Research on Credit Customer Management Based on Customer Classification and Classification Preference

Abstract

Accurate classification of credit customers is the premise of providing personalized credit services to them. According to customers' credit needs, we collect customer sample data, and then use users' repayment ability and repayment willingness to mark the samples. Bayesian classifier is constructed by constructing probability distribution function. By using test training and testing classification algorithm, it is found that Gaussian Bayesian algorithm can classify and predict data well. In the process of classifying samples, funds are allocated in combination with classification preferences. Experiments show that credit rating parameters have a significant impact on the optimization of resource allocation. By properly setting the values of credit rating parameters and classification preferences, it has reference value for reducing credit risks.