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Fintech Borrowers: Lax Screening or Cream-Skimming?

Journal: Review of Financial Studies

Date: 20211001

Author: Di Maggio, Marco; Yao, Vincent

Abstract:
We study the personal credit market using unique individual-level data covering fintech and traditional lenders. We show that fintech lenders acquire market share by lending first to higher-risk borrowers and then to safer borrowers, and rely mainly on hard information to make credit decisions. Fintech borrowers are significantly more likely to default than neighbor individuals with the same characteristics borrowing from traditional financial institutions. Furthermore, they tend to experience a short-lived reduction in the cost of credit, because their indebtedness increases more than non-fintech borrowers after loan origination. However, fintech lenders' pricing strategies are likely to take this into account.

Link: Google Scholar

Background and Context

Study Overview

Research examines fintech vs traditional lenders in personal credit market using unique individual-level data covering fintech and traditional lenders from 2012-2017.

Market Size

Consumer credit market had $3.8 trillion outstanding credit in 2018, with fintech lenders predicted to reach 20% market share by 2020.

Methodology

Analysis uses credit bureau data covering 200 million consumer files and over 1 billion credit trades, comparing borrower characteristics, loan terms and performance between fintech and traditional lenders.

Growth of Fintech Lending Market Share (2012-2017)

  • Shows dramatic growth of fintech lending from minimal market presence in 2012 to significant share by 2017
  • Fintech market share doubled between 2014-2016
  • Growth occurred alongside overall market expansion rather than pure substitution of traditional lending

Average Borrower Credit Score Evolution by Lender Type

  • Fintech lenders initially targeted lower credit score borrowers but improved borrower quality over time
  • By 2017, fintech borrowers had average credit scores of 703 vs 679 for traditional lenders
  • Demonstrates fintech lenders' evolution from serving underserved segments to attracting prime borrowers

Loan Performance: Default Rates Over Time

  • Fintech loans show higher default rates despite similar borrower characteristics
  • Default probability increases from 0.3% to 1.7% within 15 months of origination
  • Higher defaults particularly concentrated among borrowers with credit scores below 700

Total Debt Evolution Post-Loan Origination

  • Initial decline in total debt following loan origination
  • Significant increase in total indebtedness for fintech borrowers after 4 months
  • Suggests fintech borrowers use funds for additional spending rather than pure debt consolidation

Interest Rate Correlation with Default Probability

  • Fintech lenders show stronger correlation between interest rates and defaults
  • 20% higher correlation compared to traditional lenders
  • Suggests more accurate risk-based pricing by fintech lenders

Contribution and Implications

  • First comprehensive study showing fintech lenders rely more heavily on hard information from credit reports compared to traditional lenders
  • Identifies higher default risk in fintech loans, but demonstrates this is offset by more accurate risk-based pricing
  • Shows fintech lending provides valuable credit access, particularly for borrowers experiencing unemployment or financial constraints

Data Sources

  • Market share visualization based on Figure 1 panel (b)
  • Credit score evolution based on Figure 3 panel (a)
  • Default rates based on Table 6 and Figure 5
  • Debt evolution based on Table 8 and Figure 5 panel (d)
  • Rate-default correlation based on Table 9