cheng, Qin Yang, Liyang Wang, Ao Xiang, Jingyu Zhang, Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm. Financial Engineering and Risk Management (2024) Vol. 7: 11-19. DOI: http://dx.doi.org/10.23977/ferm.2024.070402.

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