Enova Decisions Will Be at Mega-PAW… Will You?

Just as summer’s about to get started, Las Vegas is playing host to the 10th annual Predictive Analytics World conference from June 16 to 20. The largest PAW event to date, this Mega-PAW is the premier conference devoted to commercial deployment of machine learning across multiple industries. And this year, PAW sports seven parallel tracks, ten hands-on workshops, and over 150 machine learning experts and speakers from around the world.

As a sponsor of PAW 2019, Enova Decisions will be there, and our own Sean Naismith, GM & Head of Analytics Services at Enova Decisions, with be among those presenters. In his presentation, “Modernizing Analytics to Effectively Fight Fraud”, Sean explores the difficulties most companies face in trying to realize the full potential of machine learning and analytics in a production environment. He then reveals how Enova International has been successful at integrating traditional operations with advanced analytics to turn fraud defense into a collaborative analytics function.

“While many businesses understand the value of advanced analytics in decision making, operationalizing data science can be challenging,” says Sean.  “At this year’s PAW, I’m going to share some of the techniques we’ve used to help our customers overcome these challenges, to mitigate fraud risk, improve profitability, and deliver a better customer experience.”

We hope you can join us at PAW 2019 to experience the very latest in machine learning and hear how with it, Enova Decisions can provide the most advanced real-time decision management solutions for our customers.

Stop by the Enova Decisions booth, #419, and enter for a chance to win the latest VR headset, Oculus Quest!

Enova Decisions

About

Enova Decisions is an analytics and decision management technology company that was formed in 2016 to enable businesses in various industries, including financial services, healthcare, and telecommunications, to automate and optimize operational decisions, in real-time and at scale, through data, machine learning, and the cloud.