Design And Implementation Of Credit Business Management System Based On Data Mining

In the process of economic development,the development of the financial industry has greatly promoted economic development.Therefore,how to develop the financial industry and make the financial institutions developed stably and efficiently is very important.For banks,the credit business is one of its key profitable businesses.At present,rural commercial banks belong to a kind of local small legal entity.The profit of such units is mainly dependent by interest.Its performance is particularly affected by its own credit management level.Because rural commercial banks have many outlets and many businesses,it is difficult to manage the credit business.This makes it more difficult for rural commercial banks to manage credit business than general commercial banks.Since the credit business is very important for the operation of rural commercial banks,how to effectively manage the credit business of rural commercial banks is an important issue in the management and operation of banks.At present,the related work is handled manually,which makes the credit management lagging behind and the management workload is large.Therefore,this thesis proposes to use computer technology to improve the credit management of rural commercial banks.The automation operation of credit business management will be improved,the informatization degree of credit management of rural commercial banks will be improved,work efficiency will be improved,and better management will be realized by making the credit business management system.In actual work,rural commercial banks have many types of credit asset risks,and the classification factors are complex.And the credit asset risk classification will change as the business develops.In order to make the model of the system identify new risks,this thesis uses an unsupervised method to classify them.In the unsupervised method,this thesis chooses the K-Means algorithm.For the credit line adjustment of the lender,because the classification index of the business is clear,the classification result is simple,and it is suitable to adopt the decision tree algorithm.This thesis chooses the more mature C4.5 algorithm in the decision tree algorithm to make decisions on the credit line adjustment of the lender.In addition,due to the stability and concurrency requirements of the banking system,the system needs to have distributed capabilities.In response to this problem,the system is implemented using the SSH framework.For the distributed needs of the banking system,Dubbo and ZooKeeper technologies are used in the implementation process.The combination of these two technologies can conveniently implement stable distributed technology applications.Through the test in the simulated environment,the effectiveness of the system developed in this thesis is proved,which plays an important role in the management of credit business of rural commercial banks.