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

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  • R. Y. Goh
  • L. S. Lee

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.

Suggested Citation

  • R. Y. Goh & L. S. Lee, 2019. "Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches," Advances in Operations Research, Hindawi, vol. 2019, pages 1-30, March.
    • Handle: RePEc:hin:jnlaor:1974794
    DOI: 10.1155/2019/1974794
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    Cited by:

    1. Karim Amzile & Mohamed Habachi, 2022. "Assessment of Support Vector Machine performance for default prediction and credit rating," Post-Print halshs-03643738, HAL.
    2. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.

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