Wang Xiaogang
College of Management, Henan University of Technology, Zhengzhou, Henan 450052, China.
Comput Intell Neurosci. 2022 Mar 10;2022:2724842. doi: 10.1155/2022/2724842. eCollection 2022.
Commercial banks are of great value to social and economic development. Therefore, how to accurately evaluate their credit risk and establish a credit risk prevention system has important theoretical and practical significance. This paper combines BP neural network with a mutation genetic algorithm, focuses on the credit risk assessment of commercial banks, applies neural network as the main modeling tool of the credit risk assessment of commercial banks, and uses the mutation genetic algorithm to optimize the main parameter combination of neural network, so as to give better play to the efficiency of neural network. After verification of various evaluation models, the accuracy of the evaluation model designed in this paper is more than 65%, while the acceptability of the evaluation results optimized by the mutation genetic algorithm is more than 85%. Compared with the accuracy of about 50% of the traditional credit scoring method, the accuracy of the credit risk evaluation using neural network technology is improved by more than 10%. It is proved that the performance of the optimized algorithm is better than that of the traditional neural network algorithm. It has important theoretical and practical significance for the establishment of the credit risk prevention system of commercial banks.
商业银行对社会经济发展具有重要价值。因此，如何准确评估其信用风险并建立信用风险防范体系具有重要的理论和现实意义。本文将BP神经网络与变异遗传算法相结合，聚焦于商业银行的信用风险评估，将神经网络作为商业银行信用风险评估的主要建模工具，并利用变异遗传算法优化神经网络的主要参数组合，以更好地发挥神经网络的效能。经过对各种评估模型的验证，本文设计的评估模型准确率超过65%，而经变异遗传算法优化后的评估结果可接受度超过85%。与传统信用评分方法约50%的准确率相比，采用神经网络技术进行信用风险评估的准确率提高了10%以上。证明了优化算法的性能优于传统神经网络算法。这对商业银行信用风险防范体系的建立具有重要的理论和现实意义。
