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Neural Network Survival Analysis for Personal Loan Data

Author

Listed:
  • B. BAESENS
  • T. VAN GESTEL
  • M. STEPANOVA
  • D. VAN DEN POEL

    ()

Abstract

Traditionally, credit scoring aimed at distinguishing good payers from bad payers at the time of the application. The timing when customers default is also interesting to investigate since it can provide the bank with the ability to do profit scoring. Analysing when customers default is typically tackled using survival analysis. In this paper, we discuss and contrast statistical and neural network approaches for survival analysis. Compared to the proportional hazards model, neural networks may offer an interesting alternative because of their universal approximation property and the fact that no baseline hazard assumption is needed. Several neural network survival analysis models are discussed and evaluated according to their way of dealing with censored observations, time-varying inputs, the monotonicity of the generated survival curves and their scalability. In the experimental, we contrast the performance of a neural network survival analysis model with that of the proportional hazards model for predicting both loan default and early repayment using data from a U.K. financial institution.

Suggested Citation

  • B. Baesens & T. Van Gestel & M. Stepanova & D. Van Den Poel, 2004. "Neural Network Survival Analysis for Personal Loan Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/281, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:04/281
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    File URL: http://wps-feb.ugent.be/Papers/wp_04_281.pdf
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    References listed on IDEAS

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    1. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    2. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
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    Cited by:

    1. Pisanets Konstantin K., 2013. "Models of Assessment of the Credit Risk of Borrowers with a Time Parameter for the Systems of Application Credit Scoring," Business Inform, RESEARCH CENTRE FOR INDUSTRIAL DEVELOPMENT PROBLEMS of NAS (KHARKIV, UKRAINE), Kharkiv National University of Economics, issue 7, pages 136-140.
    2. Okumu Argan Wekesa & Mwalili Samuel & Mwita Peter, 2012. "Modelling Credit Risk for Personal Loans Using Product-Limit Estimator," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(1), pages 22-32, January.
    3. 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.

    More about this item

    Keywords

    credit scoring; survival analysis; neural networks;

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