FRAUDability: Estimating Users' Susceptibility to Financial Fraud Using Adversarial Machine Learning

In recent years, financial fraud detection systems have become very efficient at detecting fraud, which is a major threat faced by e-commerce platforms. Such systems often include machine learning-based algorithms aimed at detecting and reporting fraudulent activity. In this paper, we examine the application of adversarial learning based ranking techniques in the fraud detection domain and propose FRAUDability, a method for the estimation of a financial fraud detection system's performance for every user. We are motivated by the assumption that "not all users are created equal" -- while some users are well protected by fraud detection algorithms, others tend to pose a challenge to such systems. The proposed method produces scores, namely "fraudability scores," which are numerical estimations of a fraud detection system's ability to detect financial fraud for a specific user, given his/her unique activity in the financial system. Our fraudability scores enable those tasked with defending users in a financial platform to focus their attention and resources on users with high fraudability scores to better protect them. We validate our method using a real e-commerce platform's dataset and demonstrate the application of fraudability scores from the attacker's perspective, on the platform, and more specifically, on the fraud detection systems used by the e-commerce enterprise. We show that the scores can also help attackers increase their financial profit by 54%, by engaging solely with users with high fraudability scores, avoiding those users whose spending habits enable more accurate fraud detection.