Research And Implementation Of Bank Credit Risk Prediction System Based On XGBoost

As the main hub for macroeconomic regulation and monetary policy regulation of the country's economy and finance,in order to ensure its healthy and stable development,it is essential to be able to predict the risks of loans effectively.Banks need to obtain the required content from a large amount of relevant data from lenders,and use these data to make efficient and accurate predictions of credit risk.At present,traditional credit forecasting methods have been unable to fully meet the needs of banks due to strong subjectivity,large labor consumption,and a small amount of data that can be processed.At the same time,machine learning as an emerging technology has been used in different fields.It can find corresponding rules among a large amount of data to help people reach a certain project,because of its accuracy,efficiency,and learnability.And other advantages are much appreciated.Compared with traditional artificial credit risk forecasting,a machine learning-based bank risk forecasting system not only greatly reduces the manpower required for credit risk forecasting,it can also process more data,and present a more objective and accurate approach to bank credit risk.The forecasting is very suitable for the application requirements of credit risk forecasting by banks today,and has great application value.And using appropriate machine learning algorithms to improve the accuracy of the prediction model as much as possible is also a very important part of the research work.The research purpose of this paper is to build a bank credit risk prediction system based on XGBoost algorithm.The main research contents of this article include the following aspects.First of all,this article learns and draws on the credit risk assessment methods of domestic and foreign banks,comprehensively sorts out the basic theories and core ideas of credit risk prediction,and analyzes the application requirements of credit risk prediction by banks today and traditional credit risk prediction methods.The characteristics of the era of big data suggest that machine learning is a powerful tool to improve the accuracy and efficiency of bank credit risk prediction.The core of the bank's credit risk prediction system is the algorithm module.In this paper,through a comparative analysis of today's machine learning algorithms,combined with the actual demand of the bank's credit risk prediction,it innovates the choice of algorithm and proposes to use XGBoost for bank credit.Risk prediction,and designed a model structure and parameters suitable for actual risk assessment,which improves the accuracy and efficiency of bank risk prediction.Compared with other related algorithms after model training,it proves that XGBoost is more suitable for bank credit risk prediction.This article builds a complete bank credit risk prediction system.The system can be divided into three modules,an administrator information management module,an employee information management module,and a credit information management module.The administrator information management module can manage the information of all ordinary employees,the employee information management module can manage the basic personal information of employees,and the loan information management module provides the bank staff with the interface of credit party information entry,editing and storage,and according to the staff The entered information predicts the risk of credit and prints an accurate forecast result.To assist bank personnel in making decisions on credit operations.