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Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference

Author

Listed:
  • Monir El Annas

    (Institut National de Statistique et d’Economie Appliquée)

  • Badreddine Benyacoub

    (Institut National de Statistique et d’Economie Appliquée)

  • Mohamed Ouzineb

    (Institut National de Statistique et d’Economie Appliquée)

Abstract

The majority of current credit-scoring models, used for loan approval processing, are generally built on the basis of the information from the accepted credit applicants whose ability to repay the loan is known. This situation generates what is called the selection bias, presented by a sample that is not representative of the population of applicants, since rejected applications are excluded. Thus, the impact on the eligibility of those models from a statistical and economic point of view. Especially for the models used in the peer-to-peer lending platforms, since their rejection rate is extremely high. The method of inferring rejected applicants information in the process of construction of the credit scoring models is known as reject inference. This study proposes a semi-supervised learning framework based on hidden Markov models (SSHMM), as a novel method of reject inference. Real data from the Lending Club platform, the most used online lending marketplace in the United States as well as the rest of the world, is used to experiment the effectiveness of our method over existing approaches. The results of this study clearly illustrate the proposed method’s superiority, stability, and adaptability.

Suggested Citation

  • Monir El Annas & Badreddine Benyacoub & Mohamed Ouzineb, 2023. "Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference," Computational Statistics, Springer, vol. 38(1), pages 149-169, March.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:1:d:10.1007_s00180-022-01220-9
    DOI: 10.1007/s00180-022-01220-9
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    References listed on IDEAS

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    1. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    2. Banasik, John & Crook, Jonathan, 2007. "Reject inference, augmentation, and sample selection," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1582-1594, December.
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    7. Nikita Kozodoi & Panagiotis Katsas & Stefan Lessmann & Luis Moreira-Matias & Konstantinos Papakonstantinou, 2019. "Shallow Self-Learning for Reject Inference in Credit Scoring," Papers 1909.06108, arXiv.org.
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