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What Determine Loan Rate and Default Status in Financial Technology Online Direct Lending? Evidence from Indonesia

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  • Wimboh Santoso
  • Irwan Trinugroho
  • Tastaftiyan Risfandy

Abstract

Using a large-scale dataset from three leading online peer-to-peer (P2P) lending platforms in Indonesia from 2014 to 2018, we investigate the determinants of platform interest rate and borrowers’ default status. Our result shows that loan and borrowers’ specific factors are significantly associated with the loan rate and loan default, although the relation could differ from one platform to another. Our empirical result shows that platforms focused on very small loan for microbusiness increase their interest rate after the introduction of formal regulation. It could be because of the increase of the borrowers requiring a very small amount of loan relatively much more than the number of lenders. The shortfall of supply then drives the increase in loan rate. Some policy implications are discussed.

Suggested Citation

  • Wimboh Santoso & Irwan Trinugroho & Tastaftiyan Risfandy, 2020. "What Determine Loan Rate and Default Status in Financial Technology Online Direct Lending? Evidence from Indonesia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(2), pages 351-369, January.
    • Handle: RePEc:mes:emfitr:v:56:y:2020:i:2:p:351-369
    DOI: 10.1080/1540496X.2019.1605595
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    Cited by:

    1. Chong, Zhaohui & Wei, Xiaolin, 2023. "Exploring the spatial linkage network of peer-to-peer lending in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    3. Zhao, Chengguo & Li, Meng & Wang, Jun & Ma, Shujian, 2021. "The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag," Research in International Business and Finance, Elsevier, vol. 57(C).
    4. Wang, Yao & Drabek, Zdenek & Wang, Zhengwei, 2022. "The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).

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