Detecting problematic transactions in a consumer-to-consumer e-commerce network
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Applied Network Science
Open Access
RESEARCH
Detecting problematic transactions in a consumer‑to‑consumer e‑commerce network Shun Kodate1,2, Ryusuke Chiba3, Shunya Kimura3 and Naoki Masuda2,4,5*
*Correspondence: [email protected] 4 Department of Mathematics, University at Buffalo, Buffalo, NY 14260‑2900, USA Full list of author information is available at the end of the article
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. Keywords: Network analysis, Machine learning, Fraud detection, Computational social science
Introduction 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. Fraud including cybercrimes costs billions of dollars per year and threatens the security of our society (UK Parliament 2017; McAfee 2019). In particular, in the recent era where online activity dominates, attacking a system is not too costly, whereas defending the system against fraud is costly (Anderson et al. 2013). The dimension of fraud is vast and ranges from credit card fraud, money laundering, computer intrusion, to plagiarism, to name a few.
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