A practical approach to bankruptcy prediction for small businesses: Substituting the unavailable financial data for credit card sales information

Small businesses are open to the elements of both consumer and business credit risks. One of the problems in bankruptcy prediction for small businesses is that the official financial data in most cases are not available for evaluating the business credit risks. In order to alleviate this problem, we propose to use the credit card sales information as a substitute for the official financial data in developing a bankruptcy prediction model. In most cases, the credit card sales information is available because most small businesses are member stores of credit card processors. First, we derived several variables using the credit card sales information, including business period, sales scale, sales fluctuation, sales pattern and business category's bankruptcy ratio, etc. Then we developed support vector machines (SVM) based model. The empirical analyses show that credit card sales information is an acceptable substitute for the financial data in predicting the bankruptcy of small businesses. In addition, the proposed SVM model exhibits superior performance compared to other classifiers such as neural networks, CART, C5.0, multivariate discriminant analysis (MDA), and logistic regression analysis (LRA).

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