Credit Risk Assessment Of Individual Bank Clients Based On Big Data Platform

With the further development of the Internet era,the explosive growth of various types of data information,the gradual popularization of big data technology,the direction and mode of development in various fields are also quietly changing.In the view of banking industry,mass data and big data technology not only bring new opportunities to its development,but also make all kinds of traditional business of banks face enormous challenges in the process of development and innovation.Starting from the credit risk of individual bank customers,this paper aims to improve the management level of bank credit risk through big data technology.The following tasks have been accomplished:Learn the basic concepts of big data,analyze the impact of big data related technology on banking industry,and further study the current domestic and foreign research on big data related technology and its specific application in financial industry.This paper studies the current mainstream credit risk control methods in banking industry,and clarifies the importance of banks for the construction of personal wind control system.This paper focuses on the calculation of individual customer default rate.By comparing the main calculation methods of individual customer default rate,this paper finally chooses the logical regression method to forecast.In order to be closer to the actual situation of the bank,this paper takes A Bank of Wuxi City as the basis,and combines the existing system of the bank to further study.Based on Huawei's big data product platform,machine learning and logistic regression are carried out through Spark Mlib tools.Finally,by analyzing the results of the calculation,it is found that the accuracy of the model is maintained at more than 80%,and the results are within our acceptable range.In the process of research,we encountered various problems,limited by the level of personal research and data integrity for modeling,the final results are not as perfect as planned,but we can draw the following preliminary conclusions:The ability to collect and process data will largely determine the difficulty of follow-up work and the accuracy of final results.Full data mining plays a very important role in the establishment of wind control model.In terms of data collection channels,in addition to collecting structured data from traditional channels,unstructured data from the Internet and even the Internet of Things need to be collected.This exploration has not done enough to test the data.For the result analysis,considering the small amount of data in the training model,the results are basically acceptable.It can be used as a reference for the future credit auditing of individual customers,and has a certain reference significance for reducing the non-performing rate of credit loans of individual customers in banks.It also provides a new way of thinking.