Economics at your fingertips  

Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

R. Y. Goh () and L. S. Lee ()

Advances in Operations Research, 2019, vol. 2019, 1-30

Abstract: 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.

Date: 2019
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf) (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1155/2019/1974794

Access Statistics for this article

More articles in Advances in Operations Research from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().

Page updated 2019-12-30
Handle: RePEc:hin:jnlaor:1974794