If reports are to be believed, cybercrooks are taking advantage of the COVID-19 situation. They are targeting people who were laid off or working from home due to the pandemic to work for them as money mules. For those who are new to the term, money mules are people who transfer ill-gotten money via various means, such as in-person transfer, courier transfer or electronic transfer, on behalf of or at the direction of criminals.
Adding layers to the money trail from a victim to a criminal actor, money mules have been one of the safest methods used by money launderers. “Once received, the mule will wire the money into a third-party bank account; cash out the money received, possibly via several cashier’s checks; convert the money into a virtual currency; convert the money into a prepaid debit card; send the money through a money service business; or conduct a combination of these actions,” according to the United States Computer Emergency Readiness Team.
The irony is people are being recruited as money mules in the pretext of legitimate employment. In most cases, they are not aware that the money they are transferring is the product of crime. Money mules are often recruited via online job websites, dating websites, social networking websites, online classifieds, email spams, and dark web forums. Even if they are unaware of the crime involved, once caught, money mules could be charged as part of the criminal money laundering conspiracy and potentially face consequences including prosecution, personal liability for money lost by victims, negative credit ratings and ban from the banking world.
A mule factory in disguise of charity
According to an article on KrebsOnSecurity.com, an “upstart mule factory” disguising itself as a non-profit was hiring people for “collecting and transmitting donations for an international Coronavirus relief fund.” The people behind the scheme set up a “perfect” website, imitating a legitimate non-profit’s portal. Revealing the fraud, the author found that the fraudsters’ portal was registered just weeks ago as compared to its claim that the “organization’ has been around for years. In addition, intercepted key file exchanges between threat actors through public file sharing services revealed details of the “job” which was identical to a typical money mule scheme.
While the above is targeted at Canadians, “Americans shouldn’t feel left out of the scam”, says the author. The cybersecurity company he referred to was able to intercept a “nearly identical set of scam templates” targeting job seekers in the US.
How banks can identify money mules with our next-gen solution?
Money mules are undoubtedly a problem for financial institutions across the globe. While law enforcement bodies are undoubtedly doing their best, financial institutions can effectively crackdown on these schemes with proper diligence and technology. These are times when criminals use sophisticated technology to avoid detection, and traditional approaches to ensure compliance fail and miss the big picture. Financial crimes are growing both in number and complexity. Sophisticated methods need sophisticated solutions like a combination of artificial intelligence and domain knowledge expertise to detect the non-obvious and flag them.
As a leading player in the anti-money laundering compliance space, Tookitaki enjoys an edge over the competition with its innovative approaches to financial crime. The power of Anti-Money Laundering Suite – our best-in-class transaction monitoring and screening solution – has its unique ways to address the problem of money mules, along with other money laundering typologies.
Typology repository: Our solution offers the world’s most comprehensive repository of money laundering patterns that we identified from financial institutions and developed in-house with a number of third-party collaborations. Our mammoth library of money laundering patterns will make sure banks don’t miss any suspicious banking activity. Banks can make use of our typologies involving money mules to enable their compliance programs to detect such criminal activity.
Multi-dimensional models: Our machine learning models learn from multiple customer interactions and apply ensemble learning techniques to predict customer behavior with accuracy. This gives a 360-degree view of the customers and their transactions along with incremental actionable insights into anomalous behavior, helping detect criminal activities in a faster and auditable manner.
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