Let’s Talk About Reducing Fraud Loss with ML at CFCA Winter, February 25–27

According to CFCA’s 2019 Global Fraud Loss Survey (gated), 50 percent of telcos believe fraud loss is trending down globally, or at least not getting any worse. How is it, then, that 56 percent of them are actually experiencing an increase in fraud loss, with subscription fraud being the number one problem?

One explanation for this discrepancy is that fraudsters are becoming smarter. Therefore, telcos too must evolve. That’s why many telcos have shifted from manual fraud detection processes to rules-based automation tools. The problem they run into is that, for automated detection to be effective, it must incorporate machine learning (ML)—and only six percent of telcos have deployed such solutions.

Machine learning models, when properly designed, are faster and more accurate at identifying patterns than humans. Therefore, telcos can leverage machine learning to use its own data along with third-party data to more accurately predict fraudulent behaviors before or during online checkout. In addition, machine learning models can be retrained and thus continuously improve over time. This allows telcos to effectively optimize fraud detection and prevention — saving them tens of millions of dollars in fraud losses every year.

Enova Decisions can help telcos improve their fraud detection and subsequent losses by not only developing new ML-based fraud detection models, but also helping embed those models directly into a telco’s daily operations.

Intrigued? Join us at the CFCA Winter Educational Event in Las Vegas, February 25 to 27, to discuss the latest developments in ML-based fraud detection and prevention. Contact us to schedule a meeting.

If you haven’t already, you can register for the event here!

Not going to CFCA but want to learn more? Schedule a meeting with us today: sales@enovadecisions.com | 1-800-245-8220

Enova Decisions


Enova Decisions is an analytics and decision management technology company that was formed in 2016 to help organizations make smarter, faster risk decisions about customers and consumers across the risk spectrum through big data, machine learning, and the cloud.