The Online Fraud Gender Gap
We analyzed hundreds of thousands of eCommerce purchases placed with retailers around the world - and we’ve laid out our findings in the following post.
In the weeks leading up to International Women’s Day, research groups and media outlets publish reports on women’s status in society and on the gender gap in countries across the world. As an eCommerce fraud prevention company, we figured Women’s Day is a good opportunity to assess the state of women in online fraud – are female consumers more or less likely to commit fraud? Are professional fraudsters mostly male or are they an egalitarian bunch?
To answer these questions, we analyzed hundreds of thousands of eCommerce purchases placed with retailers around the world – and we’ve laid out our findings in the following post.
The CNP Fraud Gender Gap
I’d like to start with a quick disclaimer; as Riskified processes purchases placed online or over the phone, we don’t have a way of actually knowing the gender of the customers. For the purpose of our analysis, we examined orders where the customer name noted in the billing or shipping details is distinctly identifiable as female or male and matches the email address provided by the consumer. With the disclaimer out of the way – let me tell you what we found!
Females Are Less Fraudulent (or perhaps better at fraud)
While Riskified’s approval rate of purchases by male & female consumers is almost identical, the rate of fraud in eCommerce orders placed by males is 30% higher than in purchases made by females. In other words, online orders placed by men are 1.3x times likelier to be fraudulent than orders placed by women.
By rate of fraud, I mean both fraud attempts that were declined by our system as well as orders that were approved but for which we subsequently incurred fraud-related chargebacks. Since the biggest disparity between the genders was in blatant fraud attempts – meaning purchases that our models identified as fraud with a very high certainty – we can only assume that male fraudsters have a “blunter” fraud pattern while female fraudsters have more sophisticated methods of operation. It appears that women are not only less likely to commit fraud, but when they do commit fraud – they are better at it than men. Another possibility that we cannot rule out is that highly sophisticated fraudsters choose to use female names when making online purchases.
Gender Fraud Rates by Industry
In addition to comparing overall fraud rates in purchases made by men versus women, we zoomed in to see what the data shows within specific industries. Interestingly, men are not always more likely to commit fraud.
- Industries with more fraud by females: Cameras, Smoking paraphernalia, Auto parts
- Industries with more fraud by males: Fashion, Jewelry, Cosmetics, Children
- Industries with even males vs females fraud rates: Gift Cards, Event Tickets, Travel
When it comes to online purchases of cameras, automobile parts and smoking paraphernalia, the rate of fraud in orders placed by females is higher than in purchases by males. It’s important to note that there are significantly fewer purchases by females in these industries.
Similarly, in industries where there are significantly more purchases by female consumers (such as jewelry and cosmetics) there’s a higher rate of fraud in orders placed by male customers. In industries with an even amount of purchases by consumers of both genders – event tickets and gift cards, for example – the rate of fraud in orders placed by males and females is nearly identical (though there’s still slightly more fraud in purchases made by men).
Female Fraud Fighters
Fraud is one of those rare fields where we do not hope to see more women joining the ranks of online fraudsters and closing the gender gap. We are, however, in favor of women taking part in the battle against CNP fraud!
The risk and payments industry already has some great female executives, such as Merchant Risk Council CEO Danielle Lagao and European Managing Director Úna Dillon. At Riskified, we are lucky to count many women among our developers, data scientists and fraud analysts.