Experian today launches the newest version of its fraud prevention platform to help businesses dealing with the rapid surge in demand for digital services and the growth of online accounts.

Due to the digital acceleration instigated by Covid-19, online retailers, businesses, and their customers are at risk of the growing threat from fraudsters exploiting outdated and unsophisticated fraud prevention systems. 

The size of the problem is significant. Experian estimates that 10 billion identity authentication checks are currently being made each day globally. Up to £31 billion* is set to be lost via online payment fraud in 2024 if rates continue at their current speed.

Specifically, Account Takeover fraud (ATO) – where a fraudster gains unauthorised access to a legitimate customer account – has increased by 34%**. It is likely to become an even bigger issue as people access more services online. 

To counter the threat and meet the demands of this new world, Experian has upgraded and enhanced its CrossCore platform. Incorporating the latest machine learning analytics capability, behavioural biometrics and device recognition technology, the single API solution gives businesses a multi-faceted approach to monitoring and reviewing suspicious activity.

Eduardo Castro, Head of Identity and Fraud, Experian UK&I, said: "The digital acceleration brought about by the pandemic means businesses have had to react to new challenges in ways that were previously unimaginable.

"With online businesses and retailers dealing with swathes of customers they've never interacted with face-to-face, it's critical they are confident in who they are dealing with and identify suspicious activity not only at the point of log-in. 

"It's no longer enough to rely on simple checks, such as email and passwords. Our combination of leading technology brings together everything a business needs to prevent it and their customers from becoming a victim of fraud in this rapidly-evolving space."

Experian's CrossCore includes the following capabilities: 

• Device capability allows businesses to recognise every device across every channel, developing a profile of each customer, and can highlight fraud indicators including inconsistencies and multiple transactions from a single device.

• Known fraud checks uses fraud databases to match transactions against known fraud and anomalies to identify any previous fraud or high-risk flags associated with the profile.

• Behavioural biometrics allows clients to identify dubious log-in patterns by detecting factors such as screen pressure, typing speed and device movements. Suspicious or non-familiar behaviour is then flagged.

• Machine Learning analytics models can predict the likelihood that an interaction is – or isn't – being made by the genuine account holder, with an overall probability of fraud. Organisations can then use this to determine how they deal with the transaction, based on their models and risk appetite.

*Juniper Digital Identity Report 2020 - 2025

** Cifas Fraudscape 2020 report. There were nearly 32,000 cases of facility takeover recorded in 2019, a 34% increase compared to 2018 (23,791).