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IAQF & Thalesians Seminar Series: Financial Inclusion and Alternate Credit Scoring: Role of Big Data and Machine Learning in Fintech. A Seminar by Professor Sudip Gupta

  • 13 Dec 2022
  • 6:00 PM (EST)
  • Fordham University: 12th Floor Lounge in Lowenstein Building 113 West 60th Street New York, NY 10023

Registration


Register


6:00 PM Seminar Begins

7:30 PM Reception


Hybrid Event:

Fordham University

12th Floor Lounge

Lowenstein Building

113 West 60th Street

New York, NY 10023


Free Registration!


For Virtual Attendees: Please select Virtual instead of member type upon registration.


Proof of Vaccination Upon Entry is Required for In-Person Attendees


Abstract:

We use unique and proprietary data from a large Fintech lender to analyze whether alternative data captured from an individual's mobile phone (mobile/social footprint) can substitute for traditional credit bureau scores and improve financial inclusion. Variables that measure a borrowers' digital presence, such as the number and types of apps installed, measures of social connections and borrowers' "deep social footprints" based on call logs, significantly improve default prediction and outperform the credit bureau score. Using machine learning-based prediction counterfactual analysis, we find that alternate credit scoring based on the mobile and social footprints can expand credit access for individuals who lack credit scores without adversely impacting the default outcomes. The marginal benefit of using alternative data for credit decisions are likely to be higher for borrowers with low levels of income and education, as well as borrowers residing in regions with low levels of financial inclusion.

Bio:

Professor Sudip Gupta is an associate professor of finance at Johns Hopkins University’s Carey Business School. 

His current research and teaching interests are in the areas of Auctions, Big Data-Machine Learning, Corporate Finance, ESG and Fintech. He is an award-winning teacher, and his research has appeared in top academic journals. He has written papers in the areas of alternative data and credit rating, credit derivatives, ESG ratings and portfolio formation with alternative data, IPOs, nowcasting with alternative data, treasury auctions etc.  

Prof Gupta is a data hackathon champion and consults various multinational financial corporations and government committees. He has served as an expert in multiple high profile financial class action litigations. 

Prior to joining Carey, Prof Gupta was a faculty and director of the top ranked MS in Quant Finance program of the Gabelli School of Business (GSB), Fordham University, where he introduced and taught big data-machine learning in finance into the MS curriculum.

Previously, Dr. Gupta was a full-time faculty member and taught at Indiana University’s Kelley School of Business, Indian School of Business (ISB), New York University’s Stern School of Business, and the University of Maryland’s Smith School of Business. He was the recipient of the Dean’s Impact award for faculty excellence and high impact in research, teaching, and service at GSB. He has a PhD in economics from the University of Wisconsin, Madison.

Prof Gupta helps build data science and analytics teams for various financial organizations in the Wall Street and has served as chief data scientist for fintech firms.