Abstract
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 the real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple criteria linear programming based classification methods have been explored for analyzing credit cardholders’ behavior. This paper proposes a multiple criteria non-linear programming (MCNP) approach to discovering the bankruptcy patterns of credit cardholders. A real-life credit database from a major US bank is used for empirical study on MCNP classification. Finally, the comparison of MCNP and other known classification methods is conducted to verify the validation of MCNP method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
