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Peer-to-peer lending platform risk analysis: an early warning model based on multi-dimensional information

Author

Listed:
  • Huosong Xia
  • Ping Wang
  • Tian Wan
  • Zuopeng Justin Zhang
  • Juan Weng
  • Sajjad M. Jasimuddin

Abstract

Purpose - The paper focuses on the variables that help analyze peer-to-peer (P2P) lending platforms. It explores the characteristic factors of identifying problematic platforms, and designs a P2P platform risk early warning model. Design/methodology/approach - With the help of web crawler software, this paper crawls the information of 1427 P2P platforms from the two largest third-party lending information platforms (i.e. P2Peye and WDZJ) in China. SPSS 22.0 was mainly used for basic descriptive statistical analysis, reliability and validity analysis, and regression analysis of the data. MPLUS 7.0 was used for confirmatory factor analysis and structural equation models analysis. Findings - Based on the multi-dimensional information, this paper performs text mining to develop an investor sentiment index. This study shows that the characteristics of the platform (i.e. basic features, capital security, operations management, and social network) have a significant impact on identifying problematic platforms. Research limitations/implications - There are some limitations to this research. In the process of model construction, some external factors may be ignored, such as government policies. Future research will need to consider the impact of policy and other factors more comprehensively on P2P lending platform risk identification. Practical implications - This study proposes an effective method for investors and regulators to identify the risk factors of P2P lending platforms. The research findings provide valuable insights for promoting government participation in platform management as well as a healthy development of the P2P lending industry. Originality/value - The paper addresses the factors that influence platform risks to help analyze P2P lending platforms. Prior research has not explored how to identify problematic P2P lending platforms in-depth and is limited by only focusing on either soft information or hard information. It identifies the characteristic factors of identifying problematic platforms and designs a P2P platform risk early warning model.

Suggested Citation

  • Huosong Xia & Ping Wang & Tian Wan & Zuopeng Justin Zhang & Juan Weng & Sajjad M. Jasimuddin, 2022. "Peer-to-peer lending platform risk analysis: an early warning model based on multi-dimensional information," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 23(3), pages 303-323, April.
    • Handle: RePEc:eme:jrfpps:jrf-06-2021-0102
    DOI: 10.1108/JRF-06-2021-0102
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    Citations

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    Cited by:

    1. Awais, Minahil & Afzal, Ayesha & Firdousi, Saba & Hasnaoui, Amir, 2023. "Is fintech the new path to sustainable resource utilisation and economic development?," Resources Policy, Elsevier, vol. 81(C).

    More about this item

    Keywords

    P2P lending Platform; Risk; Sentiment; Data mining; China;
    All these keywords.

    JEL classification:

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