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Since credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurring from credit card fraud is an important driver for the sector and end-users. Rule-based fraud detection tools have been widely used as a part of credit card systems. Rules of such tools are determined by human fraud experts. However, experts mostly ignore cardholder-specific spending behavior. In this paper, we focus on analyzing the cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is named Cardholder Behavior Model (CBM).
Kültür et al. (Thu,) studied this question.