Cardholders’ behavior prediction is an important issue in credit card portfolio management. As a promising data mining approach, multiple
criteria programming (MCLP) has been successfully applied to classify credit cardholders’ behavior into two groups. In order to better control
credit risk for financial institutes, this paper proposes three methods based on MCLP to improve the ‘‘Bad’’ catching accuracy rate. One is called
MCLP with unbalanced training set selection, the second is called fuzzy linear programming (FLP) method with moving boundary, and the third is
called penalized multi criteria linear programming (PMCLP). The experimental examples demonstrate the promising performance of these
methods.