Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
This work identifies key issues for improving fraud detection systems in data-driven economies, but it is incremental as it focuses on perspectives rather than new solutions.
The paper addresses the challenge of applying adversarial machine learning techniques to real-world fraud detection, highlighting differences from other domains and proposing directions to bridge the research gap.
Data economy relies on data-driven systems and complex machine learning applications are fueled by them. Unfortunately, however, machine learning models are exposed to fraudulent activities and adversarial attacks, which threaten their security and trustworthiness. In the last decade or so, the research interest on adversarial machine learning has grown significantly, revealing how learning applications could be severely impacted by effective attacks. Although early results of adversarial machine learning indicate the huge potential of the approach to specific domains such as image processing, still there is a gap in both the research literature and practice regarding how to generalize adversarial techniques in other domains and applications. Fraud detection is a critical defense mechanism for data economy, as it is for other applications as well, which poses several challenges for machine learning. In this work, we describe how attacks against fraud detection systems differ from other applications of adversarial machine learning, and propose a number of interesting directions to bridge this gap.