Money Mule Detection Blueprint for Banks

What is a Money Mule?

A money mule is someone who transfers illegally acquired money from their own bank account to an account on behalf of another person or organization.

After committing crimes (e.g., illegal drug sales, human trafficking, fraud, and scams), criminals need access to bank accounts to clean their money and avoid detection by law enforcement. This is where money mules play a critical role. Once recruited, these players transfer funds to accounts under criminals’ control, often in exchange for a fee.

In the big picture, they are critical links between criminals and their ill-gotten profits. They help criminals profit from fraud, scams, and other illegal activity by providing them access to banking services. Banks that can successfully identify and stop mule activity can significantly reduce their overall risk exposure and prevent more fraud and financial crime.

3 Different Types of Money Mules

Money mules fall into three key categories:

• Purposely-open money mules: Also known as “complicit money mules,” these individuals intentionally collaborate in fraud by opening bank accounts for criminal activity.
• Unwitting money mules: individuals lured into a mule scheme by various scam techniques, including fake job listings, romance scams, investment scams, or other deceptions.
• Witting money mules: individuals who intentionally engage in illegal activity, often due to financial strain or the promise of easy money.

Young people are proving especially vulnerable, with schools and colleges becoming hotbeds for mule recruitment. Cybercriminals target cash-strapped students and direct them to move funds through their own bank accounts.

How Criminals Recruit Money Mules

Money mule scam recruitment works much like other scams. Criminals contact people who fit the profile of someone needing money using phishing emails, vishing, social media, or other personal information.

Criminals direct victims to open a bank account (or more than one) using the victim’s personal information. These victims move money to accounts under criminals’ control, often in exchange for a fee.

Some criminals act as a money mule “herder” to actively recruit multiple victims. These herders can control several different mule accounts set up for a scheme.

Young People Targeted for Mule Recruitment

Over the years, several banks such as Santander, Barclays, and Lloyds Banking Group have published public warning messages to consumers, alerting them to the risks of acting as a mule. Lloyds and Barclays warned consumers of the potential prosecution risks, which include up to 14 years imprisonment, and the financial repercussions of potentially struggling to obtain banking and credit services in the future.

However, despite these warnings, the data from the banks show that money muling continues to run at its highest rate of all time. The banks agree that younger generations are most vulnerable to money muling.

For example, Santander reports that a mule’s mean age in 2022 was 31. That’s not to say that older groups aren’t susceptible to becoming mules. Lloyds reported a 29% increase in people over age 40 involving themselves in muling—a particularly sharp rise.

Feedzai’s research sheds light on the severity of the money mule challenge. An extensive survey of over 4,000 people in the United Kingdom and the United States found that 66% of respondents aged 25 to 34 reported being targeted for recruitment.We can also see the trend in the Asia-Pacific region. A report by the Asia Development Bank states that the global pandemic drove millions of people living in the APAC region into poverty. This gave criminals a golden opportunity to recruit more mules.

New Regulations Call for a Money Mule Detection Blueprint

The push for a blueprint to uncover mule account activity comes as banks face increased pressure to reimburse scam losses. The UK’s Payment Systems Regulator (PSR) requires UK banks to reimburse scam victims with liability split 50-50 between sending and receiving banks.

With the diversity of money mule activities and the increasing accountability under PSR’s new scheme, finding innovative ways to identify money mule activity is crucial. A recent Feedzai roundtable discussion brought industry experts together to explore strategic opportunities for financial institutions to bolster their money mule detection capabilities. Ultimately, participants crafted a blueprint for money mule detection that will be critical to stopping mules and financial crime.

7 Keys to a Money Mule Detection Blueprint

An effective money mule detection blueprint includes several essential factors. These include:

1. Strengthen Account Opening Controls

Banks should review and validate their account opening controls, ensuring they are both effective and up-to-date to bolster initial defenses against fraudulent transactions and activities. Although not foolproof, this step provides an opportunity to strengthen the first line of defense against money mules.

2. Propensity Modeling

Banks can develop an ongoing mule risk propensity score by monitoring accounts for subtle changes in user behavior. This approach enables a better understanding of overall mule exposure, informing future risk decisions and improving mule account detection. For instance, indicators of recent financial distress, like sudden overdraft usage, may signal a legitimate customer faces higher vulnerability to mule coercion or recruitment. Ongoing propensity modeling allows the bank to get ahead of the mule threat and often capture money mules before they receive fraudulent funds.

3. Inbound Payment Monitoring

Real-time monitoring of inbound payments emerged as a cornerstone in a robust mule detection strategy, especially in light of the UK PSR’s liability model. While a robust control, this approach requires addressing operational capacity and user treatment issues to be truly transformative.

4. Tying Decisions and Data Together

Ensuring the interconnectedness of various fraud decisioning systems provides a comprehensive view of customer transactional patterns. This holistic understanding aids in detecting cross-channel mule activities, for example, using risk signals from account opening throughout the customer lifecycle or using digital events to influence future card payment decisions

5. Network Value

Exploring network-level views from financial services providers offers unique perspectives to detect payments across multiple financial institutions. Combining these views with existing fraud strategies has demonstrated significant incremental value, providing insights that individual banks might otherwise miss.

6. Link Analysis

Rather than focusing solely on individual mule accounts, banks should focus on uncovering relationships through link analysis. Tracking funds transfers, shared devices, IP addresses, and more can lead to the identification of mule networks, facilitating broader takedown actions.

7. Enhanced Mule Education

Adopting a successful scam awareness and education approach, banks should invest in mule risk education from an early age through university. Integrating data-driven signals for timely and engaging warnings in the user journey can further enhance mule risk awareness.

3 Future Industry Considerations to Fight Money Mules

Roundtable participants also shared ideas on future industry steps to combat money mules. These include:

1. Leverage External Data Sources

Consider external data sources, including social media signals, to detect mule risks. Anonymized messages on platforms like Meta could be valuable features in real-time mule risk assessments.

2. Payment Risk Flags within Faster Payment Message

Evaluate the feasibility of including payment red flags within the UK’s Faster Payment scheme, similar to the Netherlands. These flags, provided by the sending bank, can enhance inbound payment monitoring for the receiving bank.

3. Payment Delays

Introduce payment delays for users with a high risk of being mule activity. This “cooling-off” period may offer an opportunity for the financial institution to take evasive action in case of suspicious activities. The approach carries both pros and cons and requires further debate.

How Behavioral Biometrics Detects Money Mules

Recent technological advancements, including behavioral biometrics, enable banks to track criminals and detect money mule accounts linked to money laundering.

Behavioral biometrics technology analyzes how humans typically interact with their devices and accounts. Analyzed data points include when users log in and how they touch their device screen or move their mouse.

At the most basic level, the key to fighting money laundering is understanding what’s happening behind the scenes. Behavioral biometrics can help banks understand how their customers normally behave and can flag behaviors typical of money mules.

Behavioral biometrics solutions have already proven effective at stopping account takeover (ATO) fraud attacks. However, applications of behavioral biometrics extend beyond fraud prevention. It also opens new possibilities for your bank to identify mules attempting to launder money through your organization.

4 Benefits of Behavioral Biometrics:

• Flags abnormal user behavior for a genuine account holder, such as uncertainty in how they interact when making a transfer
• Flags transactions that pose a higher risk of being mule activity, such as engagement with unfamiliar accounts
• Detects typical suspicious behavior during the account onboarding stage and prevents criminals from opening new accounts to launder money
• Identifies individuals behind a criminal operation and allows banks to use link analysis to detect the other mule accounts under their control and report the activity to authorities.

Behavioral biometrics technology works silently in the background. It leverages machine learning risk models to look for unusual behavior indicative of suspicious activity, but not at the cost of customer experience. Customers can proceed with their business uninterrupted, while banks can feel confident that only legitimate customers are using their services.