A discrete wavelet transform approach to fraud detection

The exponential growth in the number of operations carried out in the e-commerce environment is directly related to the growth in the number of operations performed through credit cards. This happens because practically all commercial operators allow their customers to make their payments by using them. Such scenario leads toward an high level of risk related to the potential fraudulent activities that the fraudsters can perform by exploiting this powerful instrument of payment illegitimately. A large number of state-of-the-art approaches have been designed to address this problem, but they must face some common issues, the most important of them are the imbalanced distribution and the heterogeneity of data. This paper presents a novel fraud detection approach based on the Discrete Wavelet Transform, which is exploited in order to define an evaluation model able to address the aforementioned problems. Such objective is achieved by using only legitimate transactions in the model definition process, an operation made possible by the more stable data representation offered by the new domain. The performed experiments show that our approach performance is comparable to that of one of the best state-of-the-art approaches such as random forests, demonstrating how such proactive strategy is also able to face the cold-start problem.