A Study on Credit Risk Analysis Model of Commercial Bank Users Based on Neural Network Optimization Algorithm

With the rapid expansion and development of credit business, commercial banks are facing increasingly serious credit risks. In order to manage these risks effectively, it is necessary to establish an effective credit risk early warning model. This paper first reviews the definition and importance of credit risk early warning models, and then elaborates on the main causes of personal credit risk in commercial banks and the main problems of current personal credit risk management. Through an in-depth study of related literature, we establish a set of credit risk early warning index system and based on it, we carry out the establishment of credit risk early warning model. In this paper, we propose a credit risk early warning model based on support vector machine (SVM) and neural network optimization algorithm, and analyze the application effect of three neural network algorithms (back propagation neural network, convolutional neural network and long and short-term memory network) in credit risk early warning by comparing experiments. The experimental results show that the credit risk early warning model based on neural network optimization algorithm has high prediction accuracy and can provide powerful decision support for credit risk management of commercial banks.