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A deep learning approach for credit scoring using credit default swaps
Stockholm University, Faculty of Social Sciences, Stockholm Business School.
Stockholm University, Faculty of Social Sciences, Stockholm Business School.
Stockholm University, Faculty of Social Sciences, Stockholm Business School.
Number of Authors: 3
2017 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 65, 465-470 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 65, 465-470 p.
Keyword [en]
Deep learning, CDS, Credit scoring, Machine learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer and Information Sciences Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:su:diva-148852DOI: 10.1016/j.engappai.2016.12.002ISI: 000413388100039OAI: oai:DiVA.org:su-148852DiVA: diva2:1159664
Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2017-11-23Bibliographically approved

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Luo, CuicuiWu, DeshengWu, Dexiang
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Stockholm Business School
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