Machine Learning Methods for Credit Card Fraud Detection: A Survey

Kanishka Ghosh Dastidar, Olivier Caelen, Michael Granitzer

Abstract: The widespread adoption of online payments has been accompanied by a significant increase in fraudulent activities, resulting in billions of dollars in financial losses. As payment providers aim to tackle this with various preventive mechanisms, fraudsters also continuously evolve their methods to remain indistinguishable from genuine actors. This necessitates sophisticated fraud detection tools to supplement these security mechanisms. As the volume of transactions taking place per day is in the millions, relying solely on human investigation is expensive and ultimately unfeasible, leading to an emergence of research into data driven or statistical methods for fraud detection. Over the last decade, this research has evolved to tackle the various particularities of the domain. These include the skewed nature of the data, the evolving user and fraud behavior, and the learning representations of the context in which a transaction takes place. This work aims to provide the community with an in-depth overview of the different directions in which recent research on online fraud detection has focused. We develop a taxonomy of the domain based on these directions and organize our analysis accordingly. For each area, we focus on significant methodological advancements and highlight limitations or gaps in the current state-of-the-art solutions. Through our analysis, it emerges that one of the primary limiting factors that many researchers face is the lack of availability of high-quality credit card data. Therefore, we provide a first step in addressing this issue in the form of a data generation framework using generative adversarial networks (GANs). We hope that this survey serves as a foundation for researchers who want to address the multi-faceted problem of credit card fraud detection.

External IDs:dblp:journals/access/DastidarCG24