A deep learning approach for credit scoring using credit default swaps

After 20072008 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.

[1]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[4]  Chris Stewart,et al.  A note comparing support vector machines and ordered choice models' predictions of international banks' ratings , 2011, Decis. Support Syst..

[5]  Geoffrey E. Hinton,et al.  Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Ralf Stecking,et al.  Support vector machines for classifying and describing credit applicants: detecting typical and critical regions , 2005, J. Oper. Res. Soc..

[7]  Ayman M. Eldeib,et al.  Breast cancer classification using deep belief networks , 2016, Expert Syst. Appl..

[8]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

[9]  Sheng-Tun Li,et al.  The evaluation of consumer loans using support vector machines , 2006, Expert Syst. Appl..

[10]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[11]  J. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research , 2015, Eur. J. Oper. Res..

[12]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[13]  Chunyan Miao,et al.  Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings , 2014, Neurocomputing.

[14]  Siddhartha Bhattacharyya,et al.  Data mining for credit card fraud: A comparative study , 2011, Decis. Support Syst..

[15]  Jonathan Crook,et al.  Support vector machines for credit scoring and discovery of significant features , 2009, Expert Syst. Appl..

[16]  Noel Lopes,et al.  Deep Belief Networks for Financial Prediction , 2011, ICONIP.

[17]  Lu Han,et al.  Orthogonal support vector machine for credit scoring , 2013, Eng. Appl. Artif. Intell..

[18]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[19]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[20]  Dong Yu,et al.  Modeling Spectral Envelopes Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[21]  Emily Mower Provost,et al.  Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[22]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[23]  Johan A. K. Suykens,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Kernel-based Classification for Financial Distress Detection Dirk Van Den Poel 4 Bayesian Kernel Based Classification for Financial Distress Detection , 2022 .

[24]  Ali Azadeh,et al.  A granular computing-based approach to credit scoring modeling , 2013, Neurocomputing.

[25]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[26]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[27]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.