Research

Improving Borrowing Behavior Through Social Media Analysis

methods to help people anticipate and avoid poor borrowing decisions

In research conducted for the TFI and U.S. National Science Foundation, data scientists Rich Colbaugh and Kristin Glass have developed methods to help people anticipate and avoid poor borrowing decisions by leveraging social media and machine learning.

Their report shows that using social media to understand borrowing behavior has advantages over other forms of data: social media posts are inexpensive to collect, reveal underlying decision-drivers even in settings that may be embarrassing or distressing, and contain user-specific information, opening up the possibility to give personalized decision-support. The research also demonstrates that machine learning, the technology which helped Watson win at Jeopardy, enables good models for borrowing to be built by examining many examples of loan outcomes, both good and bad, and learning what patterns of social media activity are predictive of defaults. Importantly, this approach does not depend on preconceived ideas of what drives borrowing behavior