In 2024, Nasdaq reported that more than $3.1tn in illicit funds circulated through the global financial system, with much of it hidden using so-called ‘money mules’.
As technology improves and criminals grow more sophisticated, financial institutions are under increasing pressure to tighten anti-money laundering (AML) systems and detect money mule activity more effectively to reduce risk, avoid regulatory fines, and protect their reputation.
Symphony AI, a developer of vertical-specific AI applications, recently delved into how firms can enhance money mule detection to reduce risk exposure.
A money mule is someone who moves funds on behalf of criminals. The act of money muling helps obscure the origins of illicit money. Because it falls between compliance and fraud teams, detecting mule activity requires close cooperation across departments. This has become more critical as cases rise—Santander reported a 45% surge in money mule activity among 25–34-year-olds in 2024, while law enforcement in the US disrupted 3,000 money mules the same year, Symphony AI explained.
Several red flags can signal potential money mule behaviour. These include unusual deposits, frequent international transfers, accounts lacking normal commercial activity, multiple IP addresses linked to a single account, and unwillingness to pass customer due diligence (CDD) checks. While not every red flag guarantees criminality, their presence warrants further investigation to manage customer risk properly.
Money mules are effective tools for criminal networks for many reasons. They offer anonymity by creating transaction layers, use compartmentalisation to protect the wider network, and can operate at a volume that disperses suspicious activity. Mules often operate via legitimate bank accounts, making detection even more difficult, and are recruited through online scams or fake job offers. Furthermore, outdated bank technology and the split between compliance and fraud teams can allow illicit transfers to slip through the cracks.
Money mules generally fall into three categories: knowingly complicit criminals, individuals aware but willing to take the risk for compensation, and those duped into participating without realising. Recruitment typically happens through phishing, fake job ads, or social engineering tactics online.
Statistics show that most mules are young. Santander found that over half of the money mules it detected were under 30, with 21% under 20. Barclays similarly reported that 40% of detected money mules were under 25.
The consequences of becoming a money mule are severe. In the US, convicted money mules face up to 20 years in prison, while in the UK it can result in 14 years. Similar penalties exist in Australia, Canada, Germany, India, and Singapore. A conviction damages employment prospects, restricts access to banking, and can severely limit international travel.
For banks, failure to detect money mule activity can result in significant regulatory fines, reputational damage, higher compliance costs, and legal liabilities, Symphony AI stated. Regulators expect banks to maintain strong AML and know your customer (KYC) frameworks to detect and prevent such crimes.
Money mule detection relies on tools such as KYC/CDD processes, AML transaction monitoring, and surveillance of communications and adverse media. Improved staff training also plays a critical role. Financial institutions are increasingly adopting AI-powered solutions to strengthen detection capabilities and meet stricter regulatory expectations.
SymphonyAI is among the leading providers helping banks upgrade their money mule detection systems. With advanced transaction monitoring and AI-powered overlays like SensaAI for AML, financial institutions can better collaborate across compliance and fraud teams and strengthen their resilience against evolving financial crime.
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