While money mule accounts don’t generate direct losses for banks, they can still have negative impacts, including maintenance costs and regulatory scrutiny. Banks should take advantage of advanced analytics and diverse data (including dark web intelligence) to help protect themselves. 

Money mules – people who receive and move money stolen from fraud victims – are particularly pernicious for banks. They’re often a key player in authorized payments fraud, money laundering, and other scams, but their accounts are difficult to identify (often because the mules themselves are unwitting pawns for criminals).   

A new survey by NICE Actimize found that financial institutions’ fraud leaders consider money mules one of their top five challenges. Similarly, more than 80% of fraud execs believe that more can and should be done to mitigate mule activity at their institutions.  

Banks have incentive to root out these accounts: They’re costly to maintain, aren’t profitable, and can expose FIs to regulatory scrutiny for enabling money laundering. While a mule account may not raise any red flags when it’s first opened, banks should rely on advanced analytics, behavioral biometrics, and diverse datasets (including dark web intelligence) to continuously monitor accounts, and flag potential risks.  

“Monitoring doesn’t stop at day zero,” the report recommends. “Data collected at the beginning serves as a solid foundation for continuous due diligence and monitoring. Think of it as a never-ending background check.” 

Flagging fraud and money laundering – including potential money mules – is one of the most promising (and well-funded) applications of AI in financial services; you can find more of Insights Distilled’s coverage of this critical topic here.