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Latent Factor Models for Credit Scoring in P2P Systems

Author

Listed:
  • Ahelegbey, Daniel Felix
  • Giudici, Paolo
  • Hadji-Misheva, Branka
Abstract
Peer-to-Peer (P2P) fintech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models.

Suggested Citation

  • Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2018. "Latent Factor Models for Credit Scoring in P2P Systems," MPRA Paper 92636, University Library of Munich, Germany, revised 11 Oct 2018.
    • Handle: RePEc:pra:mprapa:92636
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    References listed on IDEAS

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    Cited by:

    1. Chen, Xiao & Chong, Zhaohui & Giudici, Paolo & Huang, Bihong, 2022. "Network centrality effects in peer to peer lending," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. 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).
    3. Tang, Xinyin & Feng, Chong & Zhu, Jianping & He, Minna, 2022. "How Can We Learn from Borrowers’ Online Behaviors? The Signal Effect of Borrowers’ Platform Involvement on Their Credit Risk," SocArXiv qga8j, Center for Open Science.
    4. Jiang, Cuiqing & Yin, Chang & Tang, Qian & Wang, Zhao, 2023. "The value of official website information in the credit risk evaluation of SMEs," Journal of Business Research, Elsevier, vol. 169(C).
    5. Nigmonov, Asror & Shams, Syed & Alam, Khorshed, 2024. "Liquidity risk in FinTech lending: Early impact of the COVID-19 pandemic on the P2P lending market," Emerging Markets Review, Elsevier, vol. 58(C).
    6. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    7. Leite, Rodrigo & Mendes, Layla & Camelo, Emmanuel, 2024. "Innovating microcredit: how fintechs change the field," Journal of Economics and Business, Elsevier, vol. 128(C).
    8. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Factorial Network Models To Improve P2P Credit Risk Management," MPRA Paper 92633, University Library of Munich, Germany.
    9. Ahelegbey, Daniel & Giudici, Paolo & Pediroda, Valentino, 2023. "A network based fintech inclusion platform," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    10. Liu, Yiting & Baals, Lennart John & Osterrieder, Jörg & Hadji-Misheva, Branka, 2024. "Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics," Finance Research Letters, Elsevier, vol. 63(C).

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    More about this item

    Keywords

    Credit Risk; Factor Models; Financial Technology; Peer-to-Peer; Scoring Models; Spatial Clustering;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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