Study of corporate credit risk prediction based on integrating boosting and random subspace

Abstract With the rapid growth and increased competition in credit industry, the corporate credit risk prediction is becoming more important for credit-granting institutions. In this paper, we propose an integrated ensemble approach, called RS-Boosting, which is based on two popular ensemble strategies, i.e., boosting and random subspace, for corporate credit risk prediction. As there are two different factors encouraging diversity in RS-Boosting, it would be advantageous to get better performance. Two corporate credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed method. Experimental results reveal that RS-Boosting gets the best performance among seven methods, i.e., logistic regression analysis (LRA), decision tree (DT), artificial neural network (ANN), bagging, boosting and random subspace. All these results illustrate that RS-Boosting can be used as an alternative method for corporate credit risk prediction.

[1]  Chih-Fong Tsai,et al.  Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..

[2]  Ingoo Han,et al.  A case-based approach using inductive indexing for corporate bond rating , 2001, Decis. Support Syst..

[3]  Gordon V. Karels,et al.  Multivariate Normality and Forecasting of Business Bankruptcy , 1987 .

[4]  Vijay S. Desai,et al.  A comparison of neural networks and linear scoring models in the credit union environment , 1996 .

[5]  Yi Jiang.  Credit Scoring Model Based on the Decision Tree and the Simulated Annealing Algorithm , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[6]  Chihli Hung,et al.  A selective ensemble based on expected probabilities for bankruptcy prediction , 2009, Expert Syst. Appl..

[7]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[8]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

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

[10]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[11]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[12]  D. Durand.  Risk elements in consumer instalment financing , 1942 .

[13]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[14]  David West,et al.  Neural network ensemble strategies for financial decision applications , 2005, Comput. Oper. Res..

[15]  Kin Keung Lai,et al.  Credit risk assessment with a multistage neural network ensemble learning approach , 2008, Expert Syst. Appl..

[16]  Thomas G. Dietterich.  Machine-Learning Research Four Current Directions , 1997 .

[17]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  L. Thomas.  A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .

[19]  Loris Nanni,et al.  An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring , 2009, Expert Syst. Appl..

[20]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[21]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[22]  S. Raghavan,et al.  Diversification for better classification trees , 2006, Comput. Oper. Res..

[23]  Alan K. Reichert,et al.  An Examination of the Conceptual Issues Involved in Developing Credit-Scoring Models , 1983 .

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

[25]  Ling Liu,et al.  Encyclopedia of Database Systems , 2009, Encyclopedia of Database Systems.

[26]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

[27]  Evelien de Bruijn,et al.  Ensemble , 1985, The Fairchild Books Dictionary of Fashion.

[28]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[29]  David J. Hand,et al.  Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner by Galit Shmueli, Nitin R. Patel, Peter C. Bruce , 2007 .