Performance improvement of bankruptcy prediction using credit card sales information of small & micro business
- J. Yoon, Y. S. Kwon and T. H. Roh (2007). SERA 2007: Fifth ACIS International Conference on Software Engineering Research, Management, and Applications, 503-510
- Abstract: Due to the poor financial statements which represent credit risk of small and micro business, it's been difficult to develop the credit evaluation model that reflects both consumer credit risk and business credit risk of small and micro business. The purpose of this study is to develop the credit evaluation model for small and micro businesses using credit card sales information in lieu of poor financial information. 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 ana lysis(LRA). 2007 IEEE.
- Theme: Finance
- Keywords: Bankruptcy prediction, Credit cards, Financial information, Micro businesses, Performance improvement, Forecasting, Information use, Neural networks, Risk analysis, Statistical mechanics, Support vector machines, Financial data processing
- Reference type: Conference Proceedings
- Geographic location: International