Empirical analysis of online auction fraud: Credit card phantom transactions

Online auctions allow buyers to find a wider variety of items and help sellers to reach literally millions of buyers. Auctioning over the internet gives a variety of opportunities that are not offered for consumers offline. However, on the other hand, it also provides good conditions for opportunistic behaviors because of the high degree of information asymmetry. To prevent online auction fraud, preventative controls verifying the identities of auction users can be imposed. However, these measures can adversely affect the potential user-base of online markets. In this paper, we examine the ex-post detection of online fraud. Among examples of serious online fraud prevalent in auctions, we investigate the factors necessary to detect “online credit card phantom transactions,” which are fake transactions for illegal loan sharking through the collusion of the seller (creditor) and buyer (debtor). In this paper, we develop a plausible detection methodology for online fraud. In addition, employing a data collection agent, we demonstrate cost-efficient ways of data collection. Auctioneers, e-business firms with fraud-related problems, and regulatory agencies can all take advantage of this methodology. Academically, we believe that our research is a new addition to the body of empirical studies on online auction fraud.