LS-SVM for bad debt risk assessment in enterprises

With the development of market economy in China, the problem of bad debt becomes increasingly serious in enterprises. In this paper, a bad-debt-risk evaluation model is established based on LS-SVM classifier, using a new set of index system which combines financial factors with non-financial factors on the basis of the 5C system evaluation method. The bad debt rating is separated into four classes- normality, attention, doubt and loss through analyzing accounts payable. Then the LS-SVM classifier is trained with 220 samples which are stochastically extracted from listed companies of China in industry, and the four classes are identified by the trained classifier using 80 samples. Then, BP neural network is also used to assess the same data. The experiment results show that LS-SVM has an excellent performance on training accuracy and reliability in credit risk assessment and achieves better performance than BP neural network.

[1]  Zhang Qiang,et al.  Enterprise Credit Rating Model Based on Neural Networks , 2004 .

[2]  Kai Zhang,et al.  Support vector machine networks for multi-class classification , 2005, Int. J. Pattern Recognit. Artif. Intell..

[3]  Yann-Chang Huang,et al.  Evolving neural nets for fault diagnosis of power transformers , 2003 .

[4]  Chen-Guang Yang,et al.  Credit risk assessment in commercial banks based on SVM using PCA , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[5]  Jezekiel Ben-Arie,et al.  Minimal classification method with error-correcting codes for multiclass recognition , 2005, Int. J. Pattern Recognit. Artif. Intell..

[6]  M.-H. Wang.  Extension neural network for power transformer incipient fault diagnosis , 2003 .

[7]  Cheng Haozhong,et al.  Fault diagnosis of power transformer based on multi-layer SVM classifier , 2005 .

[8]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[9]  Daewon Lee,et al.  An improved cluster labeling method for support vector clustering , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Qi Hengnian,et al.  Support Vector Machines and Application Research Overview , 2004 .

[11]  Chris J. Harris,et al.  On the modelling of nonlinear dynamic systems using support vector neural networks , 2001 .

[12]  David J. C. MacKay,et al.  The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.

[13]  Carl Gold,et al.  Model selection for support vector machine classification , 2002, Neurocomputing.

[14]  Chen Xiaoou.  A Strategy of Multi-level Classification Based on SVM , 2005 .

[15]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[16]  Y. C. Huang.  A New Data Mining Approach to Dissolved Gas Analysis of Oil-Insulated Power Apparatus , 2002, IEEE Power Engineering Review.

[17]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[18]  Liu Yanhui,et al.  Enterprise Credit Rating Model Based on Fuzzy Clustering and Decision Tree , 2010, 2010 Third International Symposium on Information Science and Engineering.

[19]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[20]  M. H. Wang.  A Novel Extension Method for Transformer Fault Diagnosis , 2002, IEEE Power Engineering Review.

[21]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[22]  Nelson G. Durdle,et al.  A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography , 2006, IEEE Transactions on Information Technology in Biomedicine.

[23]  Johan A. K. Suykens,et al.  Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis , 2002, Neural Computation.