SMU Data Science Review


Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based on pattern recognition. This approach provides a more generic form of time series classification at different frequencies and timescales to recognize fraudulent NFT patterns. Results showed that over 80% of confirmed fraudulent cases were identified by modeling (recall). For every predicted fraud case, the model was correct 50% of the time (precision). Investors, regulators, and other entities can use these techniques to reduce risk exposure to NFT fraudulent activity.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License