Validating credit cards on mobile using deep learning

Validating credit cards on mobile using deep learning

Tuesday, June 19
4:50 PM - 5:30 PM
Grand Ballroom 220A

The ability to validate credit cards using a mobile device is fruitful for many e-commerce platforms including Uber. Not only does this provide a seamless experience to users, but it also enables the company to verify that a user has physical possession of the credit card. In this talk we will discuss our new application that uses object detection neural networks to scan credit cards.

Traditionally, machine learning models are hosted server side, but with challenges including high bandwidth inputs, low network speeds, and a greater focus on user privacy, hosting these models server side is not always feasible. Recent advancements bring up the possibility of deploying these models directly on the mobile devices. We will discuss the challenges we faced in designing the vision model to run on a mobile device. These include reducing the model’s size footprint and optimizing the model to run on various different types of mobile hardware.


Richard Ash
Mobile Software Engineer
I work as a Mobile Software Engineer at Uber on iOS and fraud related problems. I'm focused on the intersection of mobile and machine learning; I combine both to build capabilities into Uber that create a great, safe, and fraud free user experience. I hold a Bachelor of Science in Physics and a Bachelor of Arts in Economics from. UC Davis.
Lenny Evans
Data Scientist
Lenny Evans is a data scientist at Uber focused on the applications of unsupervised methods and deep learning to fraud prevention, specifically developing anomaly detection models to prevent account takeovers and computer vision models for verifying possession of credit cards.