The Re-Emergence of Fraud Mules in Ecommerce

New data shows that mule fraud is on the rise in Europe and posing a significant threat to merchants in the region, especially at peak times around the holidays when the volume of orders increases. During the traditionally busy time of years, merchants saw a boost in revenue. However, fraudsters also capitalised on the increased volume of orders and sending their mules to do the dirty work for them, not getting caught in the buzz of the season. 

The ins and outs of mule fraud

With cybercrime becoming more organised, fraudsters are recruiting mules via fake job adverts, phishing campaigns and social media scams, promising attractive, remote job opportunities that quickly turn into employment scams. Data shows that around 6 in 10 mules are under the age of 30 as many criminals target their recruitment of mules at university or sixth form students, when young people most need their own money.

Once recruited, fraud mules are given instructions on purchasing products before forwarding them for unauthorised resale. This is when mules often realise that they will never actually be paid for their work, which results in chargebacks. Some mules are even incentivised by fraudsters to dispute legitimate purchases after receiving the goods as well.

According to the European Money Mule Actions, more than 90% of money mule transactions are linked to cybercrime, while estimates show that there were 37,000 bank accounts which demonstrated behaviour associated with muling in 2023. Approximately £10 billion of illegal money is laundered each year in the UK, with a significant impact on merchants in Europe, who are now experiencing a rise in this type of fraud, targeting high-value, luxury products, such as watches, wallets, mobile phones, and laptops.

As fraud mules are tricked into using their own credit cards and devices to purchase goods, fraudsters can bypass security measures mandated in the region, such as 3DS and SCA, making mule fraud more challenging to detect. 

Recognising patterns

What makes mule fraud particularly difficult to detect is its seeming legitimacy. Mule orders can appear legit on the surface, especially if mules use their own billing details and shipping addresses. Unlike bot attacks, mule fraud tends to be longer-lasting as it is typically spread over a longer period of time, lowering the chances of the actor getting caught. Mule fraud also goes hand in hand with other types of fraud, such as romance or refund fraud as cybercriminals combine different tactics to recruit and deploy mules. 

As with any type of fraud, identifying patterns is critical to uncover fraudulent activity. Mule fraud comes with its specifics, such as unusual shipping patterns, high volume of returns or item-not-received chargebacks, geographic mismatches and multiple accounts with shared details. Fraudsters often use mules to test a brand’s defences, gradually escalating their operations once they believe they have found weaknesses in the system. For merchants, these patterns might seem subtle but can have significant implications if not detected early.

With the growing amount of data that merchants have about each order, it’s critical that they enlist AI-enabled technology to identify these patterns. However, to combat mule fraud, businesses need to combine two key elements – machine learning capabilities and risk expertise. The combination of the two is the proven and effective way for uncovering fraud rings and preventing them from causing damage to their brands.

Protecting the brand

Beyond revenue loss, mule fraud and unauthorised reselling activity pose a significant threat to brand reputation by disrupting the customer experience and post-sale journey. As fraudsters resell products, they take away brands’ control over the ever-more important post-sale experience and customer service that influences consumers’ decision to shop with a brand again. 

Retailers should prioritise a proactive approach to tackle mule fraud, including regular audits of their processes, investments in training for fraud prevention teams, and promoting collaboration across departments. This ensures that fraud detection becomes a shared responsibility rather than an isolated function.

Additionally, merchants should consider how global fraud prevention networks can provide shared intelligence and insights to combat fraud at scale.

Tapping into mule fraud knowledge, understanding it, identifying patterns and building bespoke alerting are all ways in which machine learning and human insights can help identify mule fraud and protect retailers at checkout. Partnering with a fraud prevention provider which has access to a network of merchants is also helpful to put patterns and behaviours into context. 

Today’s competitive ecommerce landscape shows that a holistic approach to customer experience is the way forward. Merchants who want to succeed need to ensure that the whole customer journey is seamless and secure in order to build a loyal customer base that will keep returning to them. The rise of mule fraud is a reminder that as fraud evolves, so must the strategies to combat it, combining advanced technology, expert insights, and a commitment to protecting both the business and the customer experience.