Fraudsters targeted a wide range of financial products in April, including current and savings accounts, as the UK entered lockdown.
Data from the anti-fraud experts, Experian, and the National Hunter Fraud Prevention Service shows a rise in fraud rates, with criminals looking to take advantage of the disruption to both businesses and their customers.
Across all financial products, fraud rates rose by 33 per cent in April, when compared with previous monthly averages.
The largest increase was in car and other asset finance applications, which saw a rise of 181 per cent, followed by current accounts (35%) and then saving accounts (28%). Fraudulent credit card applications (17%) and unsecured loans (10%) also went up.
While the figures point to an increase in the proportion of fraudulent applications, it also signals fraud teams have been able to successfully identify and investigate questionable account openings since the pandemic began, in part due to a decrease in total applications.
Fraudsters may have been submitting higher volumes of applications in the belief that the disruption would give them a better chance of success
Micah Willbrand, Managing Director of Identity and Fraud at Experian, said: “The rise in fraud rates across each category is a warning that banks, building societies and other financial providers need to be as alert as ever in identifying fraudulent applications, even in the unique circumstances the country finds itself in.
“Its likely fraudsters have been looking to take advantage of the situation and submitting higher volumes of applications under the belief that the disruption would give them a better chance of success.
“But they have been largely disappointed. Fraud teams have had greater capacity to flag and investigate openings that otherwise may have gone unchecked, resulting in incidents of fraud being successfully identified.”
New solutions incorporating Machine Learning and Artificial Intelligence are supporting businesses and organisations of all sizes in building robust fraud prevention systems. Traditional systems have typically been based on understanding the potential risk an application poses and then flagging it to the organisation’s fraud team for further investigation.
Machine Learning bolsters this process by looking at the results of an application, whether it was fraudulent or not, and then uses this information to inform its decision making on future applications. The more information it has at its disposal, the higher quality decisions can be made.
