Improving credit scoring by differentiating defaulter behaviour

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.

Keywords:

• credit scoring
• statistics
• domain-knowledge
• constrained clustering
• banking
• game theory

Acknowledgements

The first author acknowledges CONICYT for the grants that support this work (AT-24110006, NAC-DOC: 21090573) and the PhD in Engineering Systems, Universidad de Chile. All authors acknowledge the support of the institution that provided the data. The work reported in this paper has been partially funded by the Institute of Complex Engineering Systems (ICM: P-05-004-F, CONICYT: FBO16) and the Finance Center of the Department of Industrial Engineering, Universidad de Chile, with the support of Bank Bci.