Abstract
Providers of online marketplaces are constantly combatting against problematic transactions, such as selling illegal items and posting fictive items, exercised by some of their users. A typical approach to detect fraud activity has been to analyze registered user profiles, user’s behavior, and texts attached to individual transactions and the user. However, this traditional approach may be limited because malicious users can easily conceal their information. Given this background, network indices have been exploited for detecting frauds in various online transaction platforms. In the present study, we analyzed networks of users of an online consumer-to-consumer marketplace in which a seller and the corresponding buyer of a transaction are connected by a directed edge. We constructed egocentric networks of each of several hundreds of fraudulent users and those of a similar number of normal users. We calculated eight local network indices based on up to connectivity between the neighbors of the focal node. Based on the present descriptive analysis of these network indices, we fed twelve features that we constructed from the eight network indices to random forest classifiers with the aim of distinguishing between normal users and fraudulent users engaged in each one of the four types of problematic transactions. We found that the classifier accurately distinguished the fraudulent users from normal users and that the classification performance did not depend on the type of problematic transaction.
Highlights
In tandem with the rapid growth of online and electronic transactions and communications, fraud is expanding at a dramatic speed and penetrates our daily lives
We evaluated the performance of the random forest classifier with the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve measured on a single set of training and test samples
We showed that a random forest classifier using network features of users distinguished different types of fraudulent users from normal users with approximately 0.91–0.98 in terms of the AUC
Summary
In tandem with the rapid growth of online and electronic transactions and communications, fraud is expanding at a dramatic speed and penetrates our daily lives. Standard practice for fraud detection is to employ statistical methods including the case of machine learning algorithms. When both fraudulent and non-fraudulent samples are available, one can construct a classifier via supervised learning (Bolton and Hand 2002; Phua et al 2010; Abdallah et al 2016; West and Bhattacharya 2016). Exemplar features to be fed to such a statistical classifier include the transaction amount, day of the week, item category, and user’s address for detecting frauds in credit card systems, number of calls, call duration, call type, and user’s age, gender, and geographical region in the case of telecommunication, and user profiles and transaction history in the case of online auctions (Abdallah et al 2016)
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