Ekata releases Network Score to fight digital fraud

Network Score is a ML prediction that enables businesses to identify good and bad customers based on a series of insights. The new dataset flags potentially risky digital transactions and fraudulent customers by analysing the activity patterns of the identity information being used.

Network Score leverages the power of the Ekata Identity Network, a proprietary global dataset of billions of customer transactions, to reduce the number of false declines and increase the precision of fraud detection. The Identity Network works in conjunction with the Identity Graph, Ekata’s database of globally sourced and licensed data, vetted through rigorous acceptance criteria in compliance with global privacy and security standards.

The Identity Network offers dynamic decision making, as the model continues to learn with new transactions in order to better determine fraud potential. It does not rely on blacklisting or a data consortium and does not use previous customer decisions to influence its data. Moreover, this solution offers businesses insight into cross-border and cross-industry fraud patterns outside of their own data set.

In addition, Ekata has released a number of Network-derived features to help businesses maximise predictability in finding fraud, including:

• Network Score – assesses the risk of a digital interaction using patterns and features selected based on predictive power in the Identity Network. 
• Network Risk – available through Ekata’s SaaS tool, Pro Insight, and it equips manual review teams and fraud analysts with the top twelve signals that indicate positive or negative activity across a digital identity’s transaction history within the Identity Network.
• IP Risk – uses a ML model to determine the risk that a given IP address is associated with fraudulent activity.