Dr. Nikhil Paradkar, assistant professor in finance at the Terry College of Business at the University of Georgia, discusses his research on using data and machine learning to better understand how financial changes due to regulation, technological advancements or crises can impact the availability of credit for households. He also tells our host, Jeff Dugger, Principal Data Scientist and University Research Director at Equifax, about some very interesting research on corporate buzzwords, innovation and company earnings calls.

Jump ahead to these topics:

:58 - Paradkar provides an overview of his work at UGA

1:35 - Paradkar explains the research he presented to the Consumer Financial Protection Bureau on bank funding shocks

3:35 - If a consumer’s credit limit is reduced, how does it impact their credit score?

5:00 - Can consumers who are more exposed to their bank’s liquidity shocks have an impact on a financial recovery?

6:40 - The CFPB’s reaction to Paradkar’s research

9:10 - What machine learning has revealed about consumer finance and Fintechs

12:25 - Paradkar explains his machine learning technique used in his research

13:38 - Can lenders use Paradkar’s research to improve their lending?

15:02 - Is there a latent unobservable variable that causes FinTech borrowers to be more likely to default?

16:41 - How Paradkar uses machine learning to study corporate buzzwords, innovations and quarterly earnings calls

Learn more about our guest, Nikhil Paradkar: https://www.terry.uga.edu/directory/finance/nikhil-paradkar.html