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Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C

Listed author(s):
  • Matuszyk, Anna
  • So, Mee Chi
  • Mues, Christophe
  • Moore, Angela
Registered author(s):

    One approach to modelling Loss Given Default (LGD), the percentage of the defaulted amount of a loan that a lender will eventually lose is to model the collections process. This is particularly relevant for unsecured consumer loans where LGD depends both on a defaulter's ability and willingness to repay and the lender's collection strategy. When repaying such defaulted loans, defaulters tend to oscillate between repayment sequences where the borrower is repaying every period and non-repayment sequences where the borrower is not repaying in any period. This paper develops two models – one a Markov chain approach and the other a hazard rate approach to model such payment patterns of debtors. It also looks at simplifications of the models where one assumes that after a few repayment and non-repayment sequences the parameters of the model are fixed for the remaining payment and non-payment sequences. One advantage of these approaches is that they show the impact of different write-off strategies. The models are applied to a real case study and the LGD for that portfolio is calculated under different write-off strategies and compared with the actual LGD results.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221715008371
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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 249 (2016)
    Issue (Month): 2 ()
    Pages: 476-486

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    Handle: RePEc:eee:ejores:v:249:y:2016:i:2:p:476-486
    DOI: 10.1016/j.ejor.2015.09.013
    Contact details of provider: Web page: http://www.elsevier.com/locate/eor

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    1. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    2. Breeden, Joseph L., 2007. "Modeling data with multiple time dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4761-4785, May.
    3. Alexandra Schwarz, 2011. "Measurement, Monitoring, and Forecasting of Consumer Credit Default Risk - An Indicator Approach Based on Individual Payment Histories," Schumpeter Discussion Papers sdp11004, Universitätsbibliothek Wuppertal, University Library.
    4. R. M. Cyert & H. J. Davidson & G. L. Thompson, 1962. "Estimation of the Allowance for Doubtful Accounts by Markov Chains," Management Science, INFORMS, vol. 8(3), pages 287-303, April.
    5. Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2012. "Survival Analysis in LGD Modeling," European Financial and Accounting Journal, University of Economics, Prague, vol. 2012(1), pages 6-27.
    6. Thomas, Lyn C., 2009. "Consumer Credit Models: Pricing, Profit and Portfolios," OUP Catalogue, Oxford University Press, number 9780199232130.
    7. Han, Chulwoo & Jang, Youngmin, 2013. "Effects of debt collection practices on loss given default," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 21-31.
    8. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
    9. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
    10. Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1219-1243, April.
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