The Trade-Off Between Model Performance and Explainability

In episode 82 of The Gradient Podcast, Daniel Bashir speaks to Ryan Drapeau.

Ryan is a Staff Software Engineer at Stripe and technical lead for Stripe’s Payment Fraud organization, which uses machine learning to help prevent billions of dollars of credit card and payments fraud for business every year.

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Outline:

* (00:00) Intro

* (02:15) Ryan’s background

* (05:28) Differences between adversarial problems (fraud, content moderation, etc.)

* (08:50) How fraud manifests for businesses

* (11:07) Types of fraud

* (15:49) Fraud as an industry

* (19:05) Information asymmetries between fraudsters and defenders

* (22:40) Fraud as an ML problem and Stripe Radar

* (25:45) Evolution of Stripe Radar

* (31:38) Architectural evolution

* (41:38) Why ResNets for Stripe Radar?

* (44:15) Future architectures for Stripe Radar and the explainability/performance tradeoff

* (48:58) War stories

* (52:55) Federated learning opportunities for Stripe Radar

* (55:50) Vectors for improvement in Stripe’s fraud detection systems

* (59:22) More ways of thinking about the fraud problem, multiclass models

* (1:03:30) Lessons Ryan has picked up from working on fraud

* (1:05:44) Outro

Links:

* How We Built It: Stripe Radar

* Stripe 2022 Update