Stablecoin payments have approached nearly half of all transaction volume but are highly concentrated in a few institutional wallets: the top 1,000 addresses contribute approximately 85% of the transfer volume; although the number of P2P transactions is high, their monetary share is significantly low.
Article author: Artemis
Article compiled by: Shenchao Techflow
This report empirically analyzes the payment usage of stablecoins, covering transactions between individuals (P2P), businesses (B2B), and between individuals and businesses (P2B/B2P).
This report conducts an empirical analysis of stablecoin payment usage, studying transaction patterns between individuals (P2P), businesses (B2B), and between individuals and businesses (P2B/B2P). We utilize the Artemis dataset, which provides metadata on wallet addresses, including geographical location estimates, institutional ownership tags, and smart contract identifiers. Based on the characteristics of the sender and receiver wallets, we classify the transactions. The analysis focuses on the Ethereum network, which accounts for approximately 52% of the global stablecoin supply.
We primarily studied two mainstream stablecoins: USDT and USDC, which together account for 88% of the market share. Despite the significant increase in the adoption rate and regulatory attention on stablecoins over the past year, a key question remains unanswered: how does the actual use of stablecoins in payments compare to other activities? This report aims to reveal the main drivers of stablecoin payment adoption and provide insights for predicting future trends.
1. Background
In recent years, the adoption rate of stablecoins has significantly increased, with their supply reaching $200 billion, and the monthly raw transfer volume exceeding $4 trillion. Although blockchain networks provide fully transparent transaction records and all transactions can be analyzed, conducting transaction and user analysis remains challenging due to the anonymity of these networks and the lack of information regarding the purpose of transactions (e.g., domestic payments, cross-border payments, trading, etc.).
In addition, the use of smart contracts and automated trading on networks like Ethereum further complicates the analysis, as a single transaction may involve interactions with multiple smart contracts and tokens. Therefore, a key unresolved issue is how to assess the current use of stablecoins in the payment space relative to other activities (such as trading). Although many researchers are working to address this complex issue, this report aims to provide additional methods to assess the use of stablecoins, particularly for payment purposes.
Overall, there are two main methods for assessing the use of stablecoins (especially for payment purposes).
The first method is the filtering approach, which uses raw blockchain transaction data and removes noise through filtering techniques to provide a more accurate estimate of the payment use of stablecoins.
The second method is to survey major stablecoin payment providers and estimate stablecoin activity based on their disclosed payment data.
The Visa Onchain Analytics Dashboard developed in partnership with Allium Labs adopts the first method. They reduce noise in the raw data using filtering techniques, providing clearer information on stablecoin activity. The research shows that after filtering the raw data, the overall monthly stablecoin transaction volume decreased from approximately $5 trillion (total transaction volume) to $1 trillion (adjusted transaction volume). If only retail transaction volume (transactions with amounts below $250) is considered, the volume is only $6 billion. We adopted a filtering approach similar to that of the Visa Onchain Analytics Dashboard, but our method focuses more on explicitly tagging transactions for payment purposes.
The second method is based on company survey data, applied in the (Fireblocks 2025 Stablecoin Status Report) and (Stablecoin Payments from Scratch Report). These two reports utilize disclosures from major companies in the blockchain payment market to estimate the direct use of stablecoins in payments. Specifically, the (Stablecoin Payments from Scratch Report) provides an overall estimate of stablecoin payment transaction volumes and categorizes these payments into B2B (business-to-business), B2C (business-to-consumer), P2P (person-to-person), and other categories. The reports show that as of February 2025, the annual settlement total is approximately $72.3 billion, most of which is B2B transactions.
The main contribution of this study lies in applying data filtering methods to estimate the use of stablecoins in on-chain payments. The findings reveal the usage of stablecoins and provide more accurate estimations. Additionally, we offer guidance for researchers on how to handle raw blockchain data using data filtering methods, reduce noise, and improve estimations.
2. Data
Our dataset covers all stablecoin transactions on the Ethereum blockchain from August 2024 to August 2025. The analysis focuses on transactions involving the two main stablecoins USDC and USDT. These two stablecoins were chosen due to their high market share and strong price stability, which reduces noise during the analysis process. We focus only on transfer transactions, excluding minting, burning, or bridging transactions. Table 1 summarizes the overall situation of the dataset used in our analysis.
Table 1: Summary of Transaction Types
3. Methods and Results
In this section, we detail the methods used to analyze the use of stablecoins, focusing on payment transactions. First, we filter the data by distinguishing between transactions involving interactions with smart contracts and transactions representing transfers between EOA (external accounts), categorizing the latter as payment transactions. This process is detailed in Section 3.1. Subsequently, Section 3.2 explains how to utilize the EOA account label data provided by Artemis to further categorize payment transactions into P2P, B2B, B2P, P2B, and internal B-class transactions. Finally, Section 3.3 analyzes the concentration of stablecoin transactions.
3.1 Stablecoin Payments (EOA) and Smart Contract Transactions
In the decentralized finance (DeFi) space, many transactions involve interactions with smart contracts and combine multiple financial operations in a single transaction, such as exchanging one token for another through multiple liquidity pools. This complexity makes it even more difficult to analyze the use of stablecoins solely for payment purposes.
To simplify the analysis and improve the ability to tag stablecoin blockchain transactions as payments, we define stablecoin payments as any transaction involving an ERC-20 stablecoin transferred from one EOA address to another EOA address (excluding minting and burning transactions). Any transaction not tagged as a payment will be classified as a smart contract transaction, including all transactions involving interactions with smart contracts (e.g., primarily DeFi transactions).
Figure 1 shows that most payments between users (EOA-EOA) are completed directly, with each transaction hash corresponding to a single transfer. Some multiple EOA-EOA transfers within the same transaction hash are mainly performed through aggregators, indicating that the use of aggregators in simple transfers remains relatively low. In contrast, the distribution of smart contract transactions is different, containing more multiple transfer transactions. This indicates that in DeFi operations, stablecoins often flow between different applications and routers before ultimately returning to EOA accounts.
Figure 1:
*The sample data for this analysis covers transactions from July 4, 2025, to July 31, 2025.
Table 2 and Figure 2 show that, in terms of transaction counts, the ratio of payments (EOA-EOA) to smart contract transactions (DeFi) is approximately 50:50, while smart contract transactions account for 53.2% of transaction volume. However, Figure 2 shows that the volatility of transaction volume (total transfer amount) is greater than that of transaction counts, indicating that large EOA-EOA transfers primarily by institutions cause these fluctuations.
Table 2: Summary of Transaction Types
Figure 2:
Figure 3 explores the distribution of transaction amounts for payments (EOA-EOA) and smart contract transactions. The amount distributions for payment transactions and smart contract transactions are both similar to a heavy-tailed normal distribution, with an average value of about $100 to $1,000.
However, there was a significant spike in transactions with amounts below $0.1, which may indicate the presence of bot activity or manipulation related to false trading activities and wash trading, consistent with the descriptions by Halaburda et al. (2025) and Cong et al. (2023).
Since Ethereum's gas fees often exceed $0.1, transactions below this threshold require further scrutiny and may be excluded from the analysis.
Figure 3:
The sample data for this analysis covers transaction records from July 4, 2025, to July 31, 2025.
3.2 Payment Types
By utilizing the labeling information provided by Artemis, further analysis can be conducted on payments between two EOAs (external accounts). Artemis provides labeling information for many Ethereum wallet addresses, allowing identification of wallets owned by institutions (e.g., Coinbase). We categorize payment transactions into five types: P2P, B2B, B2P, P2B, and internal B-class. The following is a detailed description of each category.
P2P Payments:
P2P (person-to-person) blockchain payments refer to transactions that transfer funds directly from one user to another via a blockchain network. In account-based blockchains (such as Ethereum), such P2P transactions are defined as the process of digital assets being transferred from one user's wallet (EOA account) to another user's EOA wallet. All transactions are recorded and verified on the blockchain, without the involvement of intermediaries.
Key Challenges:
Identifying whether transactions between two wallets in an account system actually occur between two independent entities (i.e., individuals rather than companies) and correctly classifying them as P2P transactions is a major challenge. For example, transfers between a user's own accounts (i.e., Sybil accounts) should not be counted as P2P transactions. However, if we simply define all transactions between EOAs (external accounts) as P2P transactions, we may inadvertently misclassify such transfers as P2P.
Another issue arises when an EOA account is owned by a company, such as a centralized exchange platform (CEX, like Coinbase); this EOA wallet is not actually owned by a real individual. In our dataset, we are able to label many institutional and company EOA wallets; however, due to incomplete labeling information, some EOA wallets owned by companies but not recorded in our dataset may be incorrectly labeled as personal wallets.
Lastly, this method fails to capture blockchain P2P payments conducted through intermediaries — also known as the 'stablecoin sandwich' model. In this model, funds are transferred between users through intermediaries that settle via blockchain. Specifically, fiat currency is first sent to the intermediary, which converts it into cryptocurrency, and then the funds are transferred over the blockchain network, finally being converted back to fiat by the receiving intermediary (which could be the same or a different intermediary). The blockchain transfer acts as the 'middle layer' of the 'sandwich', while the conversion of fiat constitutes the 'outer layer'. The main challenge in identifying these transactions is that they are executed by intermediaries, which may bundle multiple transactions together to reduce gas fees. Therefore, some key data (such as the exact transaction amount and the number of users involved) is only available on the intermediary's platform.
B2B Payments:
Business-to-business (B2B) transactions refer to electronic transfers conducted from one business to another via blockchain networks. In our dataset, stablecoin payments refer to transfers between two known institutional EOA wallets, such as transfers from Coinbase to Binance.
Internal B Payments:
Transactions between two EOA wallets of the same institution are labeled as internal B-class transactions.
P2B (or B2P) Payments:
Person-to-business (P2B) or business-to-person (B2P) transactions refer to electronic transfers between individuals and businesses, and the transactions can be bi-directional.
Using this labeling method, we analyzed payment data (limited to EOA-EOA transfers), with the main results summarized in Table 3. The data show that 67% of EOA-EOA transactions are of the P2P type, but they only account for 24% of the total payment volume. This result further indicates that P2P users have lower transfer amounts compared to institutions. Furthermore, one of the highest payment transaction volume categories is internal B-class, indicating that transfers within the same organization constitute a significant proportion. Exploring the specific implications of internal B-class transactions and how to account for them in payment activity analysis remains an interesting question for research.
Table 3: Transaction Distribution by Payment Category
Finally, Figure 4 shows the cumulative distribution function (CDF) of transaction amounts divided by each payment category. The CDF clearly shows that there are significant differences in the distribution of transaction amounts across different categories. Most transactions in EOA-EOA accounts with amounts below $0.1 are of the P2P type, further supporting the idea that these transactions may be driven more by bots and manipulated wallets rather than initiated by the institutions labeled in our dataset. Additionally, the CDF of P2P transactions further supports the notion that most have smaller transaction amounts, while those labeled as B2B and internal B-class transactions show significantly higher transaction amounts. Finally, the CDFs for P2B and B2P transactions fall between P2P and B2B.
Figure 4:
The sample data for this analysis covers transaction records from July 4, 2025, to July 31, 2025.
Figures 5 and 6 show the trends over time for each payment category.
Figure 5 focuses on the weekly changes, showing a consistent adoption trend and growth in weekly transaction volumes across all categories. Table 4 further summarizes the overall changes from August 2024 to August 2025.
Additionally, Figure 6 shows the payment differences between weekdays and weekends, clearly indicating a reduction in payment transaction volumes over weekends. Overall, the usage of payment transactions across all categories shows an increasing trend over time, both on weekdays and weekends.
Figure 5:
Figure 6:
Table 4: Variation in Payment Transaction Volume, Transaction Counts, and Transaction Amounts Over Time
3.3 Concentration of Stablecoin Transactions
In Figure 9, we calculate the concentration of major sending wallets for stablecoins sent through the Ethereum blockchain. It is clear that the transfer volume of most stablecoins is concentrated in a few wallets. During our sample period, the top 1,000 wallets contributed approximately 84% of the transaction volume.
This indicates that despite DeFi and blockchain aiming to support and promote decentralization, they still exhibit highly centralized characteristics in certain aspects.
Figure 9:
The sample data for this analysis covers transaction records from July 4, 2025, to July 31, 2025.
4. Discussion
It is evident that the adoption rate of stablecoins is continuously increasing over time, with their transaction volume and number of transactions more than doubling between August 2024 and August 2025. Estimating the use of stablecoins in payments is a challenging task, and an increasing number of tools are being developed to help improve this estimation. This study utilizes the labeled data provided by Artemis to explore and estimate the use of stablecoin payments recorded on the blockchain (Ethereum).
Our estimation results indicate that stablecoin payments account for 47% of the total transaction volume (35% if internal B-class transactions are excluded). Given our fewer restrictions on payment classifications (primarily based on EOA-EOA transfers), this estimate can be seen as an upper limit. However, researchers can further apply filtering methods such as minimum and maximum transaction amounts according to their research objectives. For instance, setting a minimum amount threshold of $0.1 could exclude the low-amount transaction manipulations mentioned in Section 3.1.
In Section 3.2, by further categorizing payment transactions using Artemis label data into P2P, B2B, P2B, B2P, and internal B-class transactions, we find that P2P payments only account for 23.7% of the total payment transaction volume (across all raw data) or 11.3% (excluding internal B-class transactions). Previous research indicated that P2P payments account for about 25% of stablecoin payments, and our findings are consistent with this.
Finally, in Section 3.3, we observe that, in terms of transaction volume, most stablecoin transactions are concentrated in the top 1,000 wallets. This raises an interesting question: is the use of stablecoins developing as a payment tool driven by intermediaries and large companies, or as a settlement tool for P2P transactions? Time will reveal the answer.
References
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<2> Awrey, D., Jackson, H. E., & Massad, T. G. (2025). Stable Foundations: Towards a Robust and Bipartisan Approach to Stablecoin Legislation. Available at SSRN 5197044.
<3> Halaburda, H., Livshits, B., & Yaish, A. (2025). Platform building with fake consumers: On double dippers and airdrop farmers. NYU Stern School of Business Research Paper Forthcoming.
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