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Adverse Selection in P2P Lending: Does Peer Screening Work Efficiently?—Empirical Evidence from a P2P Platform

Author

Listed:
  • Yao Wang

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 1606/26, 110 00 Prague, Czech Republic)

  • Zdenek Drabek

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 1606/26, 110 00 Prague, Czech Republic)

Abstract

The rapid development of online lending in the past decade, while providing convenience and efficiency, also generates large hidden credit risk for the financial system. Will removing financial intermediaries really provide more efficiency to the lending market? This paper used a large dataset with 251,887 loan listings from a pioneer P2P lending platform to investigate the efficiency of the credit-screening mechanism on the P2P lending platform. Our results showed the existence of a TYPE II error in the investors’ decision-making process, which indicated that the investors were predisposed to making inaccurate diagnoses of signals, and gravitated to borrowers with low creditworthiness while inadvertently screening out their counterparts with high creditworthiness. Due to the growing size of the fintech industry, this may pose a systematic risk to the financial system, necessitating regulators’ close attention. Since, investors can better diagnose soft signals, an effective and transparent enlargement of socially related soft information together with a comprehensive and independent credit bureau could mitigate adverse selection in a disintermediation environment.

Suggested Citation

  • Yao Wang & Zdenek Drabek, 2021. "Adverse Selection in P2P Lending: Does Peer Screening Work Efficiently?—Empirical Evidence from a P2P Platform," IJFS, MDPI, vol. 9(4), pages 1-17, December.
    • Handle: RePEc:gam:jijfss:v:9:y:2021:i:4:p:73-:d:706423
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    Citations

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    Cited by:

    1. Chih-Hsiung Chang & Wu-Hua Chang & Yi-Yu Shih, 2022. "Is Financial Institution Management Effective to Reduce Problems Related to Information Asymmetry in Taiwan?," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 37-58.
    2. Avani Raval & Rajesh Desai, 2024. "Reviews and directions of FinTech research: bibliometric–content analysis approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(3), pages 1115-1134, September.

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