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Investment decision making for large-scale Peer-to-Peer lending data: A Bayesian Neural Network approach

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  • Guo, Yanhong
  • Zhai, Yonghui
  • Jiang, Shuai

Abstract

Peer-to-Peer (P2P) lending, as a pivotal innovation in the financial sector, presents both significant opportunities and complex challenges for portfolio management. This study introduces an advanced P2P lending portfolio optimization model that integrates Bayesian Neural Networks (BNNs) within the Neural Additive Model (NAM) framework to address these challenges. Our primary objective is to enhance the interpretability and operational efficacy of large-scale P2P lending portfolios. By leveraging BNNs, we not only predict returns but also quantify uncertainty to assess loan risks effectively. To augment the model’s transparency, NAMs are employed to elucidate the impact of various features on investment outcomes. Subsequently, a genetic algorithm optimizes the allocation of investment weights, ensuring maximum profitability. The proposed strategy is validated using real-world P2P lending data, demonstrating superior performance compared to traditional benchmarks in predicting P2P lending profits. Empirical evidence suggests that our approach significantly enhances investment returns by facilitating informed decision-making. This research provides actionable insights for investors in the P2P lending domain and represents a substantial advancement in risk management and decision-making through the innovative application of BNNs and NAMs.

Suggested Citation

  • Guo, Yanhong & Zhai, Yonghui & Jiang, Shuai, 2025. "Investment decision making for large-scale Peer-to-Peer lending data: A Bayesian Neural Network approach," International Review of Financial Analysis, Elsevier, vol. 102(C).
    • Handle: RePEc:eee:finana:v:102:y:2025:i:c:s1057521925001875
    DOI: 10.1016/j.irfa.2025.104100
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    1. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
    2. Jiaming Liu & Xuemei Zhang & Haitao Xiong, 2024. "Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1625-1660, August.
    3. 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.
    4. Mild, Andreas & Waitz, Martin & Wöckl, Jürgen, 2015. "How low can you go? — Overcoming the inability of lenders to set proper interest rates on unsecured peer-to-peer lending markets," Journal of Business Research, Elsevier, vol. 68(6), pages 1291-1305.
    5. Julien Chevallier & Bangzhu Zhu & Lyuyuan Zhang, 2021. "Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms," Post-Print halshs-04250258, HAL.
    6. Golnoosh Babaei & Shahrooz Bamdad, 2021. "A New Hybrid Instance-Based Learning Model for Decision-Making in the P2P Lending Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 419-432, January.
    7. Dangxing Chen & Weicheng Ye, 2022. "Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring," Papers 2209.10070, arXiv.org.
    8. Batrancea Larissa-Margareta & Batrancea Ioan & Moscviciov Andrei, 2009. "The Roots Of The World Financial Crisis," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 3(1), pages 57-62, May.
    9. 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.
    10. Leung, Melvern & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A., 2021. "Bayesian Value-at-Risk backtesting: The case of annuity pricing," European Journal of Operational Research, Elsevier, vol. 293(2), pages 786-801.
    11. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.
    12. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
    13. Chee Kian Leong, 2016. "Credit Risk Scoring with Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 423-446, March.
    14. Ömer Akgüller & Mehmet Ali Balcı & Larissa M. Batrancea & Lucian Gaban, 2023. "Path-Based Visibility Graph Kernel and Application for the Borsa Istanbul Stock Network," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    15. Zhou, Jing & Li, Wei & Wang, Jiaxin & Ding, Shuai & Xia, Chengyi, 2019. "Default prediction in P2P lending from high-dimensional data based on machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    16. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    17. Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding, 2020. "Predicting loan default in peer‐to‐peer lending using narrative data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 260-280, March.
    18. 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.
    19. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    20. Julien Chevallier & Bangzhu Zhu & Lyuyuan Zhang, 2021. "Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 537-575, February.
    21. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    22. R T Stewart, 2011. "A profit-based scoring system in consumer credit: making acquisition decisions for credit cards," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1719-1725, September.
    23. Golnoosh Babaei & Shahrooz Bamdad, 2020. "A neural‐network‐based decision‐making model in the peer‐to‐peer lending market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 142-150, July.
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