Real Time Data-Driven Approaches for Credit Card Fraud Detection

Abstract

Credit card fraud causes many financial losses for customer and also for the organization. For this reason, in the past few years, many studies have been performed using machine learning techniques to detect and block fraudulent transactions. This paper introduces two real time data-driven approaches using optimal anomaly detection techniques for credit card fraud detection. The efficiency of this method is studied over a real data set from European credit card holders. Our experiments show that our approaches achieved a high-level of detection accuracy and a low percentage of false alarm rate. Our approaches will bring many benefits for the organizations and for individual users in terms of cost and time efficiency.