Year: 2026
Type: article
Abstract: 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4407186151