Focus on money mules to tackle money laundering, says P20

Tuesday 27 September 2022 11:29 CET | News

Payments 20 (P20), the advocacy group of the payments industry has called on the industry for a collaborative approach against money laundering.


In a new report created alongside organisations such as NatWest, Mastercard, The Clearing House, McKinsey & Co, Hogan Lovells, and Featurespace, P20 focuses on how the industry can tackle money laundering by collectively trying to find money mules and follow the money. Entitled ‘Focus on Money Mules: A Collaborative Approach to Fighting Financial Crime’, the report indicates the various types of money mules, key challenges in their way of operating, and brings forward suggestions on how the payments industry can help reduce financial crime.

Money laundering operates by obscuring the source, movement, and destination of illicit funds produced through criminal activity, making anti-money laundering (AML) and detection efforts fundamentally difficult. As specified by the UK’s Financial Conduct Authority (FCA), more than USD 40 billion is laundered every week , with only 1% of this amount being intercepted and seized.

The P20 report shows cryptocurrency as having worsened this issue by providing new alternatives to be exploited by criminals, something that is most prevalent in over the counter (OTC) exchanges. 

Payments 20 (P20), the advocacy group of the payments industry has called on the industry for a collaborative approach against money laundering.

P20 recommendations for fighting financial fraud

P20 has recommended the following actions to help tackle money laundering:

  • Point of application prevention – considered to be one of the most effective strategies, money laundering prevention relies on identifying and declining potential mules at the point of their application.

  • Integrated approach to data – the combination of application data with fraud detection practices such as behavioural profiling, lifecycle scoring, and retrospective profiling help create a holistic view of application and payments.

  • AML machine learning application – AML efforts can be supported through means of current data science methods such as machine learning, three of its most significant features being example importance, feature importance, and counterfactual. Example importance focuses on showing what type of transactions are most likely to indicate money laundering;  feature importance focuses on what aspects of a bank account are most important to look at when deciding whether someone is a mule; and counterfactual focuses on what differences should a bank account have before it is identified as a mule.

  • Internal & external collaboration – a collaboration across internal functions and with external partners is required when unravelling criminal cases. By having system integration and aggregation of data sources, financial institutions can collaborate to provide access to law enforcement in a ready timely and cost-effective manner.

  • Geographical approach – combating money mules approaches vary based on geographical distribution, an improvement being needed across the board. A study carried out by Aite shows the UK as having considerably increased formal reporting standards when it comes to money mules, as opposed to the US. 

P20 representatives have stated that banks and payment service providers could identify a variety of financial crimes given the widespread reliance on money mules when it comes to money laundering. By identifying money mules and following the money, fraud, identity theft, and cybercrime can be fought, while simultaneously preventing the stolen money from ending up in criminals’ hands. Approaching money mules in a focused, collaborative manner could address the link in crime networks and help serve as a model for a broader cross-discipline joint effort in fighting financial crime.

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Keywords: AML, financial crime, fraud detection, fraud prevention, online fraud
Categories: Fraud & Financial Crime
Companies: Payments 20
Countries: World
This article is part of category

Fraud & Financial Crime

Payments 20

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