Business School of Zhejiang Wanli College, Ningbo 315101, China.
Business School, Jiyang College of Zhejiang A&F University, Shaoxing 311800, China.
Comput Intell Neurosci. 2022 May 2;2022:8791968. doi: 10.1155/2022/8791968. eCollection 2022.
With the rapid development of entrepreneurship loans in China, the construction of a credit evaluation system of risk loans has become an important financial safeguard measure. This paper mainly studies the following three aspects. Firstly, in view of the subjective factors in the approval process of venture loans, based on the credit evaluation system of commercial banks and the data characteristics of venture loans, a credit evaluation system based on venture loans is constructed. Secondly, the randomized uniform design method is used to improve the population initialization method to realize the uniform distribution of the individual population. Finally, aiming at the problem of low efficiency of venture loan audit, this paper proposes an optimized BP neural network to evaluate the venture loan. Especially, through data processing, a credit index system is constructed, and then the optimized BP neural network model is determined in parameters. The model contains 15 input nodes, 1 hidden layer, and 2 output layers. Finally, the simulation shows that the optimized BP neural network model has obvious advantages in the loan evaluation. This paper includes the development status of credit evaluation of venture loans is empirically studied by using an optimized BP neural network model of nonexpected output.
随着我国创业贷款的快速发展，构建风险贷款信用评价体系已成为重要的金融保障措施。本文主要研究以下三个方面。首先，针对创业贷款审批过程中的主观因素，基于商业银行信用评价体系和创业贷款数据特点，构建创业贷款信用评价体系。其次，采用随机均匀设计方法改进种群初始化方法，实现个体种群的均匀分布。最后，针对创业贷款审计效率低的问题，提出了一种优化的 BP 神经网络对创业贷款进行评估。特别是，通过数据处理，构建了信用指标体系，然后确定了参数优化的 BP 神经网络模型。该模型包含 15 个输入节点、1 个隐藏层和 2 个输出层。最后，模拟结果表明，优化后的 BP 神经网络模型在贷款评估方面具有明显优势。本文通过使用非期望输出的优化 BP 神经网络模型对创业贷款信用评价的发展状况进行了实证研究。
