Gal Benor
Gal Benor is a Machine Learning Scientist at PayPal. In her day-to-day projects she focuses on developing transparent fraud detection models that safeguard users. She is passionate about eXplainable AI (XAI) and works to make machine learning more interpretable and tailored to specific needs. Gal earned a BSc in Computer Science from Ben-Gurion University and an MSc in Applied Mathematics and Systems Biology from the Weizmann Institute of Science, where she focused her research on breast cancer—an area where transparency is critical, and black-box models are not an option.
Session
A wise man once told me, “It is not only the what that matters - but the WHY.” In today’s rapidly evolving landscape of fraud, relying on opaque machine learning models is no longer feasible. This talk will explore how we can—and should—harness eXplainable AI (XAI) to demystify these “black-boxed” models, providing transparency and valuable insights into the decision-making processes behind fraud detection.
We will discuss how, at PayPal, we leveraged GenAI to personalize model explanations for each business need and overcame significant production scalability challenges while scaling for over 1 Billion accounts. By changing our perspective, we found solutions rooted in a fundamental computer science principle that unlocked new efficiencies and transparency.