Research On Credit Risk Prediction Model Of Small And Medium-sized Enterprises Based On Convolutional Neural Network

The development of SMEs has played a key role in promoting China's economic growth.However,despite the strong support of the government,there are still a series of problems in the development of SMEs.Among them,financing difficulties are one of the main problems that limit the development of SMEs in China.The financing difficulties of SMEs stem from the lack of credit evaluation methods for SMEs by financial institutions and banks,which led financial institutions to adopt conservative measures such as less loans and refusal to lend due to the inability to grasp the credit risk status of SMEs.Therefore,the use of scientific methods to objectively and accurately evaluate the credit risk situation of SMEs is of great significance to the development of SMEs and even the sustained and steady growth of China's national economy.Firstly,based on the mathematical analysis of domestic and foreign enterprise credit risk assessment models,this paper compares and analyzes the applicability of different types of evaluation models,and finds that the existing models are subject to the objective conditions that SME credit risk assessment data is difficult to obtain.The applicability is limited,and the convolutional neural network method which can adapt and self-learn according to the data situation is more suitable for the credit risk prediction of SMEs in China.Subsequently,based on the basic principles of index system construction and the characteristics of enterprise operation data of SMEs,this paper constructs a credit risk evaluation index system,which includes both financial indicators and non-financial indicators,so compared with traditional methods.It can reflect the credit risk status of SMEs more comprehensively and comprehensively,and use statistical methods to analyze and screen the above indicators to determine the input variables of the neural network method.Afterwards,in order to overcome the shortcomings of traditional credit evaluation methods in data adaptability,this paper selects GoogleNet neural network method to construct credit risk assessment model.This paper introduces the process of network design and training enhancement based on GoogleNet neural network structure,and explains how to apply preprocessing and Dropout regularization to solve many problems in the process of neural network operation.Finally,based on the theoretical and empirical analysis methods,based on the characteristics of SME credit risk data,the TensorFlow deep learning framework is used,programming is performed in Python language,relevant qualitative and quantitative data are input,and sample companies are obtained through network adaptive learning.Credit risk status.In order to verify the accuracy of the model to distinguish between normal companies and risk companies,this paper compares the results with several typical risk prediction models,and verifies the theoretical significance and practical value of the model.The contribution of this paper is to compare and analyze the credit risk assessment models of different types of SMEs,and optimize the index system.The convolutional neural network method is used to construct a credit risk prediction model with data characteristics.The operational data and the actual risk situation data are verified to prove that it has strong adaptive ability to face different types of data,strong operability and high prediction accuracy.This study has made a useful exploration on how to conduct credit risk assessment for SMEs.It gives a set of quantitative analysis methods and enriches the research field of SME credit risk prediction,which has considerable theoretical and time significance.