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


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


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Institute for Mathematical Research
DOI Number: https://doi.org/10.1155/2019/1974794
Publisher: Hindawi
Keywords: Credit scoring; Internal Rating Based (IRB); Support Vector Machines (SVM)
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 14 Oct 2020 21:01
Last Modified: 14 Oct 2020 21:01
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1155/2019/1974794
URI: http://psasir.upm.edu.my/id/eprint/81046
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