Adversarial attacks pose a significant threat to data-driven systems, and researchers have devoted considerable effort to studying them. Despite its economic relevance, credit card fraud detection has received comparatively little attention. To address this gap, we propose a novel threat model that highlights the limitations of existing attacks and motivates new approaches. We introduce Fraud-RLA, an adversarial attack against credit card Fraud Detection Systems that leverages Reinforcement Learning to evade detection. Fraud-RLA is designed to maximize the amount stolen by optimizing the exploration-exploitation trade-off while requiring substantially less prior knowledge than competing methods. Our experiments on a realistic Fraud Detection System show that Fraud-RLA is effective, even under the severe limitations imposed by our threat model.

In the digital economy, the growing demand for data sharing and trading makes data a critical asset. To facilitate data acquisition between data owners and data buyers, tools such as data markets have emerged. Such modern data ecosystems typically comprise several interconnected components, often based on machine learning. Unfortunately, as practice has shown, such components are vulnerable to adversarial attacks. Lacking proper security assessment measures is a severe limitation for the success of data markets. In this paper, we delve into the challenges posed by adversarial attacks using credit card fraud detection as an example use case. We show that popular techniques such as penetration testing through existing adversarial attacks is not a viable approach, and corroborate our analysis by showing how a naive random sample attack outperforms all tested methods when considering the specifics of the fraud detection problem. Motivated by this result, we propose alternative assessment approaches and discuss promising research directions for increasing our understanding of models’ robustness.