Abstract
Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The performance assessment exercise under a set of criteria remains understudied in nature, on the one hand, and the real–scenario is not taken into account in that a single/very limited number of measure only are used, on the other hand. These problems affect the ability to make a consistent conclusion. Therefore, the aim of this study is to address this methodological issue by applying support vector machine (SVM)-based CDP algorithm by means of a set of representative performance criterions, with enclosing some novel performance measures, its performance compare with the results gained by statistical and intelligent approaches using six different types of databases from the credit prediction domains. Experimental results show that SVM model is marginally superior to CART with DA, being more robust than its other counterparts. In consequence, this study recommends that the supremacy of a classifier is linked to the way in which evaluations are measured.
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Acknowledgments
We are very grateful to the editor-in-chief, Igor Loncarski; managing editors; and anonymous reviewers for their substantial contribution because with their assistance the manuscript has been significantly improved. The research is supported by the National Natural Science Foundation of China (71171031 and 71471027), National Social Science Foundation of China (16BTJ017), Social Science Foundation of Liaoning province of China (L16BJY016), Credit Risks Rating System and Loan Pricing Project of Small Enterprises for Bank of Dalian (2012–2001) and Credit Risks Evaluation and Loan Pricing For Petty Loan Funded for the Head Office of Postal Savings Bank of China (2009–2007). We thank the organizations mentioned above.
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Fahmida E. Moula, Chi Guotai and Mohammad Zoynul Abedin have contributed equally to this work.
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Moula, F.E., Guotai, C. & Abedin, M.Z. Credit default prediction modeling: an application of support vector machine. Risk Manag 19, 158–187 (2017). https://doi.org/10.1057/s41283-017-0016-x
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DOI: https://doi.org/10.1057/s41283-017-0016-x