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Improving credit scoring by differentiating default behaviour

Improving credit scoring by differentiating default behaviour
Improving credit scoring by differentiating default behaviour
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
credit scoring, statistics, domain-knowledge, constrained clustering, banking, game theory
0160-5682
771-781
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b

Bravo, Cristian, Thomas, Lyn C. and Weber, Richard (2015) Improving credit scoring by differentiating default behaviour. Journal of the Operational Research Society, 66, 771-781. (doi:10.1057/jors.2014.50).

Record type: Article

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

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More information

Accepted/In Press date: 25 March 2014
e-pub ahead of print date: May 2015
Published date: May 2015
Keywords: credit scoring, statistics, domain-knowledge, constrained clustering, banking, game theory
Organisations: Centre of Excellence in Decision, Analytics & Risk Research

Identifiers

Local EPrints ID: 376179
URI: http://eprints.soton.ac.uk/id/eprint/376179
ISSN: 0160-5682
PURE UUID: 842c7f54-7938-4982-83ce-4530138c14b3
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 24 Apr 2015 13:17
Last modified: 17 Dec 2019 01:42

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