A deep learning approach for credit scoring using credit default swaps. (October 2017)

• Record Type: Journal Article
• Authors: Luo, Cuicui; Wu, Desheng; Wu, Dexiang
• Is Part Of: Engineering applications of artificial intelligence. Volume 65(2017:May)
• Page Start: 465
• Page End: 470
• Publication Date: 2017-10
• Subjects: Deep learning -- CDS -- Credit scoring -- Machine learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.12.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: After 2007–2008 crisis, it is clear that corporate credit scoring is becoming a key role in credit risk management. In this paper, we investigate the performances of credit scoring models applied to CDS data sets. The classification performance of deep learning algorithm such as deep belief networks with Restricted Boltzmann Machines are evaluated and compared with some popular credit scoring models such as logistic regression, multi-layer perceptron and support vector machine. The performance is assessed using the classification accuracy and the area under the receiver operating characteristic curve. It is found that DBN yields the best performance.