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A hierarchical mixture cure model with unobserved heterogeneity for credit risk

Author

Listed:
  • Lore Dirick
  • Gerda Claeskens
  • Andrey Vasnev
  • Bart Baesens

Abstract

The specific nature of credit loan data requires the use of mixture cure models within the class of survival analysis tools. The constructed models allow for competing risks such as early repayment and default, and for incorporating maturity, expressed as an unsusceptible part of the population. A novel further extension of such models incorporates unobserved heterogeneity within the risk groups. A hierarchical expectation-maximization algorithm is derived to fit the models and standard errors are obtained. Simulations and a data analysis illustrate the applicability and benefits of these models, and in particular an improved event time estimation.
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Suggested Citation

  • Lore Dirick & Gerda Claeskens & Andrey Vasnev & Bart Baesens, 2020. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Working Papers of Department of Decision Sciences and Information Management, Leuven 665250, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
    • Handle: RePEc:ete:kbiper:665250
    Note: paper number KBI_2007
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    File URL: https://lirias.kuleuven.be/retrieve/1d39d643-ebb9-4634-9363-70280ead9678
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    Cited by:

    1. Andrea Marletta, 2024. "How to control the effectiveness of a campaign of mailing list marketing: a proposal based on survival analysis," Annals of Operations Research, Springer, vol. 342(3), pages 1581-1604, November.
    2. Cedric H. A. Koffi & Viani Biatat Djeundje & Olivier Menoukeu Pamen, 2024. "Quantifying socio-temporal effects of loan delinquency drivers in microfinance," Papers 2410.13100, arXiv.org, revised Aug 2025.
    3. Jiacheng Xue & Weixin Yao & Sijia Xiang, 2025. "Machine learning embedded EM algorithms for semiparametric mixture regression models," Computational Statistics, Springer, vol. 40(1), pages 205-224, January.

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