A signature based method for fraud detection on e-commerce scenarios

Abstract

Electronic transactions have revolutionized the way that consumers shop, making the small and local retailers, which were being affected by the worldwide crisis, accessible to the entire world. As e-commercemarket expands, the number of commercial transactions supported by credit cards—Card or Customer Not Present also increases. This growing relationship, quite natural and expected, has clear advantages, facilitating e-commerce transactions and attracting new possibilities for trading. However, at the same time a big and serious problem emerges: the occurrence of fraudulent situations in payments. In this work, we used a signature based method to establish the characteristics of user behavior and detect potential fraud cases. A signature is defined by a set of attributes that receive a diverse range of variables—e.g., the average number of orders, time spent per order, number of payment attempts, number of days since last visit, and many others—related to the behavior of a user, referring to an e-commerce application scenario. Based on the analysis of user behavior deviation, detected by comparing the user’s recent activity with the user behavior data, which is expressed through the user signature, it is possible to detect potential fraud situations (deviant behavior) in useful time, giving a more robust and accurate decision support system to the fraud analysts on their daily job.