Real-time payments have redefined how we move money; it’s now fast, frictionless, and always on. For people and businesses, modern money transfer systems are an undeniable leap forward in convenience. But the flip side is that fraudsters and other criminals exploit the speed and reach of modern payment systems to move stolen funds before detection systems can react.
A sophisticated ecosystem of money mules is what helps disguise the origins of stolen money. The further the funds travel, the colder the trail becomes. This article explores money mule networks and takes a look at how they are the personas that power the ever-expanding underground economy.
A money mule is a person whose account is used to move stolen or illicit funds in a way that hides their true origin and helps criminals stay anonymous. Money mules are a cornerstone of Authorised Push Payment (APP) fraud and wider laundering schemes.
After a victim is deceived into sending funds, for example, to a fake supplier or investment, those proceeds get routed into mule accounts and rapidly dispersed through the financial system. Each transfer adds a layer of distance between the fraudster and the original crime, which makes both recovery and attribution hard.
The people controlling these operations, often called herders, controllers, or recruiters, direct transactions between victims, mules, and drop accounts. Each link in the chain may see only a fragment of the activity. In many cases, mules might never know who else is involved or how the wider network operates.
Not all money mules are the same, though. Their awareness, intent, and level of involvement vary widely. Understanding these distinctions is important to really grasp money mule networks:
These are accounts deliberately created by people to engage in illicit activities. Complicit mules might use fake or stolen identities from the start, with the sole purpose of receiving and moving fraudulent funds. Complicit mules often create and maintain multiple accounts, as was the case when a UK male was sentenced to 4 years and 9 months imprisonment for a £490,000 scam where he took control of customers’ bank accounts using social engineering and transferred funds to his own mule accounts.
These are real people’s legitimate accounts who become unknowingly involved in fraud schemes. Typically, they’re tricked into using their accounts to receive and send stolen funds under the guise of a legitimate request, like helping transfer funds abroad for someone or completing test transfers as part of a remote job. Romance scams are very common here, as seen in the case of a woman sentenced to three years' probation in Illinois earlier this year. The woman got caught up in and became a mule for a romance scam.
Witting mule accounts are held by people who suspect or know something shady is going on but go along with it anyway, often due to financial hardship. They might be students, gig workers, or other financially vulnerable individuals persuaded to “rent out” their accounts.
They don’t always fully understand the illegality of what they’re doing, but they willingly allow their accounts to be used. In fact, according to UK government polling, only 1 in 4 people think that money muling is illegal. However, it is very much an illegal activity. In mid-2025, two Bulgarian men received 18-month prison sentences for being witting money mules in a €61 million fraud scam.
|
Account Type |
Knowledge of illicit use |
Created for Fraud |
Can the account be recovered? |
Suggested actions |
|
Complicit |
Fully aware |
Yes |
No |
Apply account restrictions. Block account |
|
Unwitting |
Unaware |
No |
Yes |
Warn the user. Apply account restrictions |
|
Witting |
Partially aware |
No |
Potentially (with help) |
Notify user of mule activity. Apply account restrictions |
When criminals want to run a fraud scam and launder money through mule accounts, they use different ways of recruiting these mules. Recruitment strategies reflect the diversity of the mule ecosystem. This means you see everything from deception to deliberate criminal collaboration.
At the unwitting mule level, recruitment usually takes the form of social engineering. Fraudsters advertise fake “work-from-home” or “payment processing” jobs, pose as romantic partners in need of help transferring money, or offer attractive investment opportunities. The victim believes they’re helping someone or otherwise transferring funds in a legitimate way. In reality, they’re being used to launder stolen funds and are becoming an unwitting cog in a larger operation.
For those who know what they’re signing up for (complicit) or at least suspect something shady but do it anyway (witting), recruitment takes a more direct form. Controllers use encrypted messaging apps, social media groups, or word-of-mouth referrals to find people willing to sell or lease access to their accounts. Incentives often get framed as low-risk “side hustles”. Or, in the witting mules case, recruiters use other methods designed to appeal to those in financial hardship. Some criminal networks even maintain structured hierarchies, with recruiters earning commissions for each new mule added.
In more organised networks, recruitment extends beyond real people. Criminals use stolen or synthetic identity data to create entirely new mule accounts that appear legitimate. These are often registered using real identification numbers or hybrid profiles, part genuine, part fabricated. Such accounts can pass standard KYC and onboarding checks, allowing funds to flow undetected until deeper link analysis exposes shared infrastructure or behavioral patterns.
Money mule networks are precisely engineered laundering systems designed to fragment, disguise, and ultimately legitimize the proceeds of fraud. Whether the source is an APP scam, phishing campaign, or crypto fraud, the structure of these networks follows a recognisable pattern of collection, layering, and dispersal. They are not random chains of bank accounts or small-scale criminal operations.
Every mule network begins with a collection layer, which is the first destination for stolen funds. These "first-hop" accounts are typically controlled by witting or unwitting mules who receive direct transfers from victims. For example, in an APP scam, the victim sends money straight into a mule’s personal account under the belief they’re paying a legitimate invoice.
Funds are often split across multiple first-hop accounts within minutes to prevent recovery. Fraudsters rely on mobile banking apps, instant payment rails, and peer-to-peer transfer services to execute dozens of transactions almost simultaneously.
Here, the stolen funds move between intermediary mule accounts, sometimes crossing borders or switching between payment platforms, digital wallets, and crypto exchanges. Complicit mules are more common here because the activity is too complex and risky for unaware or hesitant participants.
The layering process introduces deliberate complexity:
This middle tier is the operational backbone of the network, where criminals transform traceable fraud proceeds into funds that appear ordinary and disconnected from their source.
At the final stage, funds exit the network through a variety of cash-out channels:
By this point, the money has travelled through multiple jurisdictions and often dozens of mule accounts. Each hop dilutes the audit trail, reducing recovery chances.
At the top of the hierarchy, professional money launderers control and manage the system like an enterprise. They rarely touch the funds directly. Instead, they coordinate mules through encrypted channels, control digital wallets, and allocate transfers based on risk level or transaction size.
Many employ regional recruiters who maintain pools of active mule accounts ready to receive transfers at short notice. In more sophisticated setups, machine learning tools or automation bots help track balances and automate distribution. This effectively turns mule networks into semi-autonomous financial machines.
Mule networks share several defining traits that make them difficult to detect:
The result is an adaptive system that evolves faster than most legacy monitoring approaches can respond to. Showing the scale of these operations, a worldwide crackdown in 2022 identified over 8,000 money mule accounts and resulted in the arrests of 2469 people.
Every scam, laundering scheme, or payment fraud has one constant: the money mule account. Fraudsters can change narratives, phishing lures, or malware, but at some point the stolen funds must land somewhere. Identifying and classifying that destination before it becomes part of a wider laundering chain is the single most effective way to disrupt financial crime at its source.
Traditional fraud detection systems focus on the transaction or session rather than the account. They flag suspicious payments after money moves, when recovery chances are slim. By that stage, mule controllers have already shifted funds through several intermediaries using instant payments, peer-to-peer transfers, and cross-platform wallets. The speed of modern real-time payment systems leaves almost no margin for reactive detection.
To stop fraud before the transaction is initiated, institutions must classify accounts based on behavioural and relational intelligence rather than isolated events. Patterns that seem normal in isolation, like a late-night transfer or a login from a new device, gain significance when linked across networks. Hidden correlations emerge when analysing:
These early signals, when combined, leave a trail of mule activity long before a fraudulent transfer occurs.
This is where Acoru’s pre-fraud signal intelligence approach stands apart. Instead of reacting to downstream fraud alerts, Acoru builds a unified intelligence layer that classifies accounts dynamically based on cross-institution patterns and other fraudulent cues.
By correlating hidden linkages and behavioural signs, Acoru enables banks to spot mule activity early, not in hindsight. This cuts losses, reimbursements, and investigative drag dramatically.