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Peer-to-Peer Lending Performance Improvement: Learn from Lean Principles

Author

Listed:
  • Mousumi Munmun
  • Dongli Zhang
  • Charles C. Luo

Abstract

While experiencing robust growth in recent years, Peer-to-Peer (P2P) lending still faces the serious challenge of a high default rate. This study argues that it is beneficial to analyze P2P lending from a process improvement perspective. Adopting an integrative literature review method, this study identifies and summarizes the characteristics of P2P lending and then maps them to the fundamental lean attributes. Furthermore, this research proposes detailed application suggestions for reducing loan default rates in terms of understanding customer needs, value stream, information flow, pull approach, and continuous improvement. As an early attempt, mapping P2P lending characteristics and lean principles allows P2P lending to learn the well-established quality improvement practices from lean management. This study contributes to both P2P lending performance improvement and applications of lean management.

Suggested Citation

  • Mousumi Munmun & Dongli Zhang & Charles C. Luo, 2024. "Peer-to-Peer Lending Performance Improvement: Learn from Lean Principles," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(1), pages 101-101, February.
    • Handle: RePEc:ibn:ijbmjn:v:19:y:2024:i:1:p:101
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    References listed on IDEAS

    as
    1. Dongyu Chen & Xiaolin Li & Fujun Lai, 2017. "Gender discrimination in online peer-to-peer credit lending: evidence from a lending platform in China," Electronic Commerce Research, Springer, vol. 17(4), pages 553-583, December.
    2. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    3. Xiao-hong Chen & Fu-jing Jin & Qun Zhang & Li Yang, 2016. "Are investors rational or perceptual in P2P lending?," Information Systems and e-Business Management, Springer, vol. 14(4), pages 921-944, November.
    4. Zaiyan Wei & Mingfeng Lin, 2017. "Market Mechanisms in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 63(12), pages 4236-4257, December.
    5. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    6. Mingfeng Tang & Mei Mei & Cuiwen Li & Xingyang Lv & Xushuang Li & Lihao Wang, 2020. "How does an individual’s default behavior on an online peer-to-peer lending platform influence an observer’s default intention?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-20, December.
    7. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    8. Liu, Zhengchi & Shang, Jennifer & Wu, Shin-yi & Chen, Pei-yu, 2020. "Social collateral, soft information and online peer-to-peer lending: A theoretical model," European Journal of Operational Research, Elsevier, vol. 281(2), pages 428-438.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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