Francesco Fulcoli – Chief Compliance Officer and Board Member at TransferGo 

The Financial Conduct Authority recently conducted an extensive review of payment account providers’ systems and controls against money mule activity, shedding light on both positive practices and areas that require enhancement. Money mules have emerged as a crucial component of fraudulent schemes, enabling criminals to launder the proceeds of their illegal activities. The FCA’s review is part of a broader effort to address this issue and protect the financial industry and the public.

Understanding Money Mules

Before delving into the key findings and recommendations from the FCA’s review, it’s essential to understand the role of money mules. Money mules are individuals recruited by criminals to assist in the movement of illicitly obtained money. They can be either knowingly involved or unknowingly ensnared in criminal activities.

1. Unknowingly Involved Money Mules: These individuals are often deceived by fraudsters who present seemingly legitimate opportunities or provide plausible explanations for their actions. Unaware of their involvement in illegal transactions, they believe they are working for a legitimate company or helping someone in need.
2. Knowingly Involved Money Mules: In contrast, knowingly involved money mules are fully aware of their participation in illicit activities. They willingly assist criminals in money laundering or fraudulent schemes, often motivated by financial gain, escaping financial difficulties, or choosing to engage in criminal activities.

Key Findings and Recommendations

• Systems and Controls: Many firms are recognizing the risks associated with money mules and have started using technology to calibrate their systems according to risk. A risk-based approach is essential for detecting and preventing money mule activities effectively. Innovative solutions, including facial recognition systems, device profiling, and geolocation, are being employed by some firms. These technologies flag suspicious activities, enabling firms to identify potential money mule networks. Firms should continue to invest in machine learning systems, as they adapt to evolving fraud tactics more effectively. A combination of machine learning and tactical rules can provide a robust anti-fraud system.

• Data Sharing and Intelligence: A growing number of firms are using lawful data sharing to combat money mule activities. Collaborative efforts with law enforcement agencies and data sharing initiatives have improved the detection and prevention of these activities. Engagement with external bodies such as Cifas, UK Finance, National Crime Agency (NCA), and FinTech FinCrime Exchange enables firms to share findings and preventive measures, contributing to a more comprehensive response to money mule typologies.

• Training: Firms are beginning to provide dedicated training for their staff on financial crime and fraud. This training ensures that employees stay updated on new criminal typologies and can effectively detect and prevent money mule activities. Regular training for both new and existing staff is essential to keep them informed and equipped to identify potential threats.

Areas for Improvement

• Governance and Risk Assessment: Firms with a higher number of reported mule accounts than their peers often lack senior management oversight and fail to report Management Information (MI) to address the risk effectively. Involvement of senior management is crucial for regulatory compliance and customer trust.

• Onboarding: Some firms conduct relatively few checks during customer onboarding, relying on subsequent monitoring to identify money mule-related activities. Robust controls during onboarding are necessary to detect potential red flags and money mules. Capturing additional information during onboarding, such as salary or turnover details, can help reduce false positives in transaction monitoring alerts.

• Transaction Monitoring: While some firms focus on outbound transaction monitoring, they frequently lack adequate inbound transaction monitoring systems. Inbound monitoring can help identify unusual transaction patterns and account behaviour changes, common characteristics of money mule behaviour. Firms should consider adopting systems like device profiling, geolocation, and behavioural biometrics learning machine to disrupt money mule networks effectively.

• Reporting: Firms must report money mule activity promptly through relevant reporting systems, such as the National Fraud Database. Timely reporting is crucial for disrupting and closing mule networks. Receiving firms must act on alerts from notifying institutions swiftly and raise Suspicious Activity Reports (SARs) as necessary to help law enforcement investigate and prosecute criminals.

• Resource Allocation:Some firms would benefit from dedicated resources for actively investigating money mule activities. This ensures timely detection, monitoring, and reporting.

• Communication and Awareness: Firms should improve their communication strategies and awareness initiatives to educate customers about the risks associated with money mule activities. Public awareness is vital in combating financial crime and protecting consumers.

The FCA’s review offers a comprehensive perspective on the role of financial institutions in disrupting money mule activities and safeguarding the public from fraud. By implementing the recommendations outlined in the review, firms can strengthen their anti-fraud systems and controls, mitigate the risks of money mule activities, and reinforce their regulatory compliance.

Consistently adapting detection and monitoring methodologies, along with educating consumers about the risks involved in money mule activities, is crucial in the ongoing fight against financial crime. Firms must remain vigilant, proactive, and adaptable to evolving threats in the ever-changing landscape of economic crime.

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• Source: https://www.finextra.com/blogposting/25071/combating-money-mule-activity-fcas-comprehensive-review-and-recommendations?utm_medium=rssfinextra&utm_source=finextrablogs