A deep learning approach for credit scoring using credit default swaps

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.