Fintech Thrust Seminar | Specialized Lending when Big Data Hardens Soft Information
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Fintech Thrust Seminar | Specialized Lending when Big Data Hardens Soft Information
Abstract:
We study specialized lending in credit market competition by developing a novel framework that distinguishes between broader data (information span) and more precise data (signal precision), capturing the emerging trend that traditionally subjective (“soft”) information becomes more objective and concrete (“hard”). Two banks, equipped with data processing systems, possess hard signals to screen loan applications; the specialized bank further accesses soft signals from interactions with borrowers to price the loans. This private-information-based pricing delivers the empirical regularity that loans by specialized lenders have lower rates and better ex-post performance. Information span and signal precision have different implications on equilibrium outcomes.
Jing Huang is an assistant professor of finance at Mays Business School, Texas A&M University. Professor Huang is an applied finance theorist working on financial intermediation, and she is a member of the Finance Theory Group. She is particularly interested in studying how FinTech lending transforms the credit competition landscape and its regulatory implications. Her research topics include financial regulation, open banking, FinTech lending and Blockchain economics. Professor Huang received her Ph.D. in Finance from Duke University.