Data Mining In Rural Credit Operations In The Applied Research

Data mining technology have a wide range of applications in business and industry,especially in specialized banks of developed countries and areas. However, little application has been found in rural credit markets. Applying data mining technology to the rural credit rating stage is studied in this paper. The work serve as a useful attempt in the developing market. Qualitative analysis is the major method of credit rating for peasants due to simple rural credit business model and credit rating techniques. The research in this paper focus on studying peasants'credit rating model in rural credit business. The decision tree algorithm in data mining was applied to the basic information of peasants collected by peasants credit rating institution. The implicit law was disclosed,which had been quantified in the specific scoring model. Meanwhile, the model had been tested by real cases and adjusted to meet the actual needs of the new peasants'rating model.Peasants'credit rating was achieved through specific data mining experiment. A peasants'rating decision tree model was established after data collection, extraction, pretreatment of peasants'rating data in reality and data of the original database. The derived rating model is a percentile personal credit rating model for peasants. The evaluation criteria for the model was also built.The peasants'credit rating model is formed through objective modeling method. Subjective modeling approach was introduced in adjustment. The subjective personal credit evaluation model was built using the analytic hierarchy approach, which make adjustment to the objective modeling method. The model helps making credit analysis and therefore better decisions for peasants'rating loan.