How to Detect Mule Accounts in 2026 [5 Best Strategies]

Money mule activity has reached a global scale. In a single coordinated operation, Europol’s European Money Mule Action (EMMA 9) identified 10,759 money mules and 474 recruiters, working with more than 2,800 banks and financial institutions worldwide. And that is one operation, in one part of the world.

As criminals increasingly rely on mule networks to move stolen funds, banks and financial institutions need more effective ways to identify these accounts.

The regulatory picture makes 2026 the year this moves from best practice to obligation. Nacha's fraud monitoring rules now require receiving financial institutions to monitor incoming ACH payments for fraud, with Phase 2 in effect since June 22, 2026.

In the UK, the PSR reimbursement mandate holds sending and receiving banks equally liable for APP fraud losses, splitting reimbursement of up to £85,000 per claim 50/50. Mule accounts sit on the receiving side of both rules. An undetected mule account is no longer just a compliance gap. It is a direct financial liability.

In this article, you'll learn how to detect mule accounts and what you can do to strengthen your AML and fraud controls.

Key Takeaways

• Mule accounts are hard to detect because they mimic legitimate behavior

Criminals use trusted banking channels, small transfers, and fragmented networks to avoid traditional fraud and AML controls. Many mules are unaware they are participating, making detection even more challenging.

• Account activity and transaction patterns reveal mule activity

Detecting mule accounts requires continuous visibility into how accounts behave over time. Rapid movement of funds, pass-through behavior, sudden spikes in activity, and structured transactions are strong indicators of mule accounts.

• Advanced analytics improve detection

Device intelligence, behavioral biometrics, and graph analytics can uncover shared devices, unusual login behavior, hidden account relationships, and interconnected mule networks that rule-based systems often miss.

• Continuous account intelligence helps detect mule activity earlier

Acoru analyzes account behavior, pre-fraud signals, and counterparty relationships to identify emerging mule activity. Rather than replacing existing fraud and AML systems, it strengthens them with predictive risk intelligence and broader visibility.

Classification of Money Mules

Understanding the different types of money mules can help you design more effective detection strategies.

You can group money mules into three categories:

Worth knowing:

Acoru helps financial institutions detect mule activity by continuously analyzing:

• Account activity over time
• Transaction patterns
• Counterparty relationships
• Pre-fraud risk signals

It continuously classifies accounts into states such as Regular, Victim, Mule, Laundry, and Honeypot, with mules further separated as complicit, witting, or unwitting. Each classification carries a 0-1000 risk score, so teams can track how an account's risk profile evolves as mule activity develops.

How Fraudsters Recruit Money Mules

Money mule networks rely on constant recruitment. Criminals target individuals with promises of easy money or legitimate employment opportunities.

Below are the most common recruitment methods.

1. Scams and Fake Employment Ads

Fake job advertisements are one of the most common ways criminals recruit money mules. They use job websites, social media, and messaging platforms to promote "easy money" opportunities.

These ads often promise high pay for minimal effort and may advertise roles such as "financial agent" or "account handler," making job seekers and financially vulnerable individuals particularly susceptible.

Once someone responds, the supposed employer instructs them to receive funds into their personal bank account and transfer the money elsewhere, usually in exchange for a small commission.

2. Romance and Relationship Scams

In romance scams, criminals build trust through fake relationships formed on dating sites and social media. Victims are manipulated into believing they are helping someone they care about and may be asked to open a bank account, receive funds, and transfer money because of a supposed banking issue.

3. Direct Recruitment and Peer Networks

Not all money mules are recruited through online scams. Criminals also use direct approaches and personal connections to expand their networks.

For example, someone may be asked by a trusted friend to allow money to pass through their account in exchange for a small payment, without realizing they are helping launder illegally obtained funds.

4. Impersonation and Social Engineering Scams

Fraudsters also recruit money mules through impersonation and social engineering tactics. Criminals pose as bank employees, government officials, police officers, or representatives of legitimate companies and use urgency or authority to manipulate individuals into handling funds on their behalf.

For example, a fraudster posing as a police officer tells a victim they must help with an investigation by receiving funds into their account and sending them to another person. The victim believes they are assisting authorities, but they are actually moving criminal proceeds and acting as a money mule.

Why Mule Accounts Are Difficult to Detect

Detecting mule accounts remains one of the most persistent challenges for financial institutions.

Several factors contribute to this challenge:

• Anonymity through layered transactions: Criminals move money through multiple accounts and often across countries to hide its origin. They keep transfers small, making it difficult for traditional alerts based on large transactions to detect suspicious activity.
• Fragmented operations: Each money mule usually handles only a small part of the process and has little knowledge of the wider network. This makes it impossible for one individual to expose the entire scheme.
• Use of legitimate financial channels: Money mules rely on personal bank accounts, digital wallets, and payment services that are widely trusted by financial institutions. For this reason, their transactions often appear legitimate.
• Diverse recruitment methods and profiles: Criminals recruit mules from a wide range of backgrounds, including students, job seekers, and migrant workers. This diversity makes it difficult to identify likely mules through demographic profiling alone.
• Limited awareness among participants: Some money mules do not realize they are involved in criminal activity. This makes mule-related transactions harder to detect, as account activity appears legitimate.
• Speed and convenience of digital banking: Online account opening and instant payment systems enable criminals to quickly create and operate mule accounts at low cost.

How to Detect Mule Accounts: 5 Best Strategies

Since mule networks continuously adapt their tactics, financial institutions need a combination of preventive controls and detection capabilities to identify suspicious accounts. Below are five effective strategies that can help you detect and prevent mule account activity:

1. Monitor Transaction Patterns

The primary purpose of money mule accounts is to receive and quickly move illicit funds. They frequently show high transaction velocity and limited engagement with normal banking services.

By analyzing transaction flows and account activity, you can identify accounts exhibiting characteristics of money mule activity, helping fraud teams stop fraudulent payments before stolen funds are moved and supporting AML investigations.

Transaction monitoring can help you identify:

• Rapid movement of funds, where incoming transfers are quickly followed by outbound payments or cash withdrawals
• Pass-through behavior, where accounts consistently transfer most or all received funds while maintaining low balances
• Sudden spikes in transaction activity that deviate from a customer's historical behavior or expected profile
• Structured transactions involving multiple smaller transfers designed to avoid detection thresholds
• Accounts with unusually high transaction volumes compared with the amount of money typically held in the account or the customer's reported income

Worth knowing:

Acoru analyzes accounts across three coverage levels to provide broader visibility into potential money mule activity:

• Level 1: Every account at your institution, including customer and internal accounts
• Level 2: External accounts your customers transact with, assessed using your own observed counterparty patterns
• Level 3: External accounts with no relationship to your customer, identified through optional consortium sharing

Levels 1 and 2 deliver mule detection using only your internal data. No consortium required. When a customer sends or receives a payment, that interaction becomes a data point on the external account, so networks become visible from both directions from day one. Consortium participation is the upgrade path for Level 3 coverage, not a prerequisite.

2. Use Device and Location Intelligence

AI-powered device and location intelligence can help identify mule accounts by analyzing device, location, and network signals to uncover patterns that are difficult to detect using traditional rules. For example, AI models can:

• Detect shared devices by identifying when multiple unrelated accounts are accessed from the same phone or computer
• Flag compromised devices by recognizing signs of tampering or remote access that may indicate account takeover or external control
• Monitor unusual location patterns by comparing login and transaction locations with customers' registered addresses and historical behavior
• Identify suspicious network activity by detecting VPNs, proxies, and shared IP addresses commonly used to conceal fraudulent activity
• Spot multiple IP addresses or locations associated with a single account

Worth knowing:

Acoru works alongside existing device intelligence, behavioral analytics, and transaction monitoring tools by combining their data into a broader account-level risk view. This helps you connect fragmented indicators across channels and determine whether device, location, login, or behavioral signals indicate emerging mule activity.

3. Employ Behavioral Biometrics

Behavioral biometrics, such as typing patterns, mouse movements, and mobile swipe behavior, can help detect suspicious activity in real time. For example, they can help you identify:

• Unusual login patterns, such as access at atypical times or frequent device changes
• Abnormal interaction behavior, including repeated copy-and-paste actions, rushed navigation, or deviations from a customer's normal typing, mouse, or mobile swipe patterns

Worth knowing:

Behavioral biometrics evaluate a session: a moment in time. They tell you what happened in that interaction, not what has been building in the account for weeks.

Acoru consumes behavioral biometric signals as one input into a continuously updated account risk profile, so a suspicious session is read in the context of the account's full history rather than in isolation.

4. Analyze Networks and Relationships with Graph Analytics

Money mule schemes often involve interconnected accounts and layered fund flows that span multiple parties. To reveal these complex relationships and better identify organized mule networks, you should incorporate graph analytics into your fraud prevention and AML framework.

Graph analytics can help you expose:

• Clusters of related accounts that exhibit similar transaction patterns or share common devices, phone numbers, or addresses
• Accounts connected to known scam accounts or high-risk jurisdictions
• Circular transaction flows, where funds move through multiple accounts before returning to the same network or leaving the financial system

Worth knowing:

Acoru's account ecosystem coverage helps you assess risk across your customers' accounts and external counterparties. The moment a customer inputs a recipient account number, Acoru returns risk context based on internal account patterns to help you determine whether the destination account shows signs of mule activity.

5. Participate in Data-Sharing Consortia and Public-Private Partnerships

Criminals often exploit the fact that financial institutions have only a partial view of mule activity. While many institutions participate in information-sharing initiatives, these programs frequently rely on static indicators such as compromised credentials, suspicious IP addresses, device IDs, or known mule accounts.

A more effective approach is to share continuously updated account-risk intelligence using privacy-preserving technologies, enabling institutions to identify emerging mule networks without exposing customer data.

Worth knowing:

Acoru's Consortium Manager enables financial institutions to collaborate through a privacy-preserving intelligence network. It helps you:

• Access account risk classifications and continuous risk insights, helping identify threats beyond your own customer base
• Share fraud intelligence securely using privacy-enhancing technologies, including homomorphic encryption and zero-knowledge proofs, without exposing personally identifiable information (PII)
• Integrate with your existing fraud prevention stack to enable collaborative intelligence without replacing current systems
• Support collaborative fraud prevention while helping maintain compliance with privacy regulations such as GDPR

Detect Mule Accounts with Acoru Before Any Transaction Is Initiated

The majority of fraud systems analyze transactions in real time, and reacting at the moment a payment is initiated is often too late. By then, institutions have only seconds to make a decision, and the most valuable warning signs may have appeared hours, days, or even weeks earlier.

Effective mule account detection requires continuous visibility into account behavior, relationships, and evolving risk. This is where Acoru can help you.

Instead of focusing only on individual transactions, Acoru continuously analyzes account activity, behavioral patterns, and cross-channel signals to identify emerging mule activity and authorized push payment (APP) fraud before a transaction is initiated.

Its key capabilities include:

1. Continuous account classification: Acoru continuously evaluates account behavior and classifies customers and counterparties into evolving risk states, including regular customers, scam victims, and complicit, witting, or unwitting money mules.

Each classification carries a 0-1000 confidence score, available at any decision point from onboarding to instant payments without on-the-fly calculation.

This enables you to detect changes in risk over time rather than relying on isolated, one-off alerts.

2. Pre-fraud signal intelligence: It detects early indicators of fraud during the preparation phase, including:

• New payees
• Profile changes
• Credential exposure
• Unusual login patterns
• Test transactions
• Channel switching
• Call-center interactions
• Counterparty anomalies

When signals appear in sequence, Acoru classifies the account and continuously builds a behavioral risk profile that links victims, counterparties, and emerging mule networks long before any high-value transfer is attempted.

3. Cross-channel intelligence: Acoru correlates signals from digital banking, payments, contact centers, and other customer touchpoints to uncover scams that would remain invisible to siloed detection systems.

4. Counterparty and ecosystem risk assessment: It evaluates both customer accounts and external destination accounts to identify suspicious relationships and detect mule networks even when the receiving account is outside the institution.

5. Easy integration with existing fraud systems: You don't need to rip and replace your current systems. Acoru can use signals from your transaction monitoring, behavioral biometrics, device intelligence, and other fraud tools, allowing you to strengthen mule account and scam detection while enhancing the value of your existing investments.

6. Consortium intelligence and secure information sharing: Acoru allows you to benefit from shared fraud intelligence and network insights without exposing sensitive customer data.

Request a demo today and see how Acoru can help you detect mule accounts.

FAQ:

1. What are common red flags for money mule activity?

Common warning signs include unexpected payments from unrelated parties, rapid movement of funds, frequent transfers to multiple beneficiaries, and transaction patterns that do not align with a customer's profile or expected activity.

2. What industries are most vulnerable to money mule activity?

Banks and payment providers are the most common targets, but money mule schemes also affect cryptocurrency exchanges, fintech companies, online marketplaces, and gambling platforms.

Criminals typically target businesses that allow people to open accounts remotely and move money quickly.

3. Can businesses accidentally become part of a money mule scheme?

Yes, businesses can unknowingly become involved in money mule schemes by receiving or sending fraudulent payments, often through invoice scams or fake suppliers.

Criminals use legitimate businesses to move money and make suspicious transactions appear more normal.

See Acoru in Action

Fraud in Latin America is moving fast, and the institutions keeping pace are the ones rethinking how they detect, share, and act on intelligence. If the challenges discussed in this episode sound familiar, we would be glad to show you how Acoru works in practice.