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Can small sample dataset be used for efficient internet loan credit risk assessment? Evidence from online peer to peer lending

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  • Yu, Lean
  • Zhang, Xiaoming

Abstract

The emerging online peer to peer (P2P) lending platforms have only a small number of samples in the early stage, it is thus unable to conduct an efficient credit risk assessment on internet loan applicants. In order to solve the sample shortage issue, a virtual sample generation (VSG) methodology integrating multi-distribution mega-trend-diffusion (MD-MTD) and particle swarm optimization (PSO) algorithm is proposed for internet loan credit risk evaluation with small samples. The empirical results indicate that the proposed VSG methodology can greatly help to improve performance of the internet loan credit risk evaluation with small sample datasets.

Suggested Citation

  • Yu, Lean & Zhang, Xiaoming, 2021. "Can small sample dataset be used for efficient internet loan credit risk assessment? Evidence from online peer to peer lending," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319312267
    DOI: 10.1016/j.frl.2020.101521
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    References listed on IDEAS

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    1. 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.
    2. Dernoncourt, David & Hanczar, Blaise & Zucker, Jean-Daniel, 2014. "Analysis of feature selection stability on high dimension and small sample data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 681-693.
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    Cited by:

    1. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    2. Lean Yu & Lihang Yu & Kaitao Yu, 2021. "A high-dimensionality-trait-driven learning paradigm for high dimensional credit classification," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.

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