AI & Deep Learning
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Date: April 21, 2020
Time: 9:00am – 10:00am PDT
Duration: 1 hour

Join us to learn how American Express leverages deep learning techniques—such as generative adversarial networks, temporal convolutional networks, and long short-term memory—to detect fraud transactions and assign credit limits.

By attending this webinar, you'll learn:
  • How machine learning automation informs better risk decisions and the main aspects of training deep learning models on financial tabular data
  • How generative adversarial networks can be leveraged to synthesize financial data for underpopulated segments
  • How to incorporate sequential financial information to recurrent neural networks for default prediction use cases



You will receive an email with instructions on how to join the webinar shortly.

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Dmitry Efimov

Vice President for Machine Learning at American Express

In 2017, Dmitry joined American Express as a director in machine learning. Dmitry and his team conduct machine learning research on company’s risk use cases. He received his Ph.D. in Mathematics from Moscow State University, Russia in 2007. In 2008, Dmitry joined MSU's Department of Mathematical Analysis as an assistant professor. In 2012, Dmitry joined the Department of Mathematics and Statistics in American University of Sharjah, United Arab Emirates. While in the UAE, Dmitry became interested in applied machine learning and started participating in competitions on the Kaggle platform.

Di, Xu

Vice President of Machine Learning and Customer Risk Modeling, American Express

Di Xu is Vice President of Machine Learning and Customer Risk Modeling within Credit and Fraud Risk organization at American Express. Di has been with American Express since 2001 in positions of increasing responsibility in data science, including acquisition, underwriting and fraud and customer management modeling functions. Prior to his current role, he headed the AXP Big Data Labs and Digital Acquisition Data Science within Enterprise Digital & Analytics organization. Di and his team are actively exploring cutting-edge machine learning research and its application in financial services. He earned a doctorate degree in Industrial Engineering, a Master of Science in Statistics, and Bachelor's in Engineering in Control Theory.

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Date & Time: Wednesday, April 22, 2018