Design And Implementation Of Risk Management System For Retail Loan Business Of Commercial Banks

As an important financial institution,banks are of great significance to the stability and good development of the national economy.Retail loan business can bring considerable profits to banks,but the large amount of total loans is also accompanied by huge default risk.Therefore,it is very important to effectively evaluate and predict its default risk.The traditional risk assessment and prediction methods of retail loan business generally rely too much on labor,which is not only time-consuming and laborious,but also inevitably flawed.At the same time,the rapid development,popularization and application of machine learning can achieve the purpose of people’s needs by finding the laws between massive data,which can not only reduce manpower,but also improve efficiency.Therefore,the rapid development of computer technology to manage bank loan risk can better meet the needs of today’s commercial banks for retail loan risk assessment and prediction.According to the above background,this paper designs and implements the risk management system of retail loan business of commercial banks under the guidance of the scientific theory of software engineering.By investigating the risk field of retail loan business of a commercial bank,a more comprehensive demand analysis is carried out,and the functional and non functional requirements are sorted out.Based on the demand analysis,the technical architecture,network architecture,functional modules and database of the system are designed.The system is built based on Java EE system,uses B / S mode to design application end and client,uses spring framework and struts and hibernate to build SSH three-tier architecture,Tomcat processes data and instructions,and uses Fusion Charts for graphics development.The system is divided into seven functional modules,namely customer management module,credit management module,loan management module,risk management module,contract management module and system management module,covering the whole process management of default risk from pre loan,in loan to post loan.In order to realize the core risk prediction and evaluation function in the risk management system,this paper designs and develops the risk evaluation model,and makes a comparative experiment on the logistic regression algorithm,random forest algorithm and XGBoost algorithm.Based on the experimental results,it is proved that the evaluation and prediction effect of XGBoost is better,and the evaluation and prediction effect is optimized through the stacking model.Finally,the system is coded and realized.The system designed and implemented in this paper realizes the management requirements of commercial banks for the risk of retail loan business,and can accurately identify the possible default risk,so as to assist the risk managers to deal with and coordinate the customer managers to carry out relevant work as soon as possible.