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| Jongsik Yoon, Young S. Kwon, Tae Hyup Roh, "Performance Improvement of Bankruptcy Prediction using Credit Card Sales Information of Small & Micro Business," Software Engineering Research, Management and Applications, ACIS International Conference on, pp. 503-512, 5th ACIS International Conference on Software Engineering Research, Management & Applications (SERA 2007), 2007. | |||
| BibTex | x | ||
| @article{ 10.1109/SERA.2007.105, author = {Jongsik Yoon and Young S. Kwon and Tae Hyup Roh}, title = {Performance Improvement of Bankruptcy Prediction using Credit Card Sales Information of Small & Micro Business}, journal ={Software Engineering Research, Management and Applications, ACIS International Conference on}, volume = {0}, year = {2007}, isbn = {0-7695-2867-8}, pages = {503-512}, doi = {http://doi.ieeecomputersociety.org/10.1109/SERA.2007.105}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Software Engineering Research, Management and Applications, ACIS International Conference on TI - Performance Improvement of Bankruptcy Prediction using Credit Card Sales Information of Small & Micro Business SN - 0-7695-2867-8 SP503 EP512 A1 - Jongsik Yoon, A1 - Young S. Kwon, A1 - Tae Hyup Roh, PY - 2007 KW - null VL - 0 JA - Software Engineering Research, Management and Applications, ACIS International Conference on ER - | |||
In order to develop the model, we derive some variables and analyze the relationship between good and bad credits. We find out that twelve variables are significant in predicting good or bad risk for small and micro business, which are categorized into the business period, scale for sale, a fluctuation in sales, sales pattern and business category's bankruptcy ratio, etc.
We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data on business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0, multivariate discriminant analysis (MDA), and logistic regression analysis(LRA).