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A time-dependent proportional hazards survival model for credit risk analysis

Author

Listed:
  • J-K Im

    (Northwestern University, Evanston, IL, USA)

  • D W Apley

    (Northwestern University, Evanston, IL, USA)

  • C Qi

    (Consultant, Lake Forest, IL, USA)

  • X Shan

    (Consultant, Deerfield, IL, USA)

Abstract

In the consumer credit industry, assessment of default risk is critically important for the financial health of both the lender and the borrower. Methods for predicting risk for an applicant using credit bureau and application data, typically based on logistic regression or survival analysis, are universally employed by credit card companies. Because of the manner in which the predictive models are fit using large historical sets of existing customer data that extend over many years, default trends, anomalies, and other temporal phenomena that result from dynamic economic conditions are not brought to light. We introduce a modification of the proportional hazards survival model that includes a time-dependency mechanism for capturing temporal phenomena, and we develop a maximum likelihood algorithm for fitting the model. Using a very large, real data set, we demonstrate that incorporating the time dependency can provide more accurate risk scoring, as well as important insight into dynamic market effects that can inform and enhance related decision making.

Suggested Citation

  • J-K Im & D W Apley & C Qi & X Shan, 2012. "A time-dependent proportional hazards survival model for credit risk analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(3), pages 306-321, March.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:3:p:306-321
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    Cited by:

    1. 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.
    2. Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.
    3. Azamat Abdymomunov & Sharon Blei & Bakhodir Ergashev, 2015. "Integrating Stress Scenarios into Risk Quantification Models," Journal of Financial Services Research, Springer;Western Finance Association, vol. 47(1), pages 57-79, February.
    4. Xiangxing Tao & Mingxin Wang & Yanting Ji, 2023. "The Application of Graph-Structured Cox Model in Financial Risk Early Warning of Companies," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    5. 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.
    6. 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.
    7. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.
    8. Aimée Backiel & Bart Baesens & Gerda Claeskens, 2016. "Predicting time-to-churn of prepaid mobile telephone customers using social network analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1135-1145, September.

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