From small local businesses to large multinationals, we can find a lot of initiatives in order to put their business online. E-Commerce is rapidly expanding worldwide with the emergence of new web shops, business to business services, or payment services, just to name a few. E-Commerce companies have been continuously improving their business incomes and extending their influence in the market. However, this proliferation of online business raised a very old phenomenon: fraud. Consequently, E-Commerce companies lose significant amounts of money every year due to a large diversity of fraudulent cases. This phenomenon is quite difficult to manage and handle, requiring significant efforts, both human and financial, in its identification and treatment. In this paper we present and discuss the use of signatures on the detection of potential fraud situations in an E-Commerce Web application, showing the way signatures are processed and analysed, as well as they can contribute to establish user behaviour deviation and consequently detect potential fraud situations in useful time.

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.

Electronic transactions (e-commerce) have revolutionized the way consumers shop, making small and local retailers, which were being affected by the worldwide crisis, accessible to the entire world. As e-commerce market expands, commercial transactions supported by credit cards - Card or Customer Not Present (CNP) 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 emerge: the occurrence of fraudulent situations in payments. Fraud imposes severe financial losses, which deeply impacts e-commerce companies and their revenue. In order to minimize losses, they spend a lot of efforts (and money) trying to establish the most satisfactory solutions to detect and counteract in a timely manner the occurrence of a fraud scenario. In the ecommerce domain, fraud analysts are typically interested in subject oriented customer data, frequently extracted from each order process that occurred in an e-commerce site. Besides transactional data, all their behavior data e.g. clickstream data are traced and recorded, enriching the means of detection with profiling data and providing a way to trace customers behavior along time. In this work, a signature-based method was used to establish the characteristics of user behavior and detect potential fraud cases. Signatures have already been used successfully for anomalous detection in many areas like credit card usage, network intrusion, and in particular in telecommunications fraud. 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 recent activity with the user behavior data, which is expressed through the user signature, it's possible to detect potential fraud situations (deviate behaviors) in useful time, giving a more robust and accurate support decision system to the fraud analysts on their daily job.