Application and Comparison of Classification Techniques in Controlling Credit Risk
5. Conclusions and Future Work
Controlling credit risk is crucial for commercial banks to identify the clients that will probably breach their contracts in the future. Although the credit rating system provides an effective tool, it is not possible to rate all the clients and repeat the rating frequently. Data mining and computational intelligence, especially classification techniques, can be applied to learn and predict the credit rating automatically, thus helping
commercial banks detect the potential high-risk clients in an accurate and timely manner.
A comprehensive examination of several well-known classifiers is described in this chapter. All these classifiers have been applied to 244 rated companies mainly from the Industrial and Commercial Bank of China. The results revealed that traditional statistical models had the poorest outcomes, and that C4.5 and SVM did not achieve a satisfactory performance as expected. On the other hand, CBA, an associative classification technique, seemed to be the most appropriate choice.
Future work may focus on collecting more data for experiments and applications, particularly with more exploration of Chinese credit rating data structures. In this chapter, feature selection/transformation methods such as ANOVA or PCA analysis are found independent of these classification methods and did not lead to improvements of their prediction abilities. An investigation in the future might be to apply another type of feature selection methods, which are dependent on the classification algorithms, in order to find out the best feature combination for each classifier.
Acknowledgements
The work was partly supported by the National Natural Science Foundation of China (79925001/70231010/70321001), and the MOE Funds for Doctoral Programs (20020003095).
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Authors’ Biographical Statements
Lan Yu graduated from the School of Economics and Management, Tsinghua University (Beijing, China), in 2006 with a doctoral degree in management. In recent years he has been doing research on data mining, focusing on the improvement and application of classification techniques.
His research publications have appeared in several international journals including Decision Support Systems, and Expert Systems with Applications. Dr. Yu is currently working as a post-doctoral researcher at Tsinghua University’s Department of Computer Science and Technology, in expanding the business intelligence knowledge to the banks in China.
Guoqing Chen received his PhD from the Catholic University of Leuven (K.U. Leuven, Belgium), and now is the EMC2 Chair Professor of information systems at the School of Economics and Management, Tsinghua University (Beijing, China). His research interests include information systems management, business intelligence and decision support, and soft computing.
He has published internationally and served as area editor/associate editor/editorial board member for international journals such as Information Sciences, Information Processing & Management, Journal of Strategic Information Systems, Information & Management, Fuzzy Sets and Systems, etc. Prof. Chen is the founding president of Association for Information Systems (AIS) China Chapter (CNAIS), and served as chair/co-chair for several international conferences including IFSA2005 World Congress, IEEE ICEBE2005, IESM2007, etc.
Andy Koronios earned his PhD from the University of Queensland (Brisbane, Australia), and now is a professor of information systems at the School of Computer & Information Science, University of South Australia (Adelaide, Australia).
His research interests include electronic commerce, data quality and security, multimedia and online learning systems. He has a major role in the CRC for Integrated Engineering Asset Management (CIEAMP) as a research program leader in the area of systems integration and IT for
assets management. Professor Koronios has numerous publications in international journals, edited volumes and conference proceedings.
Shiwu Zhu received his PhD from the Shanghai University of Finance and Economics (Shanghai, China), and currently is an associate professor of finance at the School of Economics and Management, Tsinghua University (Beijing, China). His research interests include fixed income, risk management, credit derivative pricing, and financial database. Dr.
Zhu has been the Principal Investigator for a number of research grants including the research grant awarded by the National Natural Science Foundation of China (NSFC).
Xunhua Guo received his doctoral degree from Tsinghua University (Beijing, China) in 2005, and currently he is an assistant professor of information systems at the School of Economics and Management, Tsinghua University. His research interests include information systems and organizational evolution, systems analysis and design, and data management. His academic publications have appeared in international journals such as Communications of the ACM, Information Sciences, Journal of Enterprise Information Systems etc. He has co-authored books on information systems management, and co-developed a case recently on Digital China published by Harvard Business School in 2007. Dr. Guo has served as a Co-Chair for the International Conference on Industrial Engineering and Systems Management (IESM2007).
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