Online Financial Behavior and the Internet of Things

Overview

Overview: Zijing Huang (Statistics, Finance), Artem Streltsov (Masters Economics), and Frank Yin (ECE, CompSci, Math) spent ten weeks exploring how Internet of Things (IoT) data could be used to understand potential online financial behavior. They worked closely with analytical and strategic personnel from TD Bank, who provided them with a massive dataset compiled by Epsilon, a global company that specializes in data-driven marketing.

Project Results: The team began by tying specific TD Bank products and potential products to specific financial response variables in the Epsilon data. Then, using advanced statistical and machine-learning techniques, they built models that teased out specific predictor variables, both financial and non-financial, that best illuminated relationships in the dataset. Finally, they storyboarded several potential ways to use Amazon Alexa data, or similar IoT sources, to give precisely targeted information about the relationship between a customer and these predictor variables. They finished their project with a presentation to senior leadership at TD Bank.

Click here for the Executive Summary

Project Lead: Brian Walsh

Faculty Leads: Robert Calderbank, Emma Rasiel, Paul Bendich

Project Managers: Shai Gorksy, Brooke Durham

 

 

Contact

Paul Bendich , Mathematics
bendich@math.duke.edu

Topics

Data+
Finance, Economics and Computation
Social Sciences