As more financial institutions are cracking down on fraud, criminals are turning to more creative measures to conduct illicit transactions and other activities. One common method on the rise is using so-called “money mules” to help facilitate transactions in hopes of keeping their money movements under the radar.

COVID-19 has further increased the demand for money mules as crime never takes a holiday, even during a crisis.

In response, financial institutions should explore the advantages of machine learning fraud detection software that can help identify potential money mule activity.

What are Money Mules?

The term “money mule” refers to a person who is used to transfer money in an effort to conceal financial activity. For example, someone who wants to illegally transfer ill-gotten money to someone else may use a money mule to complete the transaction and make it look legitimate. The goal is to add extra layers to the money trail to make transactions look less suspicious.

To date, money mules have been a relatively safe bet for criminal activity, as many money mules don’t realize they’re being used to launder money. The originator will wire money to the money mule, then the mule will convert that money into cash or a check, wire it into a third party bank account, or convert it into a prepaid debit card, among other options.

On the surface, this type of activity seems harmless. Millions of people request cash, write checks, or buy prepaid debit cards on a daily basis, so it’s not always easy to detect the work of a money mule. However, given that the number of money mule cases are on the rise (40,000 cases reported in the UK in 2018 alone), it’s no surprise that financial institutions are ramping up efforts to detect and eliminate this type of fraud.

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