One Token, Countless Trails: Unraveling Microtransactions in the Crypto Underworld

Today, as the number of digital assets and decentralized platforms continues to grow, criminals are employing increasingly sophisticated methods for money laundering. One such tactic involves breaking down large sums into smaller transactions across various wallets.

By 2025, this scheme gained significant popularity, making it challenging for even seasoned analysts and blockchain experts to trace the true sources of funding and identify final withdrawal points.

How is it possible that millions can be hidden behind hundreds of $50 transactions? What tools are available to untangle this cryptocurrency chaos? Is it even possible to track where the digital trail ends? Grigory Osipov, the head of investigations at “Shard,” provides insight.

Microtransactions refer to small transfers, typically in the range of a few dollars. However, when used en masse, these operations can total tens or even hundreds of thousands of dollars. Fraudsters fragment assets into numerous transactions to obscure the origin of the funds and complicate their tracking.

The scheme unfolds in four steps:

Many cryptocurrency exchanges and services impose limits, triggering additional verification procedures when exceeded (for example, with transfers above $10,000). These measures may include risk assessment analysis, halting the transaction until further details are clarified, or requiring documentation to confirm the origin of the funds. Fragmentation helps evade automatic “flags” and keeps transactions within a «safe» range.

A large number of small transactions complicates the analysis of the transaction chain. It becomes particularly challenging to trace the flow of funds if each transaction segment passes through different DeFi protocols or cross-chain bridges. This generates «noise» in the data and makes constructing a comprehensive picture difficult.

Additionally, this scheme creates the illusion of typical user activity. By distributing funds across dozens of addresses and transactions, perpetrators blend in among millions of legitimate users on cryptocurrency exchanges, NFT platforms, and in DeFi networks. This reduces the likelihood that monitoring systems will flag the transactions as suspicious.

Microtransactions create chaos, resulting in hundreds of minor transfers, multiple wallets, various exchange services, and NFT marketplaces. However, modern analytical tools are becoming increasingly precise, allowing investigators to uncover connections between seemingly disparate elements.

A key method is constructing a graph of fund movement, where each address acts as a node and each transaction constitutes a link between them. Even if a sum is broken down into numerous micro-transfers, clustering, temporal analysis, and joint control assessments can reconstruct the path from the original source to the final recipient.

In Russia, investigations into cryptocurrency crimes are also becoming more technologically advanced. A critical aspect is the utilization of off-chain data, such as KYC information, IP addresses, data from law enforcement, and information from open sources. Combined with on-chain analytics, this helps create a holistic view of fund movement and, in some cases, can de-anonymize cryptocurrency wallet owners.

Since the early 2020s, DeFi and NFTs have emerged as venues for money laundering. Decentralized platforms offer rapid and anonymous transactions without intermediaries, enabling criminals to obscure the trail of illicitly obtained assets.

By 2025, numerous schemes exploiting DeFi protocols and NFT markets exist to evade lawful cryptocurrency practices. According to Chainalysis data, criminals stole $1.1 billion in 2023 through attacks on DeFi protocols, a decrease of 64% from 2022 when losses totaled $3.1 billion. Let’s examine the main tools utilized by fraudsters.

**Use of DEX (Decentralized Exchanges).** Fraudsters leverage DEXs, such as Uniswap, PancakeSwap, and SushiSwap, to swap one asset for another. This typically occurs via a chain of exchanges involving different coins— for instance, converting ETH to DAI, then DAI to USDT, and subsequently withdrawing the stablecoin to the BSC network. These transactions divide the flow into distinct parts, making each one difficult to trace.

For example, an address receives $10,000 in ETH, splits it into 20 transactions of $500, exchanges each portion for different tokens through a DEX, and then transfers them through bridges to other networks. By employing DEXs and fragmentation techniques, the fraudster significantly complicates the transaction analysis chain.

**Transaction Mixing Protocols (Mixers).** Crypto mixers, like Tornado Cash, allow users to mix tokens, helping obscure the source of funds. Even if the amounts are small, following the cryptocurrency through mixers makes it challenging to trace who ultimately receives the money, especially if a considerable time gap exists between depositing funds in the mixer and their receipt.

**NFTs as a Money Laundering Tool.** It’s noteworthy that NFTs are increasingly utilized as a means to obscure the origin of funds: fraudsters create tokens and then buy them back from another wallet— a classic wash trading scheme that legitimizes cryptocurrency as «income from digital art.” Moreover, NFTs facilitate the transfer of funds into another asset class, often bypassing financial regulations, thereby complicating the identification of transactions and lowering the chance of automated detection of suspicious activities.

Comparing micro-payments across different blockchains remains one of the most labor-intensive challenges in cryptocurrency investigations. Criminals increasingly fragment stolen funds and scatter them across multiple networks such as Ethereum, TRON, BNB Chain, Avalanche, Polygon, and others. This method enables them to exploit the unique features of each network to obscure their tracks.

Let’s discuss the main reasons why tracing microtransactions across blockchains is a complex task.

Firstly, there is often no unified way to link a transaction in one network with a transaction in another. Unique identifiers and wallet addresses do not overlap between chains, so transitioning from one network to another (for example, through a bridge or decentralized service) disrupts the continuity of the chain. For instance, a user sends 0.001 ETH to a bridge and receives 0.001 wETH on the Polygon network. Visually, these are two distinct events with different addresses and hashes.

Secondly, most cross-chain transactions are routed through bridges that frequently use wrapped tokens like wETH and wBTC, which are treated as different assets in the receiving network. This not only obscures the source of the funds but also modifies the token structure, adding layers of confusion.

Thirdly, blockchain networks vary in terms of access levels. For instance, Ethereum and Bitcoin networks can be easily explored using public nodes and APIs, while networks such as Zcash and Monero are closed or require specific tools or permissions to access their data.

The less transparent a blockchain is, the harder it is to track transactions, particularly if micro-payments move into private networks or are concealed using specialized protocols.

Microtransactions are often employed in money laundering schemes, simulating legitimate activities and masking the connection between the sender and the recipient. Although such transactions may appear minor and inconspicuous, certain behavioral patterns manifest frequently enough to be utilized as indicators of suspicious activity. Analysts, law enforcement, and cybersecurity experts employ methods, which we outline below, to unveil detailed money laundering schemes.

By 2025, microtransactions have become integral to complex frameworks for laundering and relocating digital assets. Criminals have learned to adapt to new methods of analyzing cryptocurrency transactions and employ various techniques for washing stolen assets.

Nevertheless, the cryptocurrency industry continues to evolve. New analytical tools are emerging, including graph models, machine learning, and off-chain data processing (KYC, IP addresses, OSINT network logs, etc.). These technologies aid in reconstructing actual relationships between participants in blockchain chains.

Typical fraudulent activities, such as frequent micro-transfers, circular (loop) transactions, disposable wallets, and wash trading, are increasingly being detected in monitoring systems. However, without international cooperation and access to critical data (personal information, including KYC), combatting cryptocurrency crimes remains a daunting challenge.

Today, the effectiveness of cryptocurrency investigations relies not only on technology but also on the understanding of the behavior of perpetrators behind transactions. A single token can leave many traces— the key is for someone to notice and recognize them in time.