Implementation Of Credit Risk Prediction System Based On Deep Learning

Credit risk prediction is to judge whether the borrowers can fulfill financial commitments with the assistance of their relevant information.At present,large banks in China have built a relatively complete credit management system,which has significantly improved their business level.However,some small and medium-sized banks still utilize traditional mathematical statistical methods for risk prediction,which can neither meet the growing demand for credit risk prediction business nor ensure the prediction accuracy.Therefore,it is proposed that a credit risk assessment algorithm based on deep neural network to accurately predict credit risk.The main work is as follows:Firstly,it is proposed that a bank credit risk prediction model based on deep neural network to replace the traditional method based on mathematical statistics.In terms of data preprocessing,we mainly use analytic hierarchy process to find out the key variables and send them into neural network for training.Through this algorithm,we can score the credit of each borrower.In order to evaluate the accuracy of the model,we collect the borrower data of a bank and carried out the corresponding simulation experiments.The results show that the model has high accuracy and can help the bank credit salesman to effectively evaluate the repayment ability of the loan applicants.Secondly,a bank credit risk evaluation system based on risk prediction model is proposed.By the research on the demand of the system and the daily credit workflow of the bank,it is designed that the system and database in detail.Thirdly,extensive experiments are conducted on the bank credit risk assessment system.The results show that the system reaches the design requirements and meets the needs of borrowers.In addition,it has friendly man-machine interface,simple operation and strong security.As a result,the system improves the management efficiency,reduces the management cost of credit business and enhances the competitiveness of banks.