How Scammers Use Multiple Wallets — and How to Connect Them

When someone steals cryptocurrency, the stolen funds rarely sit in a single wallet. Instead, scammers operate complex networks of wallets, splitting, merging, and routing funds through dozens or even hundreds of addresses to obscure the trail. Understanding how these networks work -- and how to unravel them -- is one of the most powerful tools available to victims, investigators, and compliance professionals.

This is the science of address clustering: identifying which wallet addresses are controlled by the same entity, even when there is no obvious direct connection between them. It is how law enforcement has recovered billions in stolen crypto, and it is how ScamLens helps everyday users understand the true risk of any wallet they encounter.

Why Scammers Use Multiple Wallets

Using a single wallet for criminal activity would be like using a single bank account for money laundering -- it creates a clear, easy-to-follow trail. Sophisticated scammers use multiple wallets for several strategic reasons:

Obfuscation

By splitting stolen funds across many addresses, the scammer makes it harder for any single investigator to see the full picture. If you only look at one wallet, you might see a small transaction that seems harmless. Only by connecting all the wallets together does the true scale of the operation become visible.

Layering

This is a classic money laundering technique adapted for blockchain. Funds move through multiple "layers" of wallets, each adding distance between the original theft and the final destination. The more hops between source and destination, the harder it is to trace.

Mixing and Breaking the Chain

Some wallets in the network serve specifically as "mixing points" where funds from multiple sources are combined before being redistributed. This makes it mathematically harder to attribute specific funds to a specific source.

Exchange Deposit Splitting

Scammers often split funds into amounts just below exchange reporting thresholds before depositing them, similar to the traditional banking concept of "structuring" or "smurfing."

Operational Security

Different wallets serve different purposes: collection wallets receive from victims, transit wallets move funds quickly, storage wallets hold funds for long periods, and cash-out wallets interact with exchanges or OTC desks.

The 6 Core Address Clustering Techniques

Address clustering is the set of techniques used to determine that multiple blockchain addresses are controlled by the same person or organization. Here are the six most important methods:

1. Common Input Ownership (Bitcoin-Specific)

In Bitcoin, a single transaction can have multiple inputs from different addresses. If two addresses appear as inputs in the same transaction, they are almost certainly controlled by the same entity -- because you need the private keys of all input addresses to sign the transaction.

This is the oldest and most reliable clustering heuristic for Bitcoin. It was first described in the 2013 paper by Meiklejohn et al. and remains the foundation of Bitcoin forensics.

Example: If address A and address B both appear as inputs in a transaction that sends Bitcoin to address C, then A and B are controlled by the same entity.

2. Funding Chain Analysis

On account-based blockchains like Ethereum and Tron, the funding chain technique looks at where a wallet got its initial funds. If a new wallet's first transaction is receiving ETH from another wallet, and that wallet was funded by yet another wallet, the entire chain likely belongs to the same operator.

This is especially powerful for detecting scam networks because scammers typically create batches of wallets and fund them all from a single source.

How ScamLens uses this: When you check a wallet on ScamLens, the system automatically traces the funding chain back to identify the original source of funds, flagging any connections to known malicious addresses.

3. Gas Station Pattern (Ethereum/EVM)

On EVM chains, every transaction requires gas (transaction fees) paid in the native token (ETH, BNB, MATIC, etc.). When a scammer operates many wallets, they need to fund each one with gas. A "gas station" is an address that sends small amounts of native tokens to many other addresses that then engage in suspicious activity.

The gas station pattern is highly reliable because it reveals operational infrastructure. Even if the scammer uses different wallets for different victims, they often use the same gas station to fund all of them.

Detection signature: One address sends small, similar-sized transactions (just enough for gas) to multiple addresses that subsequently interact with the same contract or perform similar suspicious transactions.

4. Tron Energy Activation Pattern

Tron uses a unique resource model where transactions consume "energy" and "bandwidth" instead of gas in the traditional sense. New Tron addresses must be "activated" with a minimum TRX deposit. Scammers operating on Tron often activate batches of addresses from the same source wallet.

This pattern is especially relevant because Tron is the most popular blockchain for USDT transfers and is heavily used in pig butchering and romance scams targeting Asian markets.

Detection signature: A single address activates (first-ever TRX transfer to) multiple new addresses within a short time window, and those new addresses subsequently receive USDT from different sources.

5. Temporal Clustering

Wallets controlled by the same entity often show synchronized behavior. They may be created around the same time, become active during the same hours, or go dormant simultaneously. By analyzing timing patterns across many wallets, investigators can identify clusters that behave as a coordinated unit.

What to look for: Multiple wallets that were created within the same block or time window, show activity during the same hours, and go dormant at the same time.

6. Behavioral Fingerprinting

Every wallet operator has behavioral patterns -- preferred transaction amounts, timing habits, gas price preferences, contract interaction patterns. These "fingerprints" can be used to link wallets even when there is no direct on-chain connection.

Advanced technique: Machine learning models can be trained on known scam wallet behaviors and then applied to identify new wallets with similar patterns. ScamLens uses behavioral fingerprinting as part of its risk scoring algorithm.

How ScamLens Maps Scam Networks

ScamLens applies these clustering techniques automatically when you analyze any wallet address. Here is what the system does:

1. Trace funding origins: Follows the money backward to find where the wallet got its initial funds
2. Identify gas stations: Detects shared gas-funding infrastructure across multiple wallets
3. Map transaction counterparties: Identifies all addresses that have sent to or received from the target wallet
4. Cross-reference threat intelligence: Checks every connected address against GoPlus, Etherscan labels, OFAC sanctions, and OpenSanctions databases
5. Apply behavioral analysis: Compares transaction patterns against known scam signatures
6. Generate network visualization: Produces a visual map of connected wallets with risk ratings for each

The result is a comprehensive picture of not just whether a single wallet is risky, but whether it is part of a larger criminal network. Try ScamLens Wallet Intelligence to see the full network analysis for any address.

Real-World Case Study: Mapping a Pig Butchering Network

A victim reported sending $15,000 in USDT (Tron) to a wallet address provided by a "crypto investment advisor" they met on a dating app. Here is what ScamLens clustering analysis revealed:

Initial wallet (W1): The address the victim sent funds to

• Created 18 days before the victim's deposit
• Received USDT from 23 unique addresses (other victims)
• Forwarded all funds within 2 hours to wallet W2

Transit wallet (W2): First hop

• Received from W1 and 4 other collection wallets
• Split funds into 3 outgoing transactions to W3, W4, W5

Gas station (G1): Infrastructure

• Activated W1 and 11 other collection wallets
• All activated wallets received victim funds
• G1 was funded from a Binance withdrawal

Cash-out wallets (W3-W5):

• W3: Deposited to a known OTC desk
• W4: Swapped to USDC via DEX, then bridged to Ethereum
• W5: Sent to Tornado Cash

Total network size: 47 wallets, 23 confirmed victims, approximately $890,000 in stolen funds traced.

Without clustering analysis, each victim would only see their own transaction to a single wallet. With ScamLens, the entire operation becomes visible, providing crucial evidence for law enforcement.

Limitations of Address Clustering

Address clustering is powerful but not perfect. Important limitations include:

• False positives: Shared infrastructure (like exchange hot wallets) can create false connections between unrelated users
• Privacy tools: Mixers, privacy coins, and cross-chain bridges can break clustering links
• Sophistication level: The most advanced operators use unique infrastructure for each operation
• Cross-chain complexity: Tracking funds that bridge between blockchains requires multi-chain analysis capabilities

ScamLens addresses these limitations by combining clustering with other risk signals (sanctions screening, behavioral analysis, threat intelligence) to produce a holistic risk assessment.

What You Can Do

If you are a potential victim:

1. Check before you send: Run the wallet through ScamLens before transferring any funds
2. Report scam wallets: Report suspicious addresses on ScamLens to help protect other users
3. Document everything: If you have already been scammed, preserve all transaction hashes and wallet addresses

If you are investigating a scam:

1. Start with the known scam address
2. Use ScamLens to map the connected network
3. Identify infrastructure wallets (gas stations, funding sources)
4. Track cash-out points (exchanges, OTC desks)
5. Report findings to law enforcement with the full network map

Conclusion

Scammers use multiple wallets because they believe complexity provides safety. But blockchain's permanent, public ledger means that every transaction, every connection, and every pattern is preserved forever. Address clustering turns the scammer's own complexity against them, revealing the hidden connections that expose the entire network.

ScamLens makes these professional-grade investigation techniques available to everyone. Whether you are checking a single wallet before sending funds or investigating a full scam network, ScamLens Wallet Intelligence gives you the tools to see what scammers are trying to hide.

The blockchain never forgets. Neither should we.