Application Of Data Mining In Rural Commercial Banks In The Credit Management

In recent years, with the rapid development of rural economy and more and morefierce competition in rural commercial bank industry, the personal credit businessdevelops rapidly, and has already become the focus that each rural commercial bankfights for gradually. So, in order to preserve total profits and profit margin, ruralcommercial banks should pay more attention to their personal credit risk managementand optimize their credit risk management techniques.In this paper, firstly, I make a brief introduction of Data Mining and its operatingmechanism. Then I theoretically analyze the basic principles of Decision Tree Model,Neutral network Model and Logistic Model, and compare the advantages anddisadvantages of the above three models. After that, it is empirical research. In thispart, I use these three models to analyze historical personal credit data of ruralcommercial bank, and testify prediction accuracy of the three models, then presentsome defects of credit risk management of rural commercial banks. And summarizeboth advantages and disadvantages of different methods of data mining,This paper focuses on how to use data mining to strengthen credit riskmanagement of rural commercial banks. Specifically, there may be two majorinnovation points in this paper.(1)Adequate analysis of the rural commercial bank credit risk arises, on thisbasis, that the rural commercial banks in order to improve its credit risk managementshould focus on areas of improvement, and through the subsequent decision treemodel, confirmed the above conclusions;(2)The data mining technology into rural commercial banks’ credit riskmanagement, mainly focused on personal credit management. Were establishedthrough a decision tree model, neural network model and Logistic model analysis,credit risk assessment decisions and proposed rural commercial bank credit riskmanagement problems;...