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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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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