摘要
针对互联网金融行业信用风险评估这一问题,本文应用RBF神经网络,以互联网金融行业数据为算例,通过分析关于网贷平台的统计数据,对平台进行了信用风险评估,提出了RBF神经网络模型预警新方法,弥补了传统预警方法中所缺乏的客观性与全面性造成预测结果不准确的不足,同时与BP神经网络算法对比,优化信用风险模型,合理预测行业发展信用风险状况,为借贷人提供参考。通过构建RBF神经网络预警模型,从新的视角给出信用风险预测模型,最后通过实例对比分析说明了该方法的有效性。
Aiming at the problem of credit risk assessment in Internet finance industry, this paper applies RBF neural network, takes the data of Internet finance industry as an example, analyzes the statis-tical data of online lending platform, evaluates the credit risk of the platform, and puts forward a new method of RBF neural network model early warning, which makes up for the lack of objectivity and comprehensiveness in traditional early warning methods. At the same time, compared with BP neural network algorithm, it optimizes the credit risk model, reasonably predicts the credit risk situation of the industry development, and provides reference for borrowers. Through the construction of RBF neural network early warning model, the credit risk prediction model is given from a new perspective. Finally, the effectiveness of the method is illustrated through the compar-ative analysis of an example.
出处
《建模与仿真》
2021年第2期257-267,共11页
Modeling and Simulation
