  Non-Fungible Token (NFT) markets are one of the fastest growing digital
markets today, with the sales during the third quarter of 2021 exceeding $10
billions! Nevertheless, these emerging markets - similar to traditional
emerging marketplaces - can be seen as a great opportunity for illegal
activities (e.g., money laundering, sale of illegal goods etc.). In this study
we focus on a specific marketplace, namely NBA TopShot, that facilitates the
purchase and (peer-to-peer) trading of sports collectibles. Our objective is to
build a framework that is able to label peer-to-peer transactions on the
platform as anomalous or not. To achieve our objective we begin by building a
model for the profit to be made by selling a specific collectible on the
platform. We then use RFCDE - a random forest model for the conditional density
of the dependent variable - to model the errors from the profit models. This
step allows us to estimate the probability of a transaction being anomalous. We
finally label as anomalous any transaction whose aforementioned probability is
less than 1%. Given the absence of ground truth for evaluating the model in
terms of its classification of transactions, we analyze the trade networks
formed from these anomalous transactions and compare it with the full trade
network of the platform. Our results indicate that these two networks are
statistically different when it comes to network metrics such as, edge density,
closure, node centrality and node degree distribution. This network analysis
provides additional evidence that these transactions do not follow the same
patterns that the rest of the trades on the platform follow. However, we would
like to emphasize here that this does not mean that these transactions are also
illegal. These transactions will need to be further audited from the
appropriate entities to verify whether or not they are illicit.
