Multi-classification assessment of bank personal credit risk based on multi-source information fusion. (1st April 2022)

• Record Type: Journal Article
• Authors: Wang, Tianhui; Liu, Renjing; Qi, Guohua
• Is Part Of: Expert systems with applications. Volume 191(2022)
• Publication Date: 2022-04-01
• Subjects: Personal credit risk -- Multi-classification assessment -- Information fusion -- D-S evidence theory; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116236
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new multi-classification assessment model of bank personal credit is proposed. The D-S evidence theory is used to integrate six different base classifier advantage. The new model has higher accuracy and stronger universality than other classifiers. Abstract: There have been many studies on machine learning and data mining algorithms to improve the effect of credit risk assessment. However, there are few methods that can meet its universal and efficient characteristics. This paper proposes a new multi-classification assessment model of personal credit risk based on the theory of information fusion (MIFCA) by using six machine learning algorithms. The MIFCA model can simultaneously integrate the advantages of multiple classifiers and reduce the interference of uncertain information. In order to verify the MIFCA model, dataset collected from a real data set of commercial bank in China. Experimental results show that MIFCA model has two outstanding points in various assessment criteria. One is that it has higher accuracy for multi-classification assessment, and the other is that it is suitable for various risk assessments and has universal applicability. In addition, the results of this research can also provide references for banks and other financial institutions to strengthen their risk prevention and control capabilities, improve their credit risk identification capabilities, and avoid financial losses.