Financial Inclusion and Alternate Credit Scoring: Role of Big Data and Machine Learning in Fintech

Financial Inclusion and Alternate Credit Scoring: Role of Big Data and Machine Learning in Fintech

Organized by the Private Sector Development Research Network

Hosted by IDB Invest 
Moderated by Patricia Yañez-Pagans, Lead Economist, Development Effectiveness Division

Friday, 23rd June 2023 from 9-10am EST


In this paper that will be presented, the authors 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, the authors 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 is 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.

Link to the paper


Sudip Gupta 

Associate Professor of Finance, Johns Hopkins Carey Business School

Dr. Gupta’s current research and teaching interests are in the areas of Auctions, Big Data-Machine Learning, ESG, and Fintech. He is an award-winning teacher, and his research has appeared in top journals. He has written papers in the areas of alternative credit rating using machine learning, credit derivatives, treasury auctions, nowcasting with alternative data, ESG ratings and portfolio formation with alternative data etc.

Prior to joining Carey, Dr. Gupta was a faculty and director of the top ranked MSQF program of the Gabelli School of Business, Fordham University, where he had brought big data-machine learning in finance into the MS curriculum. Previously, Dr. Gupta was a full-time faculty 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, in the areas of corporate finance, econometrics, fintech, investments, and machine learning at both undergraduate and graduate levels. He has received awards for both research and teaching. He has a PhD in economics from the University of Wisconsin, Madison.

Dr. Gupta is a data hackathon champion and consults various multinational financial corporations and government committees. He also served as a consulting expert for multiple antitrust and financial litigations.

Watch full seminar here


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