As digital assets and decentralised platforms proliferate, criminals are refining money-laundering schemes. One tactic is to split large sums into tiny transfers across many wallets.
By 2025 the method has become widespread, taxing even seasoned blockchain analysts who try to identify the true sources of funds and the final cash-out venues.
How do millions hide behind hundreds of $50 transfers? Which tools make sense of this crypto-chaos? And can a digital trail ever be followed to its end? Grigory Osipov, director of investigations at Shard, explains.
How microtransactions are used to conceal the origin of funds
Microtransactions are small transfers, typically worth a few dollars. Used at scale, such flows can add up to tens or hundreds of thousands of dollars. Fraudsters break assets into numerous transactions to mask provenance and frustrate tracing.
The scheme unfolds in four steps:
- Fragmentation. A large sum—say, 10 BTC—is split into many small transfers, for example 0.01–0.1 BTC apiece.
- Dispersal. Funds are sent to multiple wallets that may be under common control but appear distinct.
- Recirculation. Micropayments bounce between addresses, sometimes via smart contracts or decentralised exchanges.
- Consolidation. After “laundering”, the small sums are reassembled—often in other currencies—on new addresses or on centralised exchanges with laxer controls.
Many cryptocurrency exchanges and services set thresholds that trigger enhanced checks (for example, transfers above $10,000). Measures can include risk scoring, holding transactions pending review, or requesting documents to prove source of funds. Splitting helps avoid automated flags and keeps transfers within a “safe” range.
Large volumes of tiny transfers complicate analysis of transaction chains—especially when each fragment passes through assorted DeFi protocols or cross-chain bridges. The result is data “noise” that obscures the bigger picture.
The pattern also mimics ordinary user behaviour. By spreading funds across dozens of addresses and transactions, perpetrators hide among millions of genuine users on exchanges, NFT platforms and DeFi networks, reducing the odds that monitoring systems will flag activity as suspicious.
How analysts reconstruct links between microtransactions
Microtransactions create apparent chaos: hundreds of tiny transfers, dozens of wallets, a mix of swapping services and NFT venues. Yet modern tools grow more precise, surfacing links between elements that look disconnected.
The core technique is to build a money-flow graph. Each address is a node; each transaction, an edge. Even if a sum is shattered into a hundred micropayments, clustering, temporal analysis and assessments of joint control can reconstruct the route from origin to ultimate recipient.
In Russia, cryptocurrency investigations are also becoming more technological. Off-chain data play a vital role—such as KYC information, IP addresses, law-enforcement records and open-source intelligence. Combined with on-chain analytics, these inputs help form a coherent picture of fund movements and, in some cases, deanonymise wallet owners.
How DeFi platforms and NFTs are used to muddy the trail
Since the early 2020s some have used DeFi and NFTs to launder money. Decentralised platforms offer speed and pseudonymity without intermediaries, helping offenders obfuscate assets obtained dishonestly.
By 2025 numerous schemes run through DeFi protocols and NFT marketplaces. According to Chainalysis, in 2023 attackers stole $1.1bn via DeFi protocol hacks—down 64% from 2022, when losses reached $3.1bn. The main tools include:
Using DEXs (decentralised exchanges). Fraudsters swap assets on DEXs such as Uniswap, PancakeSwap and SushiSwap, often via chains of trades: for example, exchanging ETH for DAI, DAI for USDT, then sending the stablecoin to BSC. Such sequences break the flow into segments that are hard to trace.
Example: an address receives $10,000 in ETH, splits it into 20 transfers of $500, swaps each portion into different tokens via DEXs, then bridges them into other networks. By combining DEXs with fragmentation, the perpetrator greatly complicates forensic analysis.
Mixing protocols. Crypto mixers such as Tornado Cash pool tokens from multiple users, masking the source of funds. Even with modest sums and few transactions, once funds pass through mixers it becomes hard to identify the real recipients—especially when a long delay separates deposit and withdrawal.
NFTs as a laundering tool. NFTs are increasingly used to obfuscate provenance: offenders mint tokens and then buy them from themselves using another wallet—a classic “wash trading” scheme that reclassifies crypto as “income from digital art”. NFTs also shift value into an asset class not always covered by financial rules, complicating detection and reducing the likelihood of automatic flags.
Why reconciling micro‑payments across blockchains is hard
Matching micropayments across blockchains is among the most labour‑intensive tasks in crypto investigations. Offenders increasingly split stolen funds and scatter them across networks such as Ethereum, TRON, BNB Chain, Avalanche, Polygon and others—exploiting each network’s quirks to blur the trail.
The main reasons:
First, there is rarely a single way to link a transaction in one network to one in another. Unique identifiers and wallet addresses do not overlap across chains. Moving from one network to another—via a bridge or a decentralised service—breaks continuity. For example, a user sends 0.001 ETH to a bridge and receives 0.001 wETH in Polygon. Visually these are two different events with distinct addresses and hashes.
Second, most cross-chain transfers pass through bridges that issue wrapped tokens such as wETH and wBTC in the destination network. That not only hides the source but changes the asset’s structure, adding layers of complexity.
Third, networks vary in transparency. Ethereum and Bitcoin can be probed with public nodes and APIs. Others, such as Zcash and Monero, are closed or require special tooling or permissions to access data.
The less transparent a blockchain, the harder it is to trace transactions—especially when some micropayments disappear into privacy networks or are hidden by specialised protocols.
Behavioural patterns that often betray microtransaction laundering
Microtransactions often underpin laundering schemes by simulating legitimate activity and severing the link between sender and beneficiary. Though individually small, certain behavioural patterns recur so reliably that they serve as red flags. Analysts, law‑enforcement bodies and security specialists use the methods below to uncover detailed laundering set‑ups.
- Hyper‑regular, templated transfers. Identical, frequent payments in similar amounts at short intervals are a hallmark. Such activity makes little economic sense for ordinary users. Example: one address sends 0.0015 ETH every seven seconds to 100 different addresses over an hour, with no context or return flows—suggesting an automated distribution bot.
- Cyclical routes and return flows. Part of the laundered money sometimes returns to source addresses to simulate user activity—often to legitimise funds on centralised exchanges. Example: A → B → C → A with intermediate splits into tiny payments and partial returns, creating the illusion of DeFi income.
- Heavy use of bridges and DeFi. Transfers that hop chains and services in small amounts and large volumes often signal efforts to evade oversight, as fees overwhelm any rational economic purpose. For instance: a 0.001 ETH transfer, swap to DAI on Uniswap, bridge to BNB Chain, swap back, buy an NFT, then flip it quickly.
- Disposable addresses. “Burner” wallets created for one or two operations and then abandoned are common. When many such addresses cluster in a single flow, suspicion rises. Example: over 100 addresses each receive about $40 within 30 minutes, after which all funds are swept to a new wallet and sent to an exchange.
- Deviations from an address’s usual profile. Behavioural‑profiling systems flag anomalies. If a storage‑only address suddenly begins making many small DeFi transfers, that shift is suspicious.
- Unusual hours and geographic mismatch. Odd activity times and location discrepancies raise alarms. For example, bursts of small payments at 3–4am, or logins from IP addresses unconnected to a verified account’s location (on KYC’d exchanges), often indicate automated laundering bots.
Conclusion
In 2025 microtransactions are integral to complex schemes for laundering and moving digital assets. Criminals adapt to new forensic methods, combining techniques to wash stolen funds.
Yet the industry is advancing. New tools—graph models, machine learning and the use of offline data (KYC, IP, network logs, OSINT, and more)—are helping to rebuild real relationships between actors in blockchain chains.
Typical behaviours—frequent micro‑transfers, circular transactions, disposable wallets and wash trading—are increasingly caught by monitoring systems. Still, without international co‑operation and access to critical data (including KYC), fighting crypto‑crime remains arduous.
The effectiveness of cryptocurrency investigations today depends not only on technology but on understanding the behaviour behind the transactions. One token can leave many traces—the key is that someone spots and interprets them in time.