Stopping money mules using AI

Monday 12 June 2023 08:49 CET | Editor: Raluca Ochiana | Interview

As money mules constitute both a fraud and a money laundering problem, Mike Nathan from Feedzai recommends viewing it holistically rather than solely as a compliance concern for a particular regulation.


What are the challenges in fighting money laundering?

Money laundering involves two distinct aspects – compliance and the prevention of illicit funds movement in the financial system. Compliance is crucial for banks to avoid fines and regulatory scrutiny, but identifying and preventing money laundering requires going beyond compliance.

Addressing money laundering and fraud is challenging because certain (positive) activities can be easily manipulated for fraudulent purposes. For instance, COVID-related funds created new opportunities for shell companies to be used for fraud and money laundering. This makes money laundering and fraud two sides of the same coin.

In the banking industry, the identification of money mules is tackled differently depending on the bank. For instance, in the case of some banks, it's considered an AML problem, and they have dedicated analytics teams, technology, tools, and investigators all looking at money mules. In other cases, banks are relying on fraud teams to detect money mules and investigate money mules' accounts to reduce their losses. 

What are money mules?

Money mules are accounts that are used to receive illicit funds, whether it's for fraudulent or money laundering purposes. Bad actors use various means to target money mules, such as instant payments/faster payments, internet banking, and transfers. Money mules are necessary for these types of fraud because fraudsters need a way to receive the funds, they are an essential vehicle for fraud. There are two types of money mules: recruited mules and unwitting mules. Recruited mules are paid to open bank accounts and give their details to fraudsters. Unwitting mules are people who unknowingly allow their accounts to be used for fraud. This can happen when someone tricks them into sending money to another account. 

The typical profile of a money mule is someone who may be struggling with financial difficulties or is vulnerable to offers that sound too good to be true. While it was previously targeted toward young people, nowadays it can be anyone, especially in times of economic crisis. Fraudsters play a volume game, preying on those who need quick cash. Demographics also play a role, with location and income being factors that can make someone more susceptible to becoming a money mule.

How do instant payments impact money mule activities?

Instant payments have had a significant impact on the rise of fraud and money mule activities. Banks are investing in technologies such as device identifiers, behaviour biometrics, and more screening tools to risk assess payments. However, with the rise of scams, the vulnerability no longer sits with the bank, it’s the customer. As a result, scams have become the predominant type of fraud as fraudsters look for ways to bypass the new defences. I remember once, one of the major banks in the UK said to me that ‘if you find all the mules, you can stop all the fraud’. This idea resonated a lot with me, and I have spent a lot of time putting in place strategies to try and prevent money mule accounts from taking place. A further overlap with money laundering is this kind of concept of layering, a foundational element of anti-money laundering where the fraud moves between banks and goes through multiple iterations, second, third, fourth generation mules, washing the money through the system, making it much harder for the banks to identify.

This has led the Payment Services Regulator (PSR) to put forward proposals around scam reimbursement in the UK.

What mitigation strategies can banks put in place to prevent money mules?

Monitoring banking inbound payments will become an essential cog in preventing fraud and money laundering. The trigger event for a money mule is the inbound payment, this needs to be risk assessed holistically to prevent a further outbound payment, reducing fraud and scam losses, and stopping money mules layering accounts. Another such strategy is to prevent money mules from re-entering the bank's ecosystem by listing their attributes, such as device IP, and using link analysis and graphs to investigate other shared accounts. However, these strategies are reactive and rely on fraud taking place. The use of Machine Learning can help banks build a predictive model to enable them to anticipate which accounts are likely to be money mules. It is powerful to use a model in conjunction with other strategies; to alert on a large transaction that it scored badly on the model.

It is important not using the model as a raw investigation tool, but to enhance the controls you currently have in place.

In addition to the strategies mentioned earlier, we must consider the human side of things when trying to achieve a holistic view of inbound and outbound transactions. Closing someone's account or stopping someone from doing their banking due to an incorrectly labelled transaction can be detrimental. Furthermore, fraudsters may use various tactics to obtain accounts, such as offering free trips or other incentives to unsuspecting individuals. It’s crucial to keep in mind that being convicted of being a money mule can have serious consequences, including prison time. Therefore, it's important to balance the need for effective mitigation strategies with the need to ensure that innocent customers are not unfairly impacted.4


This editorial was initially published in the Financial Crime and Fraud Report 2023 which dives into the captivating world of fraud management, digital onboarding, and financial crime in the financial services industry. You can download your free copy here.

About Mike Nathan

Mike Nathan, with 15+ years in risk and fraud, specialises in online banking, application, internal fraud, money mules, anti-money laundering, and card fraud. He's held management positions at Lehman Brothers, Lloyds, SAS, Barclaycard, and LexisNexis, and now leads a team at Feedzai, advising top financial organisations.



About Feedzai

Feedzai is the world’s first RiskOps platform, protecting people and payments with a comprehensive suite of AI-based solutions designed to stop fraud and financial crime. Feedzai enables leading financial organisations globally to safeguard trillions of dollars of transactions and manage risk while improving their customers' trust.

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Keywords: financial crime, fincrime, fraud prevention, money laundering, instant payments, banks, money mule
Categories: Fraud & Financial Crime
Companies: Feedzai
Countries: World
This article is part of category

Fraud & Financial Crime


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