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Development and application of consumer credit scoring models using profit-based classification measures

Development and application of consumer credit scoring models using profit-based classification measures
Development and application of consumer credit scoring models using profit-based classification measures
This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers' objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses -- driven by the exposure of the loan and the loss given default -- and the operational income given by the loan. Additionally, one of the major advantages of using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment.
0377-2217
505-513
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Verbraken, Thomas, Bravo, Cristian, Weber, Richard and Baesens, Bart (2014) Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research, 238 (2), 505-513. (doi:10.1016/j.ejor.2014.04.001).

Record type: Article

Abstract

This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers' objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses -- driven by the exposure of the loan and the loss given default -- and the operational income given by the loan. Additionally, one of the major advantages of using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment.

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Accepted/In Press date: 3 April 2014
e-pub ahead of print date: 13 April 2014
Published date: 16 October 2014
Organisations: Centre of Excellence in Decision, Analytics & Risk Research

Identifiers

Local EPrints ID: 403263
URI: https://eprints.soton.ac.uk/id/eprint/403263
ISSN: 0377-2217
PURE UUID: e338e460-2f1e-4009-8186-396831bdbb93
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

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Date deposited: 29 Nov 2016 13:31
Last modified: 22 May 2019 00:33

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Contributors

Author: Thomas Verbraken
Author: Cristian Bravo ORCID iD
Author: Richard Weber
Author: Bart Baesens

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