Evaluating credit card transactions in the frequency domain for a proactive fraud detection approach

The massive increase in financial transactions made in the e-commerce field has led to an equally massive increase in the risks related to fraudulent activities. It is a problem directly correlated with the use of credit cards, considering that almost all the operators that offer goods or services in the e-commerce space allow their customers to use them for making payments. The main disadvantage of these powerful methods of payment concerns the fact that they can be used not only by the legitimate users (cardholders) but also by fraudsters. Literature reports a considerable number of techniques designed to face this problem, although their effectiveness is jeopardized by a series of common problems, such as the imbalanced distribution and the heterogeneity of the involved data. The approach presented in this paper takes advantage of a novel evaluation criterion based on the analysis, in the frequency domain, of the spectral pattern of the data. Such strategy allows us to obtain a more stable model for representing information, with respect to the canonical ones, reducing both the problems of imbalance and heterogeneity of data. Experiments show that the performance of the proposed approach is comparable to that of its state-of-the-art competitor, although the model definition does not use any fraudulent previous case, adopting a proactive strategy able to contrast the cold-start issue.