Searching for NFT wash trading in Telegram Usernames in the TON blockchain

Searching for NFT wash trading in Telegram Usernames in the TON blockchain

The lack of normal regulation of the crypto market makes it a testing ground for all possible scam mechanics. 2022 study showed that about 45% of NFT trade volume is part of wash trade.

Given such a large percentage in EVM chains, I became interested in how things are with this in the TON blockchain and I decided to apply the filters from the study above to find laundering transactions in the largest TON NFT collection - Telegram Usernames

Let's take a look at:

• what is NFT wash trading
• why it is used in NFT space and what led to such a large percentage of laundering in EVM blockchains
• methodology for searching for wash deals
• wash trading in Telegram Usernames - largest collection in TON blockchain

What is wash trading?

Under the wash trade in traditional financial markets is meant the conduct of fictitious transactions in order to mislead the market. Through wash transactions, fraudsters create the appearance of attractiveness of assets, trying to involve other market participants in trading these assets.

The idea of laundering is not new, as early as 1936 in the United States a law was adopted prohibiting such trade. But there are no such laws for the crypto market, so laundering transactions are used to draw attention to certain tokens.

NFT Wash Trading

When a potential buyer considers an NFT collection, he is trying to understand whether it shows a stable growth so that by buying NFT he can sell it later at a higher price, they usually look at the trading volume indicators. Laundering trading will allow simulating volume growth by reselling an NFT element between multiple wallets.

Another scenario is an attempt to inflate the price of a particular NFT element. A potential buyer looks at the sales history and sees that the element is popular and there are many transactions, but does not realize that all the wallets in the transaction history lead to one wallet, which sponsored the price surge.

Marketing companies/hacks also contribute to the wash trade, in 2022 the NFT marketplace market was overheated, as new platforms were constantly launched. The platforms were compared with each other in terms of trading volume - where there are more, it will be easier to buy and sell your NFT. To stimulate sales Rarible, LooksRare, X2Y2 marketplaces began to reward with a token for transactions, which led to laundering transactions to receive reward tokens and their subsequent resale.

In summary, NFT wash trading is the resale of NFTs between controlled wallets for subsequent profit.

Filters to search for wash trading - hildobby Method

To search for wash trades, we will use the hildobby method, which consists of four filters, which we will consider below.

The data source will be the dton.io indexer, from which we will receive information. I don’t have the capacity to collect information on the entire TON, so let’s consider the largest collection of Telegram Usernames in terms of sales, which allows you to trade usernames in Telegram.

If you want to try to find TON wash trades for some other collection here there is a link to a Python script, with which you can collect transactions and pass them through the filters. I also note that the results in this article are relevant as of 08/24/2023, but they can always be updated through the script.

In total, as of 08/24/2023, there were 109158 sales in the collection with a volume of 57,143,316 TON, which is about $80,572,075 as of the date.

Let's move on to filters

Filter #1 Buyer equals Seller

Reselling a collection element to itself is clearly unnatural behavior. Therefore, the first filter looks for deals where the seller is equal to the buyer when selling NFTs. The results are as follows:

Buyer = Seller sales: 1562

Percentage of total sales: 1.43%

Buyer = Seller Volume: 333267 TON ~ $469,906

Percentage of total sales: 0.58%

Examples of such a transaction in the explorer and on the marketplace (not advertising, it's just convenient to watch there):

Filter #2 Reverse Trades

You can resell items not only between the same wallet, a simple strategy is to trade the same NFT between two different wallets, sending it back and forth.

There are few such deals in the Telegram Username collection:

Reverse sales: 366

Percentage of total sales: 0.34%

Volume of reverse trades: 96275 TON ~ 135747 dollars

Percentage of total sales: 0.17%

Examples of such a transaction in explorer and marketplace) (not advertising, it's easy to see it there.)

Filter #3 Purchase item three times

Since some unscrupulous NFT sellers create chains to resell items, there are money laundering deals that bypass the previous filters. Therefore, it is convenient to consider all transactions where one and the same owner repeats one and the same owner three or more times as money laundering.

The author of the technique emphasizes that this filter is not optimal, but the importance of this filter outweighs its disadvantages. Results:

3x sales: 307

Percentage of total sales: 0.28%

3x trade volume : 3676 TON ~ $5183

Percentage of total sales: 0.01%

Examples of such a transaction in the explorer and on the marketplace (not advertising, it's just convenient to watch there ):

Filter #4 Buyer and seller funded from the same wallet

To save time, wallets used for fictitious trading are often funded by the same wallet or each other. The filter will check each buyer and seller for who sent them the first transaction.

If it is the same wallet (or different), then it is marked as a laundering trade.

Sales with common owner: 1722

Percentage of total sales: 1.58%

Sale volume with common owner : 159462 TON ~ 224841 USD

Percentage of total sales: 0.28%

For example, this NFT had two owners 1 and 2, which were sponsored from one wallet.

Result

The total data is as follows:

Total sale in the collection: 109158

Transactions that fell under the filters: 3957

Percentage from all deals: 3.63 %

Filter trade Volume: 592680 TON ~ 835678 dollars

Percentage from Total Sales: 1.04 %

It is important to note that the filters certainly have errors and it seems to me that the fourth filter could be improved by searching for all transactions between wallets and highlighting some clusters between wallets (they could be displayed in bubble charts).

Also TON blockchain, at the architectural level, is an actor model in which smart contracts exchange messages and this leads to large chains of smart contracts in token transactions. Token standards in the TON network also lead to an elongation of the chains of smart contracts involved in the transfer of ownership. Therefore, filters designed for EVM networks can skip some of the money laundering transactions.

Conclusion

To dive deeper, I invite you to get to know the TON blockchain through my open source tutorials that I publish here. Also, the laundering transactions found above lead to the conclusion that due to the lack of normal regulation of the cryptocurrency market, any purchase of assets requires more research.
P.S If you like it I will be grateful if you like or repost the thread:https://x.com/roma_i_m/status/1697585133870579735?s=46&t=qYsFs6APw12MupUmHzTVCg