ID security firm Ekata has just released a machine-learning tool that enables businesses to ascertain customer identity based on a series of insights.

Called Network Score, the new identity verification system 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.

“Fake digital identities are becoming increasingly prevalent”

“With over 20 years of sourcing identity data from our global data providers, we know that authoritative data isn’t enough,” said Rob Eleveld, Ekata CEO.

“Stolen personally identifiable information (PII) and fake digital identities are becoming increasingly prevalent, which makes verifying identity in the digital and card not present (CNP) world harder than ever. Fraudsters can try to impersonate and act the way legitimate users do but they will never match 100 percent of the time; those activity patterns can be powerful signals of fraud,” he added.

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For instance, a real consumer typically uses the same primary address and secondary address together.

“But if we look at how that secondary address has been used across the digital interactions we’ve seen in our Network, we might see that the secondary address has been used with tens of different email addresses in the month, which suggests promotion abuse or other fraudulent activity,” the company noted in a release.

Ekata built the Identity Network to track these types of activities and leverage transaction-level intelligence to identify when consumer information is being misused.

Unique, differentiated datasets

The Identity Network, along with the Identity Graph, are unique, differentiated datasets that power the Ekata Identity Engine.

The Identity Engine helps businesses make accurate risk decisions about their customers by providing predictive data insights on who they are, and how their information is being used online.

Using sophisticated data science and machine learning, the Identity Engine transforms the two datasets into unique and valuable data attributes, such as Ekata’s new Network Score.

These attributes are made available through Ekata’s APIs and SaaS-based tool, to vastly improve business’s confidence in their risk analysis.

Unlike other identity verification tools, the Identity Network offers dynamic decision making, as the model continues to learn with new transactions in order to better determine fraud potential.

Relying on blacklisting or a data consortium

The Identity Network does not rely on blacklisting or a data consortium and does not use previous customer decisions to influence its data.

Moreover, the Identity Network provides businesses insight into cross-border and cross-industry fraud patterns outside of their own data set.

Ekata has released a number of Network-derived features to help businesses maximize predictability in finding fraud.