Catch me if you can: How Q2 Catches More Fraud with Breakthrough AI

Protopia AI to Collaborate with AWS to Solve Generative AI’s Privacy Challenges

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Exclusive Webcast with Q2 + Protopia from AI Summit, NY

Exclusive Webcast from Q2 + Protopia from The Annual AI Summit in New York on Using Breakthrough AI for Fraud Detection

Protopia AI and financial technology leader, Q2 present on expanding Q2’s fraud prevention to segments that were previously inaccessible.  

Complete form to access exclusive webcast.

You will receive access to Protopia AI + Q2’s presentation from the AI Summit in NY.

Schedule a consultation to see how Protopia AI can help you tap into more data.

About the AI Summit Session

The session highlights how Q2, a leading provider of digital transformation solutions for banking, applies Protopia AI’s breakthrough AI-based solution to:

  • Enable check fraud detection for their entire landscape of customers and
  • Expand open access to state-of-the-art computer vision-based fraud detection

Q2’s new privacy-preserving fraud detection solution will enable more Financial Institutions to improve business efficiencies without revealing PII that data governance and security protocols prevented them from doing prior.

About the Speakers

Jesse Barbour, Chief Data Scientist, Q2

Jesse Barbour is the Chief Data Scientist at Q2 ebanking. He uses machine learning to discover hidden structure, insight, and value from data. His work has paved the way for multiple ground-breaking products including Sentinel, Q2’s flagship security offering, and SMART, the company’s targeting and messaging platform. Jesse’s 14 years in the financial services industry have been driven by a relentless passion to deliver technologies that have a fundamental, positive impact on individuals and communities.

Eiman Ebrahimi, CEO, Protopia AI

Dr. Ebrahimi is the Chief Executive Officer of Protopia AI. Prior to Protopia AI he was a Senior Research Scientist at NVIDIA for 9 years. During his time at NVIDIA, he worked on fundamental problems at the intersection of systems and machine learning. While at NVIDIA research, he co-authored publications on topics ranging from Enabling Programmer-Transparent Near-Data Processing to Distributed Multi-GPU Deep Learning Training techniques.

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