Failing to tackle online fraud is increasingly damaging the brand reputation and the bottom line for thousands of businesses around the globe, new research reveals. 

Almost two-thirds of all ecommerce brands (61%) have found themselves at the centre of highly damaging public media storms when fraud hits, according to fraud prevention company Ravelin’s Global Fraud Trends 2024 Survey.  

Of those polled, 40% (and 41% in the UK) say their brand image has been affected by fraudulent activities. This varies by sector – 40% of retailers, 36% of those in travel, 45% in digital goods, and 38% in marketplaces.

Overall, fraud has increased for 74.8% of companies around the world, with some regions suffering more than others. The UK has seen the biggest increase, at 84%. 

The results of such negative publicity can be devastating for e-commerce brands. 

Fraud also negatively impacts stock prices for 20% of larger companies, and across the board, responders said fraud affected growth the most, at 51.8%. 

One in four say they lose over $15m (£12m) to fraud yearly, and over half (51.8%) say fraud stifles company growth, leading to broader knock-on impacts. Customer loyalty also takes a hit for 37% of companies, further impacting reputation. 

Interestingly, 31% (39% in the UK) of respondents polled by Ravelin cited worries about public image as an obstacle to fighting fraud more efficiently. The key concern was adding friction to the customer journey, which impacts loyalty and reputation.

Ravelin CEO Martin Sweeney said, “In a highly connected world where reputations can be globally trashed with just a few keystrokes, brands must do more to mitigate against fraud. 

“A company’s reputation means everything and must be protected as if it’s a matter of life and death – from a business perspective, that’s exactly what it is.

“Machine learning is recognised as crucial to fighting fraud without adding friction and enabling brands to spot issues before they escalate and impact reputation.”

The benefits of AI-powered solutions

Automation in the form of machine learning is widely seen (66.2%) as the most useful implementation of artificial intelligence for fraud prevention, ahead of LLMs (46%), generative adversarial networks (GANs) (38.7%) and big data analytics (36.8%).

Across the board, two-factor authentication (2FA) – which includes technologies such as 3D Secure and biometric verification – is the most popular fraud-thwarting tool (54.1%).