Research Summary: NFT Wash Trading: Quantifying Suspicious Behaviour in NFT Markets

Great summary @f13r. I enjoyed reading through your work.

In February 2022, [Chainalysis] (NFT Money Laundering and Wash Trading) published a report on NFT wash trading by analyzing sales of NFTs to addresses that were self-financed, or “funded either by the selling address or by the address that initially funded the selling address.” It concluded that “some NFT sellers have conducted hundreds of wash trades.”

In particular, it was able to identify 262 users who had sold an NFT to a self-financed wallet more than 25 times. Although Chainalysis admitted that it could not be 100% sure that all of the users were NFT wash traders, 110 of those wallets collectively generated $8.9 million in profit in 2021.

Furthermore to being safe, there are signs to look out for when an NFT has been wash traded, such as:

  1. A major difference between the floor price and list price of an NFT in a collection.
  2. Ultimately, researching an NFT collection and
  3. Looking at the transaction history is important to understanding if an NFT’s price has been manipulated
1 Like

NFT trading combines both art and money markets, both of which are complex on their own not to mention the crypto aspect. The result is a rather complex asset class.

Ironically, NFT markets attract a large segment of population that are not specialized in navigating either money or art markets and might not be in a position to spend enough time identifying adversarial patterns.

This work is a nice resource towards exposing the risks in the market to the growing population of retailers and hopefully make participation more informed and safer.

1 Like

In my opinion,Someone can make this project successful an infinite number of accounts and conduct business between them. The standards of need and proportionality are needed to ensure that this does not violate any privacy or privacy laws. But that might alter when investigators emphasize and also use existing pro laws against fresh NFT platforms. Perhaps NFT marketplaces could intervene in this situation by keeping an eye on questionable transactions and restricting them, or, in the most extreme case, blocking accounts.

Thank you @f13r for this fantastic summary. It is indeed an excellent summary because it speaks directly to the quantitative suspicious behavior in NFT markets.

There is no doubt that NFTs are selling like hotcakes (digitally). Christie’s sold a digital collage by artist Beeple for $69.3 million at the NFT auction in March 2021. When you start translating the real into hyper-real, you see endless scenarios that capture people’s imagination. Nevertheless, as with any shiny new toy, there have been fraudsters appearing within the binary code.

The manipulation of NFTs is similar to what we’ve seen with more traditional crypto assets, such as Bitcoins. Hacking wallets and stealing the NFTs is not just another scam, but a process through which it’s possible to assign multiple ownerships to digital designs using the NFTs.

Users have to submit their social media handles for NFT verifications, but the entity submitting these details need not prove ownership. Digital works have been fraudulently mis-sold due to this practice. Did we just create yet another avenue for crypto-crime instead of simplifying it?

Wash trading is pretty widespread on centralized cryptocurrency exchanges, as well as the NFT industry. Our mission is to warn you about the possibilities that keep NFTs a bit too clean. Knowing how to recognize frauds and scams will make it easier to avoid them. Isn’t it? But first, what are NFTs?

How is Wash Trading identified?
Early detection is primarily achieved through the use of graph clustering and nearest neighbor algorithms. But a related phenomenon called collusive cliques has also been researched.

On aggregated order volume time series, hidden Markov models, spectral clustering, and correlation statistics are other methods that help. Check out more about how these models can help in identifying complex sequence data, fraudulent activities, and failure detection.

One of the first studies to explicitly examine wash trades in the markets was by Cao et al. According to them, prior studies focused primarily on collusive and correlated trading patterns rather than wash trading’s more specific form.

A given set of trades consists of subsets that result in no position change to the traders involved. Based on their topological structures, these trades comprise a closed cycle. A closed-cycle trade is one that does not engage in exchanges with other economies. Closed economies are completely self-sufficient, which means no goods are imported or exported.

Although this sounds very much sorted on paper, in practice, it needs an ecosystem that can detect wash trading in NFTs for real. Do we have a solution for that? Yes! And the good news is Scour!

Detecting wash trading the Scour way!

With all the good news of Scour, they have come with incredible approaches to identify the fraudulent activities more precisely and practically. Having said that, with multiple methods on the table, when caught, washtraders keep coming up with new ways to washtrade. New wallets are created and new methods of washtrading are tried.

However, Scour reduced the washtraders by over 40% within two months. Unbelievable? How does it happen?

How does bitsCrunch’s knowledge graph provide a solution to Wash Trading?

As far as we learned till now, market manipulators are hard to catch. Even though the tokens are non-fungible, they are still anonymous, making fraud detection difficult. But, Scour upgrades every week to identify the new possibilities of wash trading by extreme research on the NFT activities and keeps a checkmate to them asap.

Scour is a unique product created by BitsCrunch, an AI-powered analytics company that can actively remove wash traders from exchanges.

The Scour index compiles transactions, wallet addresses, and distributions of reward tokens. Using knowledge graphs and AI technology, Scour can identify bogus orders and complex patterns of wash trades. The technology not only secures investors and markets but also solves the open secret of cryptocurrencies.

BitsCrunch built Scour with a detailed knowledge graph that includes a complete index of the NFT markets transactions thanks to partnerships with industry leaders like Covalent, Rarible, and deeplearning.ai.

At its core, Scour is an AI product that flags the spoofing activity among the traders to protect the platform (marketplace) from its market-mining operations