Application of Neural Network in Risk Management


Junyu Chen, Lebin Huang*

Sydney Smart Technology College, Northeastern University, Shenyang, Liaoning, China.

*Corresponding author: Lebin Huang

Published: April 10,2023


In recent years, with the intensification of project competition and the rapid increase of various risks, all the major companies are facing unprecedented challenges. The emergence of this phenomenon makes people pay more attention to the engineering risk management, so the project risk management system comes into being under this background. The key of risk management is to analyze various risk factors that may occur in the project. Firstly, quantify each risk factor and adopt different quantitative methods for different risk factors; Then, using the risk information in the historical project, the neural network is trained to establish a complete network model; Finally, BP algorithm is used to train the weight of the neural network. Through the verification of the proposed neural network, the effectiveness of project risk management using neural network is verified. On the basis of risk management, according to the improved risk identification method of risk identification, risk identification results input to neural network project risk analysis, then use the optimal neural network algorithm solution, finally the risk level and countermeasures displayed on the system page, to reduce the project risk, improve the success rate of the project, make risk management more accurate, standardized, scientific and convenient.


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How to cite this paper

Application of Neural Network in Risk Management

How to cite this paper: Junyu Chen, Lebin Huang. (2023) Application of Neural Network in Risk Management. Advances in Computer and Communication4(1), 61-64.