Detection and Prevention of Fraud in Online Auction

Online auctions have become an increasingly popular and convenient method for conducting e-commerce transactions over the Internet. However, the rapid growth in the number of users participating in online auctions has also led to the emergence of auction fraud. Among the most common forms is shill bidding. This type of fraud consists of intentionally deceptive bidding carried out by the seller on their own product in order to artificially inflate the final price. This may be done either directly by the seller or by an individual acting in collusion with the seller by placing fictitious bids on their behalf. Therefore, investigating a large number of auctions and participants in order to identify shill bidding activities is a challenging task. In this paper, by applying Dempster–Shafer theory, methods are presented for determining whether an auction in which a user intends to participate is affected by fraudulent behavior. The research methodology integrates mathematical modeling with statistical and empirical approaches and is grounded in Dempster–Shafer theory in order to develop a robust framework for detecting fraudulent behavior in online auction systems. The main findings and implications for the auction field consist in providing a clear analytical framework for evaluating auctions and in offering a competitive advantage to participating users. The contribution of this paper to the field lies in clarifying the mechanisms employed within auctions for all participants. The purpose of the paper is to serve as a useful tool for understanding and participating in auctions by providing a clear bidding framework and contributing to the prevention of artificial price inflation.