To read this content please select one of the options below:

A comprehensive decision support approach for credit scoring

Cuicui Luo (International School, University of Chinese Academy of Sciences, Beijing, China) (Stockholm Business School, Stockholm University, Stockholm, Sweden)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 14 October 2019

Issue publication date: 22 January 2020

589

Abstract

Purpose

The purpose of this paper is to provide a comprehensive decision support approach in credit risk assessment.

Design/methodology/approach

A comprehensive decision support approach is proposed for credit scoring and prediction. The predictive performance of the new approach has been investigated by using data including number and text.

Findings

The results demonstrate that the proposed approach achieves better and more stable classification accuracy than the single classifiers in most cases. Meanwhile, the prediction accuracy of individual classifiers is also improved by the proposed approach.

Originality/value

This study provides a comprehensive model for credit risk scoring and provides valuable information to the existing literature on credit scoring by using artificial intelligence.

Keywords

Acknowledgements

This study was supported by the National Natural Science Foundation of China under Grant Nos 71825007 and Y91Z0141A9, in part by the Chinese Academy of Sciences Frontier Scientific Research Key Project under Grant No. QYZDB-SSW-SYS021, in part by the CAS Strategic Research and Decision Support System Development under Grant No. GHJ-ZLZX-2019-33-3, in part by the Marianne and Marcus Wallenberg Foundation under Grant No. MMW 2015.0007, in part by the Strategic Priority Research Program of CAS under Grant No. XDA23020203 and supported by the International Partnership Program of Chinese Academy of Sciences, Grant No. 211211KYSB20180042.

Citation

Luo, C. (2020), "A comprehensive decision support approach for credit scoring", Industrial Management & Data Systems, Vol. 120 No. 2, pp. 280-290. https://doi.org/10.1108/IMDS-03-2019-0182

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

Related articles