Behavior analysis of credit cardholders is one of the main research topics in credit card portfolio management. Usually, the cardholder's behavior, especially bankruptcy, is measured by a score of aggregate attributes that describe cardholder's spending history. In real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple linear programming-based classification methods have been promoted for analyzing credit cardholders' behaviors. As a continuation of this research direction, this paper proposes a heuristic classification method by using the fuzzy linear programming (FLP) to discover the bankruptcy patterns of credit cardholders. Instead of identifying a compromise solution for the separation of credit cardholder behaviors, this approach classifies the credit cardholder behaviors by seeking a fuzzy (satisfying) solution obtained from a fuzzy linear program. In this paper, a real-life credit database from a major US bank is used for empirical study which is compared with the results of known multiple linear programming approaches.
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