Discovering credit cardholders' behavior by multiple criteria linear programming

In credit card portfolio management, predicting the cardholder's spending behavior is a key to reduce the risk of bankruptcy. Given a set of attributes for major aspects of credit cardholders and predefined classes for spending behaviors, this paper proposes a classification model by using multiple criteria linear programming to discover behavior patterns of credit cardholders. It shows a general classification model that can theoretically handle any class-size. Then, it focuses on a typical case where the cardholders' behaviors are predefined as four classes. A dataset from a major US bank is used to demonstrate the applicability of the proposed method.