# Abnormal Trading Detection in the NFT Market

arXiv: https://arxiv.org/abs/2306.04643 · HTML: https://mlsanity.com/paper/2306.04643/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 8 of 8)

- [Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2105.07447/) (2021)
- [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021)
- [Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods](https://mlsanity.com/paper/1611.03941/) (2016)
- [NFT Wash Trading: Quantifying suspicious behaviour in NFT markets](https://mlsanity.com/paper/2202.03866/) (2022)
- [Understanding Security Issues in the NFT Ecosystem](https://mlsanity.com/paper/2111.08893/) (2021)
- [Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot](https://mlsanity.com/paper/2202.04013/) (2022)
- [Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges](https://mlsanity.com/paper/2102.07001/) (2021)
- [NFTDisk: Visual Detection of Wash Trading in NFT Markets](https://mlsanity.com/paper/2302.05863/) (2023)

## Abstract (arXiv)

The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. The lack of industry-wide regulations, and the fact that amateur traders and retail investors comprise a significant fraction of the NFT market, make this market particularly vulnerable to fraudulent activities. Therefore it is essential to investigate and highlight the relevant risks involved in NFT trading. In this paper, we attempted to uncover common fraudulent behaviors such as wash trading that could mislead other traders. Using market data, we designed quantitative features from the network, monetary, and temporal perspectives that were fed into K-means clustering unsupervised learning algorithm to sort traders into groups. Lastly, we discussed the clustering results' significance and how regulations can reduce undesired behaviors. Our work can potentially help regulators narrow down their search space for bad actors in the market as well as provide insights for amateur traders to protect themselves from unforeseen frauds.