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-ofthe-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-theart approaches (i.e. Random Forests).

A Blockchain-Based Distributed Paradigm to Secure Localization Services

“…It means that a series of problems that affect security in a broad sense jeopardize the significant opportunities offered by these new technologies. Some cases in point are fraud related to the e-commerce infrastructure [ 24 ], where several approaches, proactive and retroactive, have been experimented in order to face such problems [ 25 ], as well as the ever-increasing number of identity theft [ 26 ] or, even more simply, the countless frauds made by exploiting the people’s trust [ 27 ], often by recurring to social engineering techniques [ 28 ].…”

Transfer Learning Strategies for Credit Card Fraud Detection

“…Hard versions add samples with specific labels while others [21] assign label confidences when fitting the model. A self-supervised approach based on Fourier transform and Wavelet transform is presented in [22]. • Feature representation: these methods aim to find a new feature representation and belong to two main categories: distribution similarity and latent approaches.…”

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

“…(Bolton and Hand, 2001) showed the necessity to use attributes describing the history of the transactions for unsupervised credit card fraud detection (peer group analysis). Lately, Saia and Carta (2019) used Fourier and wavelet transforms in order to move the transaction in a new domain before applying a machine learning algorithm. This allows to raise outliers based on a different view of the dataset (frequential view).…”