Fraud models are generally based on narrow data streams processed by traditional machine learning models such as gradient boosted machines. Our talk will cover how Uber improved on this by applying deep learning to extract complex feature relationships from high-dimensional datasets such as tapstream and location data. We will cover the lessons we learned while applying deep learning to three fraud use cases: Finding anomalous trip locations based on all Uber trip history; Using tap streams to model normal vs fraud app usage; Computer vision for validating credit cards and IDs.
Karthik is a senior data scientist at Uber focusing on solving fraud problems using machine learning. He builds advanced machine learning models like semi-supervised and deep learning models to detect account takeovers and stolen credit cards. Before Uber, Karthik was a co-founder of his company LogBase where he worked on real-time analytics infrastructure and building models to rate drivers based on their driving behavior. Prior to founding LogBase, he was a founding member of the LinkedIn security team where he developed various security products, with a particular focus on anti-automation efforts.