Improving credit scoring by differentiating defaulter behaviour

Cristian Bravo, Lyn C Thomas and Richard Weber
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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

Journal of the Operational Research Society, 2015, vol. 66, issue 5, 771-781

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.

Date: 2015
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