IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v241y2015i1p202-211.html
   My bibliography  Save this article

Identifying future defaulters: A hierarchical Bayesian method

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221714006432
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2014.08.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Steven Finlay, 2010. "Credit Scoring, Response Modelling and Insurance Rating," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-29898-9.
    2. Peng, Yingwei, 2003. "Estimating baseline distribution in proportional hazards cure models," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 187-201, February.
    3. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    4. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    5. Makoto Abe, 2009. "“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 28(3), pages 541-553, 05-06.
    6. Altuzarra, Alfredo & Moreno-Jimenez, Jose Maria & Salvador, Manuel, 2007. "A Bayesian priorization procedure for AHP-group decision making," European Journal of Operational Research, Elsevier, vol. 182(1), pages 367-382, October.
    7. D J Hand & M G Kelly, 2001. "Lookahead scorecards for new fixed term credit products," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 989-996, September.
    8. Yingwei Peng & Keith B. G. Dear, 2000. "A Nonparametric Mixture Model for Cure Rate Estimation," Biometrics, The International Biometric Society, vol. 56(1), pages 237-243, March.
    9. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    10. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    11. J Banasik & J N Crook & L C Thomas, 1999. "Not if but when will borrowers default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1185-1190, December.
    12. Judy P. Sy & Jeremy M. G. Taylor, 2000. "Estimation in a Cox Proportional Hazards Cure Model," Biometrics, The International Biometric Society, vol. 56(1), pages 227-236, March.
    13. Andreeva, Galina & Ansell, Jake & Crook, Jonathan, 2007. "Modelling profitability using survival combination scores," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1537-1549, December.
    14. Pang, Wan-Kai & Leung, Ping-Kei & Huang, Wei-Kwang & Liu, Wei, 2005. "On interval estimation of the coefficient of variation for the three-parameter Weibull, lognormal and gamma distribution: A simulation-based approach," European Journal of Operational Research, Elsevier, vol. 164(2), pages 367-377, July.
    15. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    16. Yildiray Yildirim, 2008. "Estimating Default Probabilities of CMBS Loans with Clustering and Heavy Censoring," The Journal of Real Estate Finance and Economics, Springer, vol. 37(2), pages 93-111, August.
    17. 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.
    18. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    19. Velarde, Luis Guillermo C. & Migon, Helio S. & Alcoforado, David A., 2008. "Hierarchical Bayesian models applied to air surveillance radars," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1155-1162, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    3. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    4. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    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. Naveed Chehrazi & Thomas A. Weber, 2015. "Dynamic Valuation of Delinquent Credit-Card Accounts," Management Science, INFORMS, vol. 61(12), pages 3077-3096, December.
    7. repec:syb:wpbsba:03/2013 is not listed on IDEAS
    8. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
    9. Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
    10. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    11. Calabrese, Raffaella & Crook, Jonathan, 2020. "Spatial contagion in mortgage defaults: A spatial dynamic survival model with time and space varying coefficients," European Journal of Operational Research, Elsevier, vol. 287(2), pages 749-761.
    12. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    13. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    14. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
    15. G Andreeva, 2006. "European generic scoring models using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1180-1187, October.
    16. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020.
    17. Richard Chamboko & Jorge Miguel Bravo, 2020. "A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes," Risks, MDPI, vol. 8(2), pages 1-29, June.
    18. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    19. Mariusz Górajski & Dobromił Serwa & Zuzanna Wośko, 2019. "Measuring expected time to default under stress conditions for corporate loans," Empirical Economics, Springer, vol. 57(1), pages 31-52, July.
    20. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.
    21. Medina-Olivares, Victor & Lindgren, Finn & Calabrese, Raffaella & Crook, Jonathan, 2023. "Joint models of multivariate longitudinal outcomes and discrete survival data with INLA: An application to credit repayment behaviour," European Journal of Operational Research, Elsevier, vol. 310(2), pages 860-873.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:241:y:2015:i:1:p:202-211. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.