What Mega-PAW 2019 Taught Us About Modernizing Fraud Prevention with ML

This year’s Predictive Analytics World (PAW) event was the largest ever. The premier conference devoted to commercial deployment of machine learning across multiple industries, this Mega-PAW is now just a memory—a mirage over the Las Vegas desert. However, it was packed with info on the latest trends in machine learning applications.

Along with PAW’s seven conference tracks and hands-on workshops, there were over 150 experts and speakers from around the world. And that included Enova Decisions’ own Sean Naismith, GM & Head of Analytics Services. In his presentation, “Modernizing Analytics to Effectively Fight Fraud”, Sean discussed the difficulties many companies face in truly leveraging machine learning in a production environment. He then discussed approaches that has made parent company Enova International successful at fraud detection and prevention. For example, at Enova, predictive analytics augments rather than replaces manual operations. Through multi-variate velocity models, advanced pattern detection, and automated machine learning models, the fraud analytics team is able to automate prevention of known fraud schemes. This frees of up the fraud operations team to focus on detecting new fraud schemes. Enova activates predictive analytics by leveraging a decision management platform that is made available to businesses through Enova Decisions Cloud. “The flexibility and scalability of our decision management platform,” says Joe DeCosmo, Chief Analytics Officer at Enova International, “is enabling us to achieve our vision of drastically shortening the feedback loop from weeks to hours.”

Contact us now to learn how Enova Decisions Cloud can help you activate predictive analytics in your organization.

Enova Decisions


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.