Abstract: It is very important to extract the right features from transactional data in implementing a credit card fraud detection model. It is normally done by combining the transactions in order to observe the spending patterns of the customers. We propose to create a new set of features based on analyzing the periodic behavior of the time of a transaction using the von Mises distribution in this paper. We compare credit card fraud detection models, and evaluate how the different sets of features have an impact on the results with the help of a real credit card fraud dataset provided by a large European card processing company. The results show an average increase in savings of 13% by including the proposed periodic features into the methods. The methodology proposed in this paper is currently being incorporated into the fraud detection system of aforementioned card processing company.
Keywords: Cost sensitive learning; Fraud detection; von Mises distribution.