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Consumer Lending Efficiency: Commercial Banks Versus A Fintech Lender

Author

Listed:
  • Joseph Hughes

    () (Rutgers University)

  • Julapa Jagtiani

    () (Federal Reserve Bank of Philadelphia)

  • Choon-Geol Moon

    () (Hanyang University)

Abstract

Using 2013 and 2016 data, we compare the performance of unsecured consumer loans made by U.S. bank holding companies to that of the fintech lender, LendingClub. We focus on the volume of nonperforming unsecured consumer loans and apply a novel technique developed by Hughes and Moon (2017) that decomposes the observed rate of nonperforming loans into three components: a best-practice minimum ratio, a ratio that gauges nonperformance in excess of the best-practice (reflecting the relative proficiency of credit analysis and loan monitoring), and the statistical noise. Stochastic frontier techniques are used to estimate a minimum rate of nonperforming consumer loans conditioned on the volume of consumer loans and total loans, the average contractual lending rate on consumer loans, and market conditions (GDP growth rate and market concentration). This minimum gauges best-observed practice and answers the question, what ratio of nonperforming consumer loans to total consumer lending could a lender achieve if it were fully efficient at credit-risk evaluation and loan management? The frontier estimation eliminates the influence of luck (statistical noise) and gauges the systematic failure to obtain the minimum ratio. The conditional minimum ratio can be interpreted as a measure of inherent credit risk. The difference between the observed ratio, adjusted for statistical noise, and the minimum ratio gauges lending inefficiency. In 2013 and 2016, the largest bank holding companies with consolidated assets exceeding $250 billion experience the highest ratio of nonperforming consumer loans among the five size groups constituting the sample. Moreover, the inherent credit risk of their consumer lending is the highest among the five groups, but their lending efficiency is also the highest. Thus, the high ratio of consumer nonperformance of the largest financial institutions appears to result from assuming more inherent credit risk, not from inefficiency at lending. In 2016, LendingClub’s scale of unsecured consumer lending is slightly smaller than the scale of the largest banks. And like these large lenders, its relatively high nonperforming loan ratio is the result of a higher best-practice ratio of nonperforming consumer loans – i.e., higher inherent credit risk. As of 2016, LendingClub’s lending efficiency is similar to the high average efficiency of the largest bank lenders - a conclusion that may not be applicable to other fintech lenders. While the efficiency metric is well-accepted, widely used, and conceptually sound, it may be subject to some data limitations. For example, our data do not include lending performance during an economic downturn when delinquency rates would be higher and when lenders more experienced with downturns might achieve higher efficiency.

Suggested Citation

  • Joseph Hughes & Julapa Jagtiani & Choon-Geol Moon, 2018. "Consumer Lending Efficiency: Commercial Banks Versus A Fintech Lender," Departmental Working Papers 201806, Rutgers University, Department of Economics.
  • Handle: RePEc:rut:rutres:201806
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    References listed on IDEAS

    as
    1. Hughes, Joseph P. & Jagtiani, Julapa & Mester, Loretta J. & Moon, Choon-Geol, 2018. "Does Scale Matter in Community Bank Performance? Evidence Obtained by Applying Several New Measures of Performance," Working Papers 18-11, Federal Reserve Bank of Philadelphia.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Joseph Hughes & Choon-Geol Moon, 2017. "How Bad Is a Bad Loan? Distinguishing Inherent Credit Risk from Inefficient Lending (Does the Capital Market Price This Difference?)," Departmental Working Papers 201709, Rutgers University, Department of Economics.
    4. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    5. Donald Morgan & Adam Ashcraft, 2003. "Using Loan Rates to Measure and Regulate Bank Risk: Findings and an Immodest Proposal," Journal of Financial Services Research, Springer;Western Finance Association, vol. 24(2), pages 181-200, October.
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    More about this item

    Keywords

    commercial banking; online lending; credit risk; lending efficiency;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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