Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives

TL;DR

This paper discusses the unique challenges of applying adversarial machine learning to fraud detection systems, highlighting differences from other domains and proposing future research directions to enhance robustness.

Contribution

It identifies domain-specific challenges in adversarial attacks on fraud detection and suggests new research directions to address these issues.

Findings

Adversarial attacks in fraud detection differ from other domains.

Current research gaps in generalizing adversarial techniques to fraud detection.

Proposed directions to improve robustness of fraud detection systems.

Abstract

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.