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A new mixture cure model under competing risks to score online consumer loans

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  • Nailong Zhang
  • Qingyu Yang
  • Aidan Kelleher
  • Wujun Si

Abstract

In credit scoring, survival analysis models have been widely applied to answer the question as to whether and when an applicant would default. In this paper, we propose a novel mixture cure proportional hazards model under competing risks. Most existing mixture cure models either do not consider competing risks or generally assume that a subpopulation of subjects is immune to any risk from all the competing risks. Compared with existing models, the proposed model is more flexible since it assumes that a subpopulation of subjects is immune to a subset of risks instead of being immune to all the risks. To estimate model parameters, we derive the likelihood function of the proposed model, based on which an expectation maximization estimation algorithm is developed. A simulation algorithm is designed to simulate time-to-event observations from the proposed model, and simulation studies are conducted to verify the proposed methodology. A real world example of credit scoring for online customer loans based on the proposed model is demonstrated.

Suggested Citation

  • Nailong Zhang & Qingyu Yang & Aidan Kelleher & Wujun Si, 2019. "A new mixture cure model under competing risks to score online consumer loans," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1243-1253, July.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:7:p:1243-1253
    DOI: 10.1080/14697688.2018.1552791
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    Cited by:

    1. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    2. Yang, Qi & He, Haijin & Lu, Bin & Song, Xinyuan, 2022. "Mixture additive hazards cure model with latent variables: Application to corporate default data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    3. Jackson P. Lautier & Vladimir Pozdnyakov & Jun Yan, 2022. "On the Convergence of Credit Risk in Current Consumer Automobile Loans," Papers 2211.09176, arXiv.org, revised Jan 2024.
    4. Lohmann, Christian & Ohliger, Thorsten, 2024. "Predicting the cure of a defaulted company: Nonlinear relationships between loan-related variables and the cure probability," Research in International Business and Finance, Elsevier, vol. 70(PB).

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