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Improving credit scoring by differentiating defaulter behaviour

Author

Listed:
  • Cristian Bravo

    (1] Departamento de Modelamiento y Gestión Industrial, Universidad de Talca, Curicó, Chile[2] Department of Industrial Engineering, Universidad de Chile, Santiago, Chile)

  • Lyn C Thomas

    (Southampton School of Management, University of Southampton. Highfield, Southampton, UK)

  • Richard Weber

    (Department of Industrial Engineering, Universidad de Chile, Santiago, Chile)

Abstract

We present a methodology for improving credit scoring models by distinguishing two forms of rational behaviour of loan defaulters. It is common knowledge among practitioners that there are two types of defaulters, those who do not pay because of cash flow problems (‘Can’t Pay’), and those that do not pay because of lack of willingness to pay (‘Won’t Pay’). This work proposes to differentiate them using a game theory model that describes their behaviour. This separation of behaviours is represented by a set of constraints that form part of a semi-supervised constrained clustering algorithm, constructing a new target variable summarizing relevant future information. Within this approach the results of several supervised models are benchmarked, in which the models deliver the probability of belonging to one of these three new classes (good payers, ‘Can’t Pays’, and ‘Won’t Pays’). The process improves classification accuracy significantly, and delivers strong insights regarding the behaviour of defaulters.

Suggested Citation

  • Cristian Bravo & Lyn C Thomas & Richard Weber, 2015. "Improving credit scoring by differentiating defaulter behaviour," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 771-781, May.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:5:p:771-781
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    Citations

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    Cited by:

    1. Arno Botha & Conrad Beyers & Pieter de Villiers, 2019. "A procedure for loss-optimising default definitions across simulated credit risk scenarios," Papers 1907.12615, arXiv.org, revised Feb 2021.
    2. Luisa Roa & Alejandro Correa-Bahnsen & Gabriel Suarez & Fernando Cort'es-Tejada & Mar'ia A. Luque & Cristi'an Bravo, 2020. "Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications," Papers 2005.14658, arXiv.org, revised Jan 2021.

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