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In 2024, UK financial institutions reported 34,476 cases with behavior indicative of money muling to the National Fraud Database, making it one of the most common forms of account misuse. Public research commissioned by the UK government paints an even more concerning picture: only around one in four people understand that money muling is illegal, and recruiters have been found targeting children as young as eleven through social media and online gaming platforms.
The conclusion is clear. Money muling remains a major challenge for anti-money laundering (AML) programs. But how much can strong ID verification really do to stop it?
This article takes a practical look at where ID verification helps reduce money mule fraud, where its limits lie, and how it fits into a broader prevention strategy, using real-world cases to illustrate the mechanics of modern money muling schemes.
What is money muling?
One of the clearest official definitions comes from the UK Home Office’s Money Mule and Financial Exploitation Action Plan. It defines money muling as a situation in which an individual moves criminal proceeds on behalf of others, typically in exchange for money, goods, or other benefits.
Funds may pass through personal bank accounts, business accounts, crypto wallets, or cash withdrawals. In some cases, the mule hands over full control of their account so transfers appear to originate from a legitimate customer.
In simple terms:
- What is money muling?
Using seemingly legitimate accounts to move or layer criminal funds. - What is a money mule?
Any person whose account, credentials, or identity is used for that purpose, whether knowingly, under pressure, or without full awareness.
Money mule networks are closely linked to financial exploitation. Vulnerable individuals, including minors, may be manipulated or coerced into participating without understanding the legal consequences.
While money muling is defined by the movement of funds for others, it is important to distinguish it from related but distinct techniques.
Money muling vs. structuring vs. smurfing
Structuring, as described in the FFIEC BSA/AML Manual, involves breaking transactions into smaller amounts to avoid reporting thresholds, such as Currency Transaction Reports. Any transaction pattern designed to evade those thresholds qualifies, even if no single transaction crosses the limit.
Smurfing is a specific form of structuring. A large amount of illicit money is split among many individuals (“smurfs”), each of whom deposits or transfers small sums. The activity only becomes visible when the transactions are viewed collectively.
Money muling can involve structuring or smurfing, but it does not require either. Many modern money mule schemes move value through instant payments, cards, and crypto exchanges, without using cash at all. They still qualify as money mule fraud because criminal proceeds are being routed through networks of third-party accounts.
In practice:
- Money muling focuses on using other people’s accounts to move funds
- Structuring focuses on avoiding reporting thresholds
- Smurfing scales structuring across many participants
How money muling works in real life
Despite constant variation, most money mule schemes follow a similar three-stage pattern.
1. Recruitment and social engineering
Most money mule scams begin in informal online spaces. Social media platforms, messaging apps, and gaming communities are common entry points.
Targets are offered “easy jobs,” “payment testing roles,” or help with moving money for a foreign business. In romance scams, the request may be framed as helping a trusted partner. At this stage, the recruit typically believes they are assisting someone, not committing a crime.
Low awareness plays a major role. Many people have encountered money muling offers without recognizing them as illegal.
2. Account acquisition and control
Once recruited, criminals need access to accounts. This usually happens in one of two ways.
In some schemes, recruits are instructed to open new accounts at multiple financial institutions, often digital-first banks with remote onboarding. A Romanian network dismantled in 2025 recruited hundreds of people to travel to the UK to open accounts that later moved the proceeds of online fraud worth at least three million euros. Forged residence permits and local SIM cards were used to improve onboarding success.
In other cases, recruits surrender access to existing accounts. Criminals receive login credentials, cards, and device codes, while the real account holder remains available to answer calls or complete biometric prompts. From a monitoring perspective, the account suddenly begins receiving funds from unrelated parties and transferring them onward at speed.
3. Movement of funds through the network
Once accounts are in place, funds are layered through multiple steps.
The Romanian scheme combined compromised business emails with fake invoices. Funds entered first-tier mule accounts and were quickly redistributed across a wider network.
A Spanish crypto investment fraud ring dismantled in 2025 followed a similar pattern at scale. Fraudsters collected deposits from thousands of victims, then laundered approximately 460 million euros through corporate accounts and exchanges. Straw directors and controlled companies acted as money mules in legal form.
Typically:
- First-tier accounts receive funds from fraud victims or upstream crimes
• Funds are rapidly redistributed across multiple accounts, often internationally
• Later-stage accounts focus on cashing out, buying assets, or converting to crypto
By the time an account is frozen, most of the value has already moved on.
Can ID verification stop money mule fraud?
The honest answer is yes — but only to a point.
ID verification plays its strongest role at the beginning of the fraud chain and at selected high-risk moments later on. It does not detect transaction behavior on its own, but it directly affects how easy money mule schemes are to scale.
Document verification and resistance to synthetic identities
Strong document verification goes beyond basic OCR and visual checks. It includes full-page capture, security feature analysis, and chip reading where available.
For money muling prevention, this matters in several ways:
- It makes mass account opening with forged or altered documents harder
• It raises the cost of opening accounts using stolen identities
• It produces structured, high-quality identity data that can feed downstream analytics
In the Romanian case, forged residence permits were part of the operation. Institutions with advanced document checks are better positioned to block such attempts early.
Document verification alone does not identify money mules, but it reduces one of the main supply channels for mule accounts.
Biometrics, context, and account control
Biometric verification ties a real person to an identity document at onboarding. Reused later, it strengthens control over sensitive actions.
In money muling scenarios, biometrics help by:
- Supporting step-up authentication when behavior suddenly changes
• Making it harder to operate accounts using only stolen credentials
• Distinguishing between shared environments and centralized account control
Biometrics do not reveal intent. A person who believes they are doing legitimate work may pass every check. However, tying high-risk actions to strong identity evidence limits how easily mule recruiters can hide behind weak authentication.
ID data as input for mule detection systems
A less visible benefit of ID verification is the data it generates.
Complete IDV processes produce verified attributes, risk indicators, device context, and audit-ready evidence. Shared utilities and sector-level tools combine this data with transaction monitoring to identify clusters of mule accounts and recurring recruitment patterns.
When identity data is consistent and reliable, money muling prevention becomes a collective capability rather than a single-institution problem.
The limits of ID verification
If ID verification alone could solve money mule fraud, the problem would already be fading. Several structural limitations prevent that.
Money mules with genuine identities
Many money mules use their own real documents and willingly complete biometric checks. At onboarding:
- The document is genuine
• The biometric sample matches
• The customer profile looks normal
ID verification confirms identity, not motivation. In these cases, the checks work exactly as designed.
Rented identities vs. synthetic identities
While synthetic identities still matter, evidence suggests that rented identities are more common in money muling. Recruiting real people is often cheaper and more reliable for criminals than maintaining sophisticated forgery operations.
Improving document forensics remains essential, but it will not eliminate money muling on its own.
Building a money muling prevention stack with ID verification
Financial institutions should treat ID verification as a foundation, not a standalone control.
First, use IDV to ensure high-quality identity data. Document quality indicators, biometric outcomes, and device context should feed an internal identity graph that supports transaction monitoring and data sharing.
Second, link ID checks to risk signals. When transaction patterns change suddenly, biometric step-ups and device checks provide valuable context.
Third, combine identity data with behavioral and network analytics. Graph analysis, typology-based rules, and central fraud registers rely on clean identity anchors to detect mule networks.
Finally, support law enforcement collaboration. Detailed identity and transaction evidence has been critical in dismantling large mule networks across Europe.
How Regula supports money muling prevention
Money muling persists because criminals can still recruit vulnerable people and move stolen funds faster than traditional controls can respond.
While ID verification is not sufficient on its own, it remains a critical layer. It raises barriers for synthetic identities, disrupts identity theft-based schemes, and provides the evidence needed for analytics and investigations.
Regula IDV Platform supports this approach through an end-to-end framework for identity verification and user identity lifecycle management:
- Document verification using a database of over 16,000 document templates from 254 countries and territories
• Advanced biometric verification with face matching and liveness detection
• Flexible workflows for KYC, onboarding, and step-up authentication
• AML and PEP screening with custom watch lists
• Structured identity data and audit-ready evidence
• Seamless integration with existing systems
By strengthening onboarding, supporting re-verification when behavior changes, and feeding clean identity data into fraud and AML systems, Regula helps financial institutions reduce exposure to money mule fraud.