Practical Applications and the Role of Technology

• How can the money mule problem be addressed, particularly its impact on financial institutions?
  
  
• What practical applications should be discussed when considering the benefits of addressing the issues with money mules?
  
  
• How have money mules evolved and how has this form of fraud become more sophisticated?
  
  
• What regulatory changes in the UK have led to more focus on inbound payment monitoring?
  
  
• What are the challenges of detecting the activities of mules and the need for continuous monitoring at different stages, such as onboarding or transacting?

The impact of money mules cannot be overestimated. Money mule networks launder as much as $1.6 trillion a year globally.

Money mules substantially contribute to money laundering crimes, removing colossal sums of illicit funds from the legitimate economy, stripping governments of tax revenue and thwarting economic growth by diverting capital away from productive investments, ultimately shrinking a country’s GDP. The National Crime Agency (NCA) estimates that over £10 billion is laundered via money mule activity in the UK annually. Alongside this, in 2022, UK banks identified over 39,000 accounts that demonstrated behaviour that was indicative of money muling.

It is evident that a cross-sector response is required to mitigate money mule activity and protect victims against financial exploitation because fraud teams cannot be stretched further than they already are. At the moment, banks monitor accounts for suspicious activity and use digital identity verification to conduct behavioural analysis, and in turn, search for behavioural anomalies in mouse activity, typing patterns, navigation preferences and platform choice.

Financial institutions could also prioritise investing in mule risk education, build a repository of intelligence, and cooperate with law enforcement to prevent these threats. However, more can always be done. With machine learning, continuous pattern analysis, rule modelling and optimisation can connect insights that have been gathered from a diverse network to predict the likelihood of an account being used for mule activity.

This also ensures that banks are taking advantage of the data-driven solutions available to them. Innovative technology companies can now develop, train and test machine learning mule models to identity fraud threats and protecting customers. This increased focus on inbound payment monitoring is also to the bank’s benefit. With the UK’s new reimbursement rules in effect from October 2024, payment service providers are required to reimburse eligible victims of fraud.

Banks will need to enhance their fraud prevention systems with technology to minimise the need to reimburse customers for fraudulent transactions. Better detection and proactive measures can significantly reduce financial losses caused by fraud, ultimately protecting both the bank and the customer.

Sign up for this Finextra webinar, hosted in association with Nice Actimize, to join our panel of industry experts who will explore how money muling has evolved and how this form of fraud has become more sophisticated.