Non-fungible Tokens - Exploring Suspicious Washtrader Communities in NFT Networks

Non-fungible Tokens (NFTs) have received increased attention since 2021. NFTs can be susceptible to fraudulent activities such as washtrading or trading of counterfeit digital assets. Such behaviors threaten the trust in this new trading space and for this reason, NFT skeptics are suspicious of the true values of highly priced digital assets. In this paper, we propose a two-step methodological approach to identify washtraded assets, and the suspicious communities of washtraders. Our approach uses bipartite graph characteristics to provide an efficient algorithm that does not require computationally intensive methods. We also identify the challenges in this stream of research and propose suggestions to address those challenges. Our method demonstrates practical applicability on real life networks of NFT transactions and opens doors for several future directions for investigating and exploring the communities of suspicious washtrading actors

Shill Bidder’s Behavior in a Second-Price Online Auction

Shill bidding is a fraudulent in-auction strategy where a seller participates as a bidder in his own auctions. This is the first paper on shill bidding that is based on a data set which includes personal details. Along with bidding histories, I can prove that on the investigated platform 0.3% of all auctions were influence by obvious shill bidders. The majority of the proven shill bidders' behavior in this paper does not fulfill any of the shill bidder types' criteria discussed in the literature. I adopt two algorithms which aim to identify shill bidders based on public information. On average, these approaches assign a higher probability of being a shill bidder to the accounts of bidders who certainly shilled on auctions in my data set. However, a reliable identification of proven shill bidders and honest bidders is only possible to a limited extent

Investigating shill bidding behaviour involving colluding bidders

Shill bidding is where spurious bids are introduced into an auction to drive up the final price for the seller, thereby defrauding legitimate bidders. Trevathan and Read presented an algorithm to detect the presence of shill bidding in online auctions. The algorithm observes bidding patterns over a series of auctions, and gives each bidder a shill score to indicate the likelihood that they are engaging in shill behaviour. While the algorithm is able to accurately identify those with suspicious behaviour, it is designed for the instance where there is only one shill bidder. However, there are situations where there may be two or more shill bidders working in collusion with each other.\ud Colluding shill bidders are able to engage in more sophisticated strategies that are harder to detect. This paper proposes a method for detecting colluding shill bidders, which is referred to as the collusion score. The collusion score, either detects a colluding group, or forces the colluders to act individually like a single shill, in which case they are detected by the shill score algorithm. The collusion score has been tested on simulated auction data and is able to successfully identify\ud colluding shill bidders