What is smurfing in money laundering?
Smurfing in money laundering refers to the process of breaking a large sum of illicit money into many smaller transactions, then using multiple people — called smurfs — to move those funds through banks, payment apps, or digital wallets. By spreading activity across different accounts, devices, and locations, smurfing makes it difficult for financial institutions to detect suspicious patterns and easier for criminals to launder money without drawing attention.
The term comes from The Smurfs cartoon, referencing how many small actors work together to complete a larger task.
In this guide, we break down how smurfing in money laundering works, why it continues to pose a risk in modern banking, and what tactics compliance teams can use to spot it.
How does smurfing in money laundering work?
Smurfing in money laundering works by fragmenting large sums of illicit cash into structured, low-value transactions to evade anti-money laundering (AML) detection.
Under AML regulations, financial institutions must report any single cash transaction over a certain amount — e.g., $10,000 in the United States — to regulators, including the Financial Crimes Enforcement Network (FinCEN).
To bypass regulations, criminals use smurfs, individuals who make multiple deposits below that threshold, such as several transfers of $9,800 spread across different branches or days. The goal is to insert criminal proceeds into the financial system without raising suspicion.
Smurfing occurs in what stage of money laundering?
Smurfing occurs in the placement stage of money laundering, when illicit funds are first introduced into the financial system. Once those structured deposits are complete, the process often continues through two additional stages: layering and integration.
In the layering stage, criminals move funds between accounts, convert them into other currencies or assets, or route them through third parties to obscure their origin. This process makes transactions appear unrelated, reducing traceability.
During integration, bad actors consolidate the laundered money and reintroduce it into the economy — often through business investments, real estate, or luxury purchases — now disguised as legitimate income.
Without dynamic identity verification (IDV) and real-time AML transaction monitoring, these structured movements often go undetected.
Is smurfing still relevant in 2026?
Yes, smurfing is still relevant in 2026. The tactic remains one of the most effective ways criminals can move illicit funds through legitimate systems, because the transactions mimic normal customer behavior, which doesn’t trigger basic detection systems. As a result, smurfing often escapes early detection and enables larger, coordinated operations such as:
Drug trafficking
Transnational crime
For instance, in May 2024, five individuals pleaded guilty to laundering the proceeds of computer fraud through smurfing. They created fake businesses in Florida, opened accounts, and made repeated cash deposits under the $10,000 threshold, transferring over $3.3 million to offshore accounts in China.
In Fiscal Year 2023 alone, US courts reported 1,132 money laundering convictions, representing a 14.3% increase since fiscal year 2019, according to the US Sentencing Commission as shown below.
These figures highlight the growing scale of money laundering tactics, which are also rapidly evolving, as financial criminals adapt to modern banking environments.
Bad actors now use fintech apps, neobanks, and digital wallets to scale smurfing without ever visiting a branch. They open multiple accounts with fake or stolen identities, then move structured deposits across platforms to avoid detection. They also use bots to mimic normal behavior and bypass basic controls.
As digital banking grows, so do the criminal tactics. If compliance teams fail to detect smurfing early, they risk exposing their platforms to regulatory scrutiny, reputational damage, and financial loss.
Structuring and smurfing: Key differences
Structuring and smurfing are closely related money laundering tactics, but they differ in scale, execution, and complexity.
Structuring refers to the act of breaking down a large transaction into smaller ones to stay below reporting thresholds — often executed by a single individual.
Smurfing, on the other hand, expands on this method by involving multiple people and accounts to further obscure the money trail. In essence, smurfing is a form of structuring carried out across multiple accounts and individuals, making it harder to detect at scale.
This table outlines the key differences between structuring and smurfing:
| Aspect | Structuring | Smurfing |
| Number of actors | Typically one person | Multiple people (smurfs) working together |
| Method | Break one transaction into smaller parts | Break and disperse one transaction across identities, accounts, and banks |
| Detection challenge | Patterned transactions in one account | Unlinked activity across many identities and locations |
9 red flags to help you recognize smurfs in money laundering
Smurfs in money laundering often follow recognizable patterns, especially when working as part of a larger fraud ring. Red flags involve coordinated behavior across accounts, devices, or geographies, signaling attempts to bypass monitoring thresholds or identity verification.
Here are nine key red flags that may indicate smurfing in money laundering:
Frequent small deposits just below reporting thresholds
Multiple accounts linked to the same device, IP address, or phone number
Mismatched identity details across accounts with similar behavior
Unusual cash inflows without corresponding spending or transfers
Third-party deposits into personal or newly opened accounts
Cross-border microtransactions with no clear economic rationale
High volume of new accounts created from similar locations
Accounts used only for deposits with no regular usage patterns
Shared traits across accounts previously flagged for suspicious activity
Spotting one red flag may not be sufficient to indicate money laundering smurfs, but seeing several across accounts or devices can help you connect the dots to suspect organized smurfing.
What are the most targeted industries for money laundering and smurfing?
Money laundering and smurfing are common practices across industries that handle high-volume transactions, digital payments, or international transfers. Examples include financial services, real estate, crypto, and ecommerce niches.
Read the table below to understand the industries most likely to be exposed to smurfing in money laundering, and why they’re high-risk targets:
| Industry | How smurfing in money laundering affects this industry |
| iGaming | Smurfs use multiple player accounts to make small deposits that mimic regular betting behavior. |
| Real estate firms | Criminals funnel structured down payments through third parties or shell buyers to avoid scrutiny. |
| Cryptocurrency exchanges | A high volume of low-value transfers and wallet fragmentation enables easy structuring across identities. |
| Fintech platforms | Fast onboarding and peer-to-peer transfers make it easier to spread transactions across unlinked accounts. |
| Ecommerce vendors | Money laundering smurfs place multiple low-value orders using stolen or synthetic identities to disperse illicit funds. |
| Payment processors | Fragmented payments from varied sources can bypass basic AML rules before being consolidated into merchant accounts. |
How smurfing in banking affects institutions
Smurfing in banking strains compliance operations by flooding systems with suspicious activity that requires rigorous and time-intensive investigation. Patterns spread across multiple users, devices, or accounts force teams to manually review alerts, file more reports, and defend decisions during audits.
US financial institutions submitted over 4.7 million Suspicious Activity Reports (SARs) and 20.5 million Currency Transaction Reports (CTRs) in Fiscal Year 2024, according to FinCEN’s Fiscal Year Review 2024. A good majority of this volume comes from structured transactions designed to bypass thresholds.
Without tools that connect identity and transaction data in real time, risk teams face growing workloads, higher false positives, and increased regulatory pressure to detect and escalate smurfing before illicit activity continues.
Smurfing examples
Smurfing adapts to various financial environments. Because of this, this type of money laundering can look different from case to case. Let’s review hypothetical smurfing examples across industries and customer profiles:
By industry
Smurfing often exploits operational blind spots specific to each sector, whether that’s fragmented transaction flows or gaps in vendor due diligence. These examples illustrate how this illicit activity adapts to different industry norms to evade detection:
Financial institutions: A fraud ring may use 20 individuals to deposit $9,500 each across five branches over three days, avoiding the $10,000 threshold.
Real estate: A buyer may funnel structured cash deposits into several third-party accounts that send funds to a title company to hide the true source of the down payment.
Crypto exchanges: Multiple accounts can fund crypto wallets with small, recurring transfers under identity verification limits, then consolidate the funds into a single wallet for withdrawal.
Ecommerce: Fraudsters may use gift card purchases and small online transactions under different customer accounts to launder cash before reselling assets.
Remittance services: Smurfs may send small international transfers from multiple senders to a single recipient, who then converts the funds into cash or crypto.
By customer profile
Money laundering and smurfing tactics also vary by customer segment. Different profiles introduce different risks, making it harder for teams to rely on static rules or thresholds alone.
Retail banking customers: A family network may spread deposits across five personal accounts, keeping each just under $10,000 to avoid detection.
NGOs and charities: A small nonprofit might receive dozens of small “donations” from unknown senders over a week, and then wire the funds abroad with no clear program activity.
Gig economy workers: A ride-share driver’s account might receive 40 small deposits from unrelated names over two weeks, with none tied to known platforms.
Commercial customers: A shell vendor might accept structured payments from multiple businesses to forward the consolidated total to an offshore account.
AML smurfing regulations
Several US and global AML regulations explicitly address structuring tactics like smurfing. These frameworks set thresholds for reporting, mandate enhanced due diligence, and push institutions to adopt real-time monitoring and identity resolution practices.
Key AML smurfing regulations to look out for include:
Bank Secrecy Act (BSA): This law requires financial institutions to file SARs and Currency Transaction Reports (CTRs) for transactions over $10,000 or patterns that appear evasive.
USA PATRIOT Act: This regulation expands customer due diligence requirements and enables broader enforcement for structuring and layering activity.
Financial Action Task Force (FATF) recommendations: Global AML standards that require risk-based monitoring, especially for cross-border payments and high-risk customers.
Failure to comply with laws can lead to AML fines, legal consequences, and reputational damage.
How to detect and prevent smurfing in money laundering
Smurfing often bypasses controls due to outdated AML systems built around rigid, legacy infrastructure. These systems struggle to adapt to modern laundering tactics that exploit digital channels and velocity limits, creating blind spots that let suspicious activity go unnoticed. Organizations frequently see these issues:
Stale detection rules: Many systems rely on fixed thresholds and outdated logic that don’t reflect evolving tactics like low-dollar transfers across digital wallets or third-party apps.
Lack of segmentation: Applying the same monitoring logic across all customer types overlooks key behavioral differences — for example, between gig workers and commercial accounts.
Insufficient customer risk scoring: Without dynamic, identity-linked scoring, institutions fail to adjust monitoring intensity based on user history, device signals, or known fraud markers.
While there’s no silver bullet to stop smurfing in money laundering, there are ways to detect and prevent it before it happens. The best way to avoid falling victim to scheming smurfs is to start connecting behavioral patterns across accounts, identities, time, and other defining factors.
Here are four practical steps you can take to detect and prevent smurfing:
1. Start by verifying identity with layered checks
Prevent money laundering smurfs from creating multiple accounts using fake or synthetic identities by layering identity checks at onboarding.
With Persona’s identity verification solution, you can:
Confirm liveness with government ID and selfie match
Detect reused phone numbers, emails, or IP addresses
Flag inconsistent personal details like names, birthdates, or document types
2. Monitor for behavioral patterns and transaction velocity
Monitor behavioral patterns such as deposit frequency, time-of-day activity, and shared origin points. You should also watch out for velocity red flags, or how quickly deposits are made. Examples include:
5+ deposits under $9,000 within 24 hours
Transactions clustered just before business hours
Funds deposited from multiple accounts are then quickly pooled
Tracking transaction velocity and suspicious behavior patterns helps teams detect potential smurfing without raising concern for legitimate customers, since advanced systems monitor these signals passively, in the background.
3. Link accounts using device intelligence and shared risk signals
Money laundering smurfs often use a small network of devices and locations to control multiple accounts. Use device fingerprinting and IP mapping — the process of linking IP addresses to accounts and activity patterns — to surface hidden linkages.
Linking signals are clues that suggest multiple accounts may be controlled by the same person or group. You should monitor:
Shared devices or browsers across unrelated accounts
Common login times or session lengths
Reuse of recovery credentials (email, phone)
This network-level view helps uncover laundering rings that would go undetected by transaction monitoring alone.
4. Escalate multi-factor risk through automated case workflows
Build automated logic that escalates cases when multiple smurfing signals overlap — for example, velocity red flags, shared devices, and ID mismatches.
A strong AML case management system should:
Combine identity, transaction, and behavioral data in one view
Show the history and connection between linked accounts
Support fast decisions with audit-ready trails
Smurfing risk assessment checklist
Global risk scores reveal how widespread and persistent AML vulnerabilities remain. According to the Basel AML Index 2024, the average global money laundering risk score remains high at 5.04 out of 10, with 77% of countries scoring above the “elevated risk” threshold. These figures underscore the importance of proactive controls, especially for high-risk industries and regions.
To help you detect smurfing in AML, consider this list of questions
Are clients depositing just below reporting thresholds?
Are transactions inconsistent with the customer’s stated occupation or business?
Are multiple accounts linked by shared IP addresses, devices, or personal details?
Do incoming or outgoing flows exceed typical customer patterns?
Are identity details (e.g., phone numbers, addresses) reused across accounts?
Are funds routed through multiple accounts or intermediaries before withdrawal?
Detect smurfing in AML faster with Persona
Legacy AML tools often miss structured laundering activity because they rely on rigid thresholds, siloed signals, or manual review. Persona offers a more adaptive, identity-linked approach to detecting smurfing in AML — helping compliance teams respond faster, stay audit-ready, and scale with confidence.
With Persona, you can:
Layer Know Your Customer (KYC), transaction monitoring, and case management to connect risk signals across the customer lifecycle
Flag structuring behavior with identity-linked thresholds, pattern-based detection, and progressive risk segmentation
Investigate faster with centralized case views that combine behavioral, device, and identity data
Stay ahead of evolving crimes like smurfing in money laundering while meeting compliance mandates. Get started for free or contact us for a demo of our Persona solution today.
FAQs
What is a smurf in money laundering?
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A smurf in money laundering is a person or entity that divides up a large sum of cash into smaller, structured transactions to avoid regulatory scrutiny. Smurfs place these structured deposits into one or more bank accounts, often across institutions, to mask the origin of funds.
Money laundering smurfs are often recruited through fraud rings that target individuals with financial vulnerabilities, such as people in debt, students, or those looking for easy income. In complex schemes, rings use dozens of smurfs to conduct small deposits at different locations, often leveraging synthetic identities or digital wallets.
Why is it called smurfing?
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The term “smurfing” draws from the idea of many small actors, like the blue cartoon Smurf characters, working together to conduct a task that would otherwise raise flags. In financial crime, the term refers to splitting illicit money into many minor transfers to avoid detection.
Over time, the term expanded beyond physical deposits to include digital laundering. Today’s smurfing involves e-wallets, online payments, and fintech apps where criminals disperse money into smaller transactions that blend in with normal customer behavior. These transactions may still raise red flags if linked identities or behaviors are detected.
What is a money mule?
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A money mule is someone who moves illicit funds on behalf of another person, knowingly or unknowingly, often across borders. Their actions obscure the audit trail, making it difficult to trace the original source of the money. While smurfs structure the initial deposits into smaller amounts to avoid detection, money mules help move or layer those funds through various accounts, countries, or payment platforms.
In many smurfing schemes, money mules act as the next link in the laundering chain. After smurfs complete their role by depositing funds below regulatory thresholds, mules transfer the money to other accounts, convert it into crypto or gift cards, or withdraw it as cash. Some individuals even perform both roles, depending on how sophisticated or resource-constrained the fraud ring is.
What is cuckoo smurfing in money laundering?
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Cuckoo smurfing is a variation of smurfing in money laundering that uses legitimate international money transfers to disguise the movement of illicit funds.
For instance, imagine someone wires money to their parents abroad. Instead of the expected transfer landing in the parents' account, a criminal working with a corrupt intermediary deposits illicit funds of the same amount. The original transfer is then rerouted to a different recipient, laundering the dirty money without the sender noticing.
Cuckoo smurfing in money laundering is difficult to detect because the transactions appear legitimate and involve individuals with no direct connection to the crime. Common indicators include:
Third-party deposits: Unknown individuals or entities send funds to an account without a clear business or personal relationship to the recipient.
Mismatch between sender and recipient: The transaction details don’t align — such as a remittance from an international student landing in a corporate or unrelated personal account.
Frequent inbound transfers from unrelated sources: Multiple senders transfer money into the same personal account, suggesting it’s being used to route illicit funds.
These signals often go unnoticed without identity-linked monitoring, behavioral analysis, and link analysis to uncover hidden relationships across accounts.
