How PayPal Uses Large Graph Neural Networks to Detect Bad Actors
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How PayPal Uses Large Graph Neural Networks to Detect Bad Actors
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About this listen
How do you detect fraud when less than one percent of your network’s users are bad actors? In this episode, SigOpt’s Head of Engineering Michael McCourt speaks with Venkatesh Ramanathan, a Director of Data Science at PayPal, about his work using Graph Neural Networks to detect fraud across large financial networks.
- 0:23 - Intro
- 3:08 - AI/ML at AOL
- 4:24 - The scale of data today
- 6:11 – The tradeoffs of accuracy and interpretability
- 7:54 - What are Graph Neural Networks?
- 9:18 - Robustness of GNNs; how they work with blockchain networks
- 10:57 - The need for robust hardware for GNNs
- 12:44 - How PayPal uses SigOpt for hyperparameter search
- 15:12 - The importance of sample efficiency
- 16:51 - What's next for Data Science at PayPal
- 20:52 - Opportunities for academia to power industry insights
Learn more about SigOpt at sigopt.com and follow us on Twitter at twitter.com/sigopt Subscribe to our YouTube channel to watch Experiment Exchange interviews: https://www.youtube.com/channel/sigopt
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