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Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches
Joint Authors
Source
Advances in Operations Research
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-30, 30 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-13
Country of Publication
Egypt
No. of Pages
30
Main Subjects
Information Technology and Computer Science
Abstract EN
Development of credit scoring models is important for financial institutions to identify defaulters and nondefaulters when making credit granting decisions.
In recent years, artificial 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.
The main discussions are based on two main aspects which are model type with issues addressed and assessment procedures.
Then, 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 identified.
American Psychological Association (APA)
Goh, R. Y.& Lee, Lai Soon. 2019. Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches. Advances in Operations Research،Vol. 2019, no. 2019, pp.1-30.
https://search.emarefa.net/detail/BIM-1121528
Modern Language Association (MLA)
Goh, R. Y.& Lee, Lai Soon. Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches. Advances in Operations Research No. 2019 (2019), pp.1-30.
https://search.emarefa.net/detail/BIM-1121528
American Medical Association (AMA)
Goh, R. Y.& Lee, Lai Soon. Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches. Advances in Operations Research. 2019. Vol. 2019, no. 2019, pp.1-30.
https://search.emarefa.net/detail/BIM-1121528
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1121528