KUALA LUMPUR (Aug 4): One of the biggest drags on business growth is e-commerce fraud, says Stripe, a global financial services and software-as-a-service (SaaS) company.
Combining data from billions of transactions on the Stripe network from 2019 to 2021 with a survey of more than 2,500 businesses from nine countries, the report illustrates global fraud patterns across locations and business models.
It stated the growing burden that global businesses face from e-commerce fraud, the difficult choices they must make and the steps they can take to fight back and protect their margins. Key findings include:
- Nearly three-quarters of businesses have diverted engineering resources, and more than half have curtailed expansion plans, owing to fraud concerns;
- Subscription businesses struggle most with fraud, and 72% think they will lose more money to fraud in 2022 than in 2021; and
- The volume and sophistication of fraud vary dramatically across markets, requiring tools that adapt to local fraud patterns. France had nearly twice the fraud rate of Germany, while Singapore experienced half the rate of the wider Asia-Pacific region.
The report finds that more than half of businesses surveyed said fraud was a growing concern and had particularly affected businesses offering business-to-business SaaS products and business-to-consumer subscriptions. These companies are more vulnerable to fraud since they are more likely to be household brands.
Businesses lacked the tools or resources to deal with fraud or, rather, they were more focused on generating revenue and making their businesses profitable than forming a fraud prevention strategy.
This was not restricted to new businesses only, as some of the established businesses also find it hard to combat fraud, owing to the complexity of fraud patterns compared to pre-pandemic times.
Stripe suggests that businesses ask customers to provide more relevant information at payment checkout to better verify their legitimacy and explore other payment methods to reduce the risk of fraud. They also suggest leveraging machine learning fraud detection, among other prevention methods.