  Guaranteeing the security of transactional systems is a crucial priority of
all institutions that process transactions, in order to protect their
businesses against cyberattacks and fraudulent attempts. Adversarial attacks
are novel techniques that, other than being proven to be effective to fool
image classification models, can also be applied to tabular data. Adversarial
attacks aim at producing adversarial examples, in other words, slightly
modified inputs that induce the Artificial Intelligence (AI) system to return
incorrect outputs that are advantageous for the attacker. In this paper we
illustrate a novel approach to modify and adapt state-of-the-art algorithms to
imbalanced tabular data, in the context of fraud detection. Experimental
results show that the proposed modifications lead to a perfect attack success
rate, obtaining adversarial examples that are also less perceptible when
analyzed by humans. Moreover, when applied to a real-world production system,
the proposed techniques shows the possibility of posing a serious threat to the
robustness of advanced AI-based fraud detection procedures.
