Online Financial Behavior and the Internet of Things

Project Summary

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.

Themes and Categories
Interdisciplinary Inquiry
Data+, Finance, Economics and Computation, Social Sciences
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

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

 

Related People

Related Projects

Maximizing Data Communication for Faster Energy Access

United Nations Sustainable Development Goal 7 calls for universal access to affordable, reliable, sustainable, and modern energy. Researchers and practitioners around the world have responded to this call by producing a wealth of energy access data. While many data gaps still exist, are we capturing the fullest potential from the information and research we do have, and what it tells us about how to accelerate energy access? Power for All’s Platform for Energy Access Knowledge (PEAK) is an interactive knowledge platform designed to automatically curate, organize, and streamline large, growing bodies of data into digestible, sharable, and useable knowledge through automated data capture, indexing, and visualization. A team of students led by Rebekah Shirley will consult with Power for All to creatively visualize PEAK’s library, and to explore machine learning and natural language processing tools that can enable auto-extraction and visualization of data for more effective science communication.

Data and the Global Corporate Bond Market

Are there relative value opportunities in the global corporate bond markets?  
A team of students will work with Professor Emma Rasiel to understand whether an analysis of credit spreads on bonds issued by international firms in multiple countries over time can shed light on potential arbitrage opportunities. The team will have frequent opportunities to interact with analytics professionals at a leading financial advisory and asset management firm.

 

Construction Machinery and the Business Cycle

A team of students will consult with a leading financial advisory and asset management firm that is seeking to understand how big data can shed light on the secondary market for construction machinery. Students will explore a combination of publicly-available datasets that describe the used-machinery market and its potential implications as an indicator for the business cycle. There will be frequent interactions with analytical professionals from the firm.