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Credit scoring: a review on support vector machines and metaheuristic approaches

Development of credit scoring models is important for fnancial institutions to identify defaulters and nondefaulters when making credit granting decisions. In recent years, artifcial intelligence (AI) techniques have shown successful performance in credit scoring. Support Vector Machines and metaheu...

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Main Authors: Goh, Rui Ying, Lee, Lai Soon
Format: Article
Language:English
Published: Hindawi 2019
Online Access:http://psasir.upm.edu.my/id/eprint/81046/1/SCORING.pdf
http://psasir.upm.edu.my/id/eprint/81046/
https://www.hindawi.com/journals/aor/2019/1974794/
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id my.upm.eprints.81046
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spelling my.upm.eprints.810462020-10-14T21:01:31Z http://psasir.upm.edu.my/id/eprint/81046/ Credit scoring: a review on support vector machines and metaheuristic approaches Goh, Rui Ying Lee, Lai Soon Development of credit scoring models is important for fnancial institutions to identify defaulters and nondefaulters when making credit granting decisions. In recent years, artifcial intelligence (AI) techniques have shown successful performance in credit scoring. Support Vector Machines and metaheuristic approaches have constantly received attention from researchers in establishing new credit models. In this paper, two AI techniques are reviewed with detailed discussions on credit scoring models built from both methods since 1997 to 2018. Te main discussions are based on two main aspects which are model type with issues addressed and assessment procedures. Ten, together with the compilation of past experiments results on common datasets, hybrid modelling is the state-of-the-art approach for both methods. Some possible research gaps for future research are identifed. Hindawi 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81046/1/SCORING.pdf Goh, Rui Ying and Lee, Lai Soon (2019) Credit scoring: a review on support vector machines and metaheuristic approaches. Advances in Operations Research, 2019. art. no. 1974794. pp. 1-30. ISSN 1687-9147; ESSN: 1687-9155 https://www.hindawi.com/journals/aor/2019/1974794/ 10.1155/2019/1974794
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Development of credit scoring models is important for fnancial institutions to identify defaulters and nondefaulters when making credit granting decisions. In recent years, artifcial intelligence (AI) techniques have shown successful performance in credit scoring. Support Vector Machines and metaheuristic approaches have constantly received attention from researchers in establishing new credit models. In this paper, two AI techniques are reviewed with detailed discussions on credit scoring models built from both methods since 1997 to 2018. Te main discussions are based on two main aspects which are model type with issues addressed and assessment procedures. Ten, together with the compilation of past experiments results on common datasets, hybrid modelling is the state-of-the-art approach for both methods. Some possible research gaps for future research are identifed.
format Article
author Goh, Rui Ying
Lee, Lai Soon
spellingShingle Goh, Rui Ying
Lee, Lai Soon
Credit scoring: a review on support vector machines and metaheuristic approaches
author_facet Goh, Rui Ying
Lee, Lai Soon
author_sort Goh, Rui Ying
title Credit scoring: a review on support vector machines and metaheuristic approaches
title_short Credit scoring: a review on support vector machines and metaheuristic approaches
title_full Credit scoring: a review on support vector machines and metaheuristic approaches
title_fullStr Credit scoring: a review on support vector machines and metaheuristic approaches
title_full_unstemmed Credit scoring: a review on support vector machines and metaheuristic approaches
title_sort credit scoring: a review on support vector machines and metaheuristic approaches
publisher Hindawi
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/81046/1/SCORING.pdf
http://psasir.upm.edu.my/id/eprint/81046/
https://www.hindawi.com/journals/aor/2019/1974794/
_version_ 1681490843249147904
score 13.211869

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