Artikel

Adverse selection in P2P lending: Does peer screening work efficiently? Empirical evidence from a P2P platform

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.

Sprache
Englisch

Erschienen in
Journal: International Journal of Financial Studies ; ISSN: 2227-7072 ; Volume: 9 ; Year: 2021 ; Issue: 4 ; Pages: 1-17 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
credit analysis
decentralized finance
fintech
microfinance
P2P
soft information

Ereignis
Geistige Schöpfung
(wer)
Wang, Yao
Drábek, Zdeněk
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2021

DOI
doi:10.3390/ijfs9040073
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Wang, Yao
  • Drábek, Zdeněk
  • MDPI

Entstanden

  • 2021

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