Black-Box Adversarial Entry in Finance through Credit Card Fraud Detection

In the literature, it is well explored that machine learning algorithms trained on image classes are highly vulnerable against adversarial examples. However, very limited attention has been given to other sets of inputs such as speech, text, and tabular data. One such application where little work has been done towards adversarial examples generation is financial systems. Despite processing sensitive information such as credit fraud detection and default payment prediction, a low depiction of the robustness of the financial machine learning algorithms can be dangerous. One possible reason for such limited work is the challenge of crafting adversarial examples on the financial databases. The financial databases are heterogeneous where features might have a strong dependency on each other. Whereas image databases are homogeneous, and hence several existing works have shown it is easy to attack the classifiers trained on them. In this paper, for the first, we have analyzed the vulnerability of several traditional machine learning classifiers trained on financial tabular databases. To check the robustness of these classifiers, 'black-box and classifier agnostic' adversarial attack is proposed through mathematical operations on the features. In brief, the proposed research for the first time presents a detailed analysis that reflects which classifier is robust against minute perturbation in the tabular features. Apart from that through the perturbation on individual features, it is shown which column feature is more or less sensitive for the incorrect classification of the classifier.