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Identifying future defaulters: A hierarchical Bayesian method

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  • Liu, Fan
  • Hua, Zhongsheng
  • Lim, Andrew

Abstract

Traditional methods of applying classification models into the area of credit scoring may ignore the effect from censoring. Survival analysis has been introduced with its ability to deal with censored data. The mixture cure model, one important branch of survival models, is also applied in the context of credit scoring, assuming that the study population is a mixture of never-default and will-default customers.

Suggested Citation

  • Liu, Fan & Hua, Zhongsheng & Lim, Andrew, 2015. "Identifying future defaulters: A hierarchical Bayesian method," European Journal of Operational Research, Elsevier, vol. 241(1), pages 202-211.
    • Handle: RePEc:eee:ejores:v:241:y:2015:i:1:p:202-211
    DOI: 10.1016/j.ejor.2014.08.008
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    References listed on IDEAS

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    Cited by:

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    2. Silva, Thiago Christiano & Guerra, Solange Maria & Tabak, Benjamin Miranda, 2020. "Fiscal risk and financial fragility," Emerging Markets Review, Elsevier, vol. 45(C).
    3. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    4. Yao, Yiyu & Zhou, Bing, 2016. "Two Bayesian approaches to rough sets," European Journal of Operational Research, Elsevier, vol. 251(3), pages 904-917.
    5. Bhattacharya, Arnab & Wilson, Simon P. & Soyer, Refik, 2019. "A Bayesian approach to modeling mortgage default and prepayment," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1112-1124.
    6. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    7. Mukhoti, Sujay & Guhathakurta, Kousik, 2015. "Product market performance and capital structure: A Hierarchical Bayesian semi-parametric panel regression model," MPRA Paper 62517, University Library of Munich, Germany.

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