The data in the bank customer’s credit scoring often include lots of missing values，which affect the modeling performance to a large extent． To overcome the deficiencies of existing models，this paper proposes a dynamic classifier ensemble selection model for missing values ( DCESM) ． The model can make full use of the information included in the dataset and does not need to pre-process the missing values before training the model，which decreases the dependence on the hypothesis for data missing mechanism and distribution model．Two credit scoring datasets on bank credit card business from UCI database were selected for our empirical analysis．The results show that the DCESM model outperformed four imputation-based multi-classifiers ensemble models and one ensemble model for missing values