A recent study jointly conducted by Visa and Allium Labs discovered that less than 10% of the reported $2.2 trillion stablecoin transaction volumes in April 2021 were generated by actual users, amounting to only $149 billion.
The research intends to isolate human-initiated activity from bot or bulk trader transactions. Various strategies like machine learning algorithms and comprehensive data analysis will be used to differentiate between the two.
The stablecoin market, currently supplied with roughly $150 billion, is dominated by Tether and USD coin, holding market shares of 75% and 22% respectively. Other competing players such as Binance USD, Dai, and TrueUSD account for the remaining 3%.
However, there has been a rise in new entrants into the market, leading to increased competition and diversity. For instance, Facebook plans to introduce its own stablecoin, “Diem”.
Analyzing bot influence on stablecoin transactions
This could potentially disrupt the stablecoin distribution model as we know it.
Stablecoins, a type of digital currency that mirrors the value of other asset classes such as the U.S. dollar, are experiencing heightened interest and scrutiny, especially with major companies like PayPal announcing potential launches.
Legislation surrounding stablecoins will likely be prioritized by the U.S. Congress, hinting at major changes in crypto-related policies. Cuy Sheffield, Head of Crypto at Visa, emphasized the difficulties in analyzing this data accurately due to the diverse uses of stablecoins and the rapidly evolving cryptocurrency landscape.
Even though there is a large gap between total transfer volumes and bot-adjusted transfer volumes, the study noted a consistent rise in active stablecoin users each month. Currently, there are around 27.5 million users across all chains.
This rise in activity aligns with the sharp increase in the total global supply of stablecoins, which is at a record high. The researchers have highlighted the significant influence of bot transactions on these transfer volumes, thereby necessitating meticulous data interpretation. Future research will further explore variations in user behaviors across different blockchain networks.
