New identity tool helps combat digital fraud

Spotting potentially fraudulent customers and transactions is difficult. But a new tool from identity verification firm Ekata uses machine learning to help identify good and bad customers.

Network Score uses the Ekata Identity Network, a proprietary global dataset of billions of customer transactions, to reduce the number of false transaction 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. These are used to track activities and leverage transaction-level intelligence to identify when consumer information is being misused.

"With over 20 years of sourcing identity data from our global data providers, we know that authoritative data isn’t enough," says Rob Eleveld, Ekata's 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."

The Identity Network 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. The information is made available through Ekata's APIs and SaaS-based tool, to vastly improve business’s confidence in their risk analysis.

Identity Network offers dynamic decision making, as the model continues to learn with new transactions in order to better determine fraud potential. It doesn't rely on lists or a data consortium and doesn't use previous customer decisions to influence its data.

You can find out more on the Ekata site.

Photo credit: alphaspirit / Shutterstock