One Token, Countless Trails: Unraveling the Complexities of Cryptocurrency Money Laundering

**Rewritten Text in English:**

In today’s world, where digital assets and decentralized platforms are becoming increasingly widespread, criminals are employing more sophisticated methods for money laundering. One such tactic involves breaking down large sums into smaller transfers across numerous wallets.

By 2025, this strategy gained significant traction, making it challenging even for seasoned analysts and blockchain experts to trace the original sources of funds and pinpoint the final cash-out locations.

How is it possible that millions can be hidden behind countless transfers of $50? What tools can help navigate this cryptocurrency chaos? Is it even feasible to trace where the digital trail ends? Gregory Osipov, the head of investigations at «Shard,» sheds light on these questions.

Microtransactions refer to small transfers, typically involving just a few dollars. However, when conducted in bulk, these transactions can amount to tens or hundreds of thousands of dollars. Fraudsters divide their assets into numerous transactions to obscure the origin of the funds and complicate tracking efforts.

This scheme unfolds in four steps:

Many cryptocurrency exchanges and services impose limits that trigger additional verification processes when exceeded (for instance, for transfers over $10,000). These measures may include risk assessments, blocking. transfers until circumstances are clarified, or requesting documentation to confirm the origins of the funds. By fragmenting the transfers, criminals avoid automatic “flags” and keep transactions within a “safe” range.

A large volume of minor transfers complicates the analysis of transaction chains. It becomes especially challenging to reconstruct the flow of funds when each segment of the transaction passes through different DeFi protocols or cross-chain bridges. This creates “noise” in the data, making it difficult to assemble a coherent picture.

Furthermore, this pattern creates an illusion of normal user activity. By distributing funds across numerous addresses and transactions, perpetrators mask themselves among millions of legitimate users on crypto exchanges, NFT platforms, and DeFi networks. This significantly decreases the likelihood of monitoring systems identifying the transfer as suspicious.

Microtransactions induce a sense of chaos: hundreds of small transfers, dozens of wallets, multiple exchange services, and NFT platforms. However, modern analytical tools are becoming increasingly precise, enabling the detection of connections between seemingly unrelated elements.

A crucial method involves constructing a graph of funds flow. In this model, each address acts as a node, and each transaction serves as a connection between them. Even if the total amount is split into hundreds of micro-transfers, clustering, temporal dependency analysis, and joint control assessment over addresses allow the tracing of the route from the initial point to the final recipient.

In Russia, investigations into cryptocurrency crimes are also becoming more technologically advanced. The use of off-chain data—such as KYC information, IP addresses, law enforcement data, and information from open sources—plays a vital role here. When combined with on-chain analytics, this helps form a comprehensive view of fund movements and, in some cases, de-anonymizes cryptocurrency wallet owners.

Since the early 2020s, DeFi and NFTs have emerged as venues for money laundering for some individuals. Decentralized platforms offer swift and anonymous operations without intermediaries, aiding criminals in obscuring the traces of their illegitimately obtained assets.

By 2025, numerous schemes utilizing DeFi protocols and NFT markets are linked to evading the honest use of cryptocurrencies. According to Chainalysis data, in 2023, criminals stole $1.1 billion through attacks on DeFi protocols—down 64% from 2022, which saw losses totaling $3.1 billion. Let’s examine some of the primary tools used by fraudsters.

**Utilization of DEX (Decentralized Exchanges).** Fraudsters exploit DEX platforms like Uniswap, PancakeSwap, and SushiSwap to exchange one asset for another. Typically, this occurs through a chain of exchanges involving different coins: for example, converting ETH to DAI, then DAI to USDT, before withdrawing the stablecoin to the BSC network. These transactions fragment the flow into separate parts, making each one challenging to trace.

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

**Transaction Mixing Protocols (Mixers).** Crypto mixers like Tornado Cash allow users to obscure the source of their funds. Even if the amounts involved are small, tracking who ultimately receives funds becomes daunting after running the cryptocurrencies through mixers, especially if an extended time-lapse exists between sending to the mixer and receiving the funds.

**NFTs as a Money Laundering Tool.** It is noteworthy that NFTs are increasingly used to obscure the origins of funds: criminals create tokens and then buy them back from themselves using another wallet—this constitutes a classic wash trading scheme, in which cryptocurrency is legitimized as “income from digital art.” Furthermore, NFTs enable the transfer of funds into a different class of assets that may not always fall under financial regulation, complicating transaction identification and reducing the likelihood of automatic detection of suspicious activity.

Comparing microtransactions across different blockchains is one of the most labor-intensive challenges in cryptocurrency investigations. Criminals increasingly fragment stolen funds and distribute them across multiple networks like Ethereum, TRON, BNB Chain, Avalanche, and Polygon. This method allows them to exploit the peculiarities of each network to obscure their tracks.

Let’s explore the key reasons why tracking microtransactions between blockchains poses such a complex challenge.

First, there is often no unified method to link a transaction in one network with a transaction in another. Unique identifiers and wallet addresses do not overlap between chains, so when moving from one network to another (for example, via a bridge or decentralized service), the continuity of the chain is disrupted. For instance, a user may send 0.001 ETH to a bridge and receive 0.001 wETH on the Polygon network. Visually, these are two distinct events with different addresses and hashes.

Second, most cross-chain transactions occur through bridges. Bridges often utilize wrapped tokens, such as wETH and wBTC, which are different assets in the recipient network. This not only conceals the origin of the funds but also alters the token’s structure, adding layers of complexity.

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

The less transparent a blockchain is, the more challenging it becomes to track transactions, especially if certain microtransactions vanish into closed networks or are concealed using specialized protocols.

Microtransactions are often employed in money laundering schemes, simulating the appearance of legitimate activities while hiding the connection between the sender and the recipient of funds. Although these transactions may appear small and inconspicuous, certain behavioral patterns recur frequently enough to be indicative of suspicious activity. Analysts, law enforcement, and cybersecurity experts apply methods explained below to unveil detailed schemes for laundering funds.

By 2025, microtransactions have become part of intricate schemes for laundering and moving digital assets. Criminals have learned to adapt to new techniques for analyzing crypto transactions and employ various strategies for washing stolen assets.

Nonetheless, the crypto industry continues to evolve. New analytical tools, such as graph models, machine learning, and the integration of offline data (KYC, IP addresses, OSINT network logs, etc.), are emerging. These technologies aid in reconstructing real relationships between participants in blockchain chains.

Typical behaviors exhibited by fraudsters, such as frequent micro-transfers, circular transactions, disposable wallets, and wash trading, are increasingly being captured within monitoring systems. However, without international cooperation and access to critically important data (personal information, including KYC), combating crypto crime will remain a formidable challenge.

Today, the effectiveness of cryptocurrency investigations hinges not only on technology but also on the ability to understand the behavior of perpetrators behind transactions. One token can leave numerous traces—the key is to ensure that someone recognizes and identifies them in a timely manner.