Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks

Abstract The exponential growth in the number of E-commerce transactions indicates a radical change in the way people buy and sell goods and services, a new opportunity offered by a huge global market, where they may choose sellers or buyers on the basis of multiple criteria (e.g., economic, logistical, ethical, sustainability, etc.), without being forced to use the traditional brick-and-mortar criterion. If, on the one hand, such a scenario offers an enormous control to people, both at private and corporate level, allowing them to filter their needs by adopting a large range of criteria, on the other hand, it has contributed to the growth of fraud cases related to the involved electronic instruments of payment, such as credit cards. The Big Data Information Security for Sustainability is a research branch aimed to face these issues in relation to the potential implications in the field of sustainability, proposing effective solutions to design safe environments in which the people can operate and by exploiting the benefits related to new technologies. The fraud detection systems are a significant example of such solutions, although the techniques adopted by them are typically based on retroactive strategies, which are incapable of preventing fraudulent events. In this perspective, this paper aims to investigate the benefits related to the adoption of proactive fraud detection strategies, instead of the canonical retroactive ones, theorizing those solutions that can lead toward practical effective implementations. We evaluate two previously experimented novel proactive strategies, one based on the Fourier transform, and one based on the Wavelet transform, which are used in order to move the data (i.e., financial transactions) into a new domain, where they are analyzed and an evaluation model is defined. Such strategies allow a fraud detection system to operate by using a proactive approach, since they do not exploit previous fraudulent transactions, overcoming some important problems that reduce the effectiveness of the canonical retroactive state-of-the-art solutions. Potential benefits and limitations of the proposed proactive approach have been evaluated in a real-world credit card fraud detection scenario, by comparing its performance to that of one of the most used and performing retroactive state-of-the-art approaches (i.e. Random Forests).