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Deep learning for credit scoring: Do or don’t?

Deep learning for credit scoring: Do or don’t?
Deep learning for credit scoring: Do or don’t?

Developing accurate analytical credit scoring models has become a major focus for financial institutions. For this purpose, numerous classification algorithms have been proposed for credit scoring. However, the application of deep learning algorithms for classification has been largely ignored in the credit scoring literature. The main motivation for this research is to consider the appropriateness of deep learning algorithms for credit scoring. To this end two deep learning architectures are constructed, namely a multilayer perceptron network and a deep belief network, and their performance compared to that of two conventional methods and two ensemble methods for credit scoring. The models are then evaluated using a range of credit scoring data sets and performance measures. Furthermore, Bayesian statistical testing procedures are introduced in the context of credit scoring and compared to frequentist non-parametric testing procedures which have traditionally been considered best practice in credit scoring. This comparison will highlight the benefits of Bayesian statistical procedures and secure empirical findings. Two main conclusions emerge from comparing the different classification algorithms for credit scoring. Firstly, the ensemble method, XGBoost, is the best performing method for credit scoring of all the methods considered here. Secondly, deep neural networks do not outperform their shallower counterparts and are considerably more computationally expensive to construct. Therefore, deep learning algorithms do not seem to be appropriate models for credit scoring based on this comparison and XGBoost should be preferred over the other credit scoring methods considered here when classification performance is the main objective of credit scoring activities.

Bayesian statistical testing, Credit scoring, Decision support systems, Deep learning, Risk analysis
0377-2217
292-305
Gunnarsson, Björn Rafn
9dbf0abc-544e-4db3-ace1-6745b78ede82
Vanden Broucke, Seppe
89c69367-232e-4c1e-9e57-531bf474e12d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068
Gunnarsson, Björn Rafn
9dbf0abc-544e-4db3-ace1-6745b78ede82
Vanden Broucke, Seppe
89c69367-232e-4c1e-9e57-531bf474e12d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Óskarsdóttir, María
1622b6dd-5d25-4228-9418-a1729e9577e0
Lemahieu, Wilfried
be4bae3f-12b9-417a-91a1-c3c264ffe068

Gunnarsson, Björn Rafn, Vanden Broucke, Seppe, Baesens, Bart, Óskarsdóttir, María and Lemahieu, Wilfried (2021) Deep learning for credit scoring: Do or don’t? European Journal of Operational Research, 295 (1), 292-305. (doi:10.1016/j.ejor.2021.03.006).

Record type: Article

Abstract

Developing accurate analytical credit scoring models has become a major focus for financial institutions. For this purpose, numerous classification algorithms have been proposed for credit scoring. However, the application of deep learning algorithms for classification has been largely ignored in the credit scoring literature. The main motivation for this research is to consider the appropriateness of deep learning algorithms for credit scoring. To this end two deep learning architectures are constructed, namely a multilayer perceptron network and a deep belief network, and their performance compared to that of two conventional methods and two ensemble methods for credit scoring. The models are then evaluated using a range of credit scoring data sets and performance measures. Furthermore, Bayesian statistical testing procedures are introduced in the context of credit scoring and compared to frequentist non-parametric testing procedures which have traditionally been considered best practice in credit scoring. This comparison will highlight the benefits of Bayesian statistical procedures and secure empirical findings. Two main conclusions emerge from comparing the different classification algorithms for credit scoring. Firstly, the ensemble method, XGBoost, is the best performing method for credit scoring of all the methods considered here. Secondly, deep neural networks do not outperform their shallower counterparts and are considerably more computationally expensive to construct. Therefore, deep learning algorithms do not seem to be appropriate models for credit scoring based on this comparison and XGBoost should be preferred over the other credit scoring methods considered here when classification performance is the main objective of credit scoring activities.

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DeepLearningforCreditScoring_nohighlights - Accepted Manuscript
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More information

Accepted/In Press date: 4 March 2021
e-pub ahead of print date: 10 March 2021
Additional Information: Publisher Copyright: © 2021 Elsevier B.V. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Bayesian statistical testing, Credit scoring, Decision support systems, Deep learning, Risk analysis

Identifiers

Local EPrints ID: 448931
URI: http://eprints.soton.ac.uk/id/eprint/448931
ISSN: 0377-2217
PURE UUID: 29070dbb-211e-4227-8a79-8d666a34b70d
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

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Date deposited: 11 May 2021 16:48
Last modified: 28 Apr 2022 01:53

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Contributors

Author: Björn Rafn Gunnarsson
Author: Seppe Vanden Broucke
Author: Bart Baesens ORCID iD
Author: María Óskarsdóttir
Author: Wilfried Lemahieu

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