IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v13y2025i2p33-d1589846.html
   My bibliography  Save this article

A Bivariate Model for Correlated and Mixed Outcomes: A Case Study on the Simultaneous Prediction of Credit Risk and Profitability of Peer-to-Peer (P2P) Loans

Author

Listed:
  • Yan Wang

    (School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA 30144, USA)

  • Xuelei Sherry Ni

    (School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA 30144, USA)

  • Huan Ni

    (Department of Economics, Finance and Quantitative Analysis, Kennesaw State University, Kennesaw, GA 30144, USA)

  • Sanad Biswas

    (School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA 30144, USA)

Abstract

In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However, relying on a single scoring approach may bias the loan evaluation conclusion. In this paper, we propose a bivariate model based on the integration of two scoring approaches. We first formulate the loan evaluation task as a multi-target problem, in which loan_status (i.e., default or not default) is used as the discrete outcome for the credit risk measure while the annualized rate of return ( ARR ) is used as the continuous outcome for the profitability measure. Then to solve the multi-target problem, we design a novel loss function based on the assumption that the discrete outcome follows a Bernoulli distribution, and the continuous outcome is normally distributed conditional on the discrete output. The effectiveness of the proposed model is examined using the real-world P2P data from the Lending Club. Results indicate that our approach outperforms the sole scoring methods by identifying loans with higher profit and lower default risk. Therefore, the proposed method can serve as an alternative for loan evaluation.

Suggested Citation

  • Yan Wang & Xuelei Sherry Ni & Huan Ni & Sanad Biswas, 2025. "A Bivariate Model for Correlated and Mixed Outcomes: A Case Study on the Simultaneous Prediction of Credit Risk and Profitability of Peer-to-Peer (P2P) Loans," Risks, MDPI, vol. 13(2), pages 1-18, February.
  • Handle: RePEc:gam:jrisks:v:13:y:2025:i:2:p:33-:d:1589846
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/13/2/33/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/13/2/33/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Wolfgang Pointner & Burkhard Raunig, 2018. "A primer on peer-to-peer lending: immediate financial intermediation in practice," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/18, pages 36-51.
    3. Zhou Rongxi & Xiong Yahui & Wang Ning & Wang Xizu, 2019. "Coupling Degree Evaluation of China’s Internet Financial Ecosystem Based on Entropy Method and Principal Component Analysis," Journal of Systems Science and Information, De Gruyter, vol. 7(5), pages 399-421, October.
    4. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    5. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    6. Kräussl, Roman & Kräussl, Zsofia & Pollet, Joshua & Rinne, Kalle, 2024. "The performance of marketplace lenders," Journal of Banking & Finance, Elsevier, vol. 162(C).
    7. Yeh, Jen-Yin & Chiu, Hsin-Yu & Huang, Jhih-Huei, 2024. "Predicting failure of P2P lending platforms through machine learning: The case in China," Finance Research Letters, Elsevier, vol. 59(C).
    8. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    9. Aneta Dzik-Walczak & Mateusz Heba, 2019. "A comparison of credit scoring techniques in Peer-to-Peer lending," Working Papers 2019-16, Faculty of Economic Sciences, University of Warsaw.
    10. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    11. Käfer Benjamin, 2018. "Peer-to-Peer Lending – A (Financial Stability) Risk Perspective," Review of Economics, De Gruyter, vol. 69(1), pages 1-25, April.
    12. Dorfleitner, Gregor & Rad, Jacqueline & Weber, Martina, 2017. "Pricing in the online invoice trading market: First empirical evidence," Economics Letters, Elsevier, vol. 161(C), pages 56-61.
    13. Xi Yang & Wenjuan Fan & Shanlin Yang, 2020. "Identifying the Influencing Factors on Investors’ Investment Behavior: An Empirical Study Focusing on the Chinese P2P Lending Market," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    14. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    15. Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    16. Wang, Qi & Xiong, Xiong & Zheng, Zunxin, 2021. "Platform Characteristics and Online Peer-to-Peer Lending: Evidence from China," Finance Research Letters, Elsevier, vol. 38(C).
    17. Sajjad Taghiyeh & David C Lengacher & Robert B Handfield, 2020. "Loss Rate Forecasting Framework Based on Macroeconomic Changes: Application to US Credit Card Industry," Papers 2006.07911, arXiv.org.
    18. Wu, Yu & Zhang, Tong, 2021. "Can credit ratings predict defaults in peer-to-peer online lending? Evidence from a Chinese platform," Finance Research Letters, Elsevier, vol. 40(C).
    19. Tatjana Miljkovic & Pei Wang, 2025. "A dimension reduction assisted credit scoring method for big data with categorical features," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-30, December.
    20. Eid, Nourhan & Maltby, Josephine & Talavera, Oleksandr, 2016. "Income Rounding and Loan Performance in the Peer-to-Peer Market," MPRA Paper 72852, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:13:y:2025:i:2:p:33-:d:1589846. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.