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  5. Information in Credit Markets
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Information in Credit Markets

Paper Session

Friday, Jan. 5, 2024 2:30 PM - 4:30 PM (CST)

Marriott Rivercenter, Grand Ballroom Salon K, & L
Hosted By: American Finance Association
  • Chair: Boaz Abramson, Columbia University

Relationship Banking and Credit Scores: Evidence from a Natural Experiment

Maya Shaton
,
Federal Reserve Board
Tali Bank
,
Bank of Israel
Nimrod Segev
,
Bank of Israel

Abstract

We show the effect of credit scores’ introduction on consumer credit prices. Utilizing a novel dataset of the universe of loans in Israel, we find that a decline in information asymmetry, following credit scores’ introduction, led to a decrease in loan prices for households with strong relationship banking. Prior to that, when banks held a monopoly on potential borrowers’ credit history, they charged higher interest rates all else equal, as predicted by theoretical models. We then show that these informational rents significantly decrease once credit scores are introduced, and document the resulting decline in the hold-up problem. We find that this effect is most pronounced for middle class households. To the best of our knowledge, this paper is the first to show the causal impact of credit scoring on households’ loan pricing. Our results highlight the importance of information sharing in consumer credit markets. Our results have important public policy implications as to the value of credit scores in consumer credit markets.

Data and Welfare in Credit Markets

Mark Jansen
,
University of Utah
Fabian Nagel
,
University of Chicago
Constantine Yannelis
,
University of Chicago
Anthony Lee Zhang
,
University of Chicago

Abstract

We show how to measure the welfare effects arising from increased data availability.
When lenders have more data on prospective borrower costs, they can charge prices that
are more aligned with these costs. This increases total social welfare and transfers surplus
from borrowers to lenders. We show that the magnitudes of the welfare changes can
be estimated using only quantity data and variation in prices. We apply the methodology
on bankruptcy flag removals and find that removing prior bankruptcy information substantially
increases the social surplus of previously bankrupt consumers, at the cost of a
modest decrease in total allocative welfare. We show how the framework can be extended
to incorporate adverse selection and imperfect competition.

FinTech Lending with LowTech Pricing

Mark Johnson
,
Brigham Young University
Itzhak Ben-David
,
Ohio State University
Jason Lee
,
Ohio State University
Vincent Yao
,
Georgia State University

Abstract

FinTech lending-known for using big data and advanced technologies-promised to break away from the traditional credit scoring and pricing models. Using a comprehensive dataset of FinTech personal loans, our study shows that loan rates continue to rely heavily on conventional credit scores, including a 45% premium for nonprime borrowers. Other known default predictors are often neglected. Loan rates are not responsive to risk, with realized loan-level returns decreasing with risk. These patterns reflect rather simplistic and inefficient pricing. The pricing distortions result in substantial transfers from low-risk to high-risk loans.

Discussant(s)
Jillian Grennan
,
University of California-Berkeley
Lulu Wang
,
Northwestern University
Tianyue Ruan
,
National University of Singapore
JEL Classifications
  • G2 - Financial Institutions and Services