Applying Classification and Regression Trees Model to the Evaluation of Credit Risk of Virtual Store in Retailing Industry in Taiwan
Date Issued
2008
Date
2008
Author(s)
Tsai, Chen-Yu
Abstract
TV shopping companies using live broadcast are growing rapidly in Taiwan recently. Plus the sales channels of mail order and web shopping, the market value of the virtual store in retail industry is continuously escalating, and the market has arrived the mature stage. In order to raise the sales, all virtual-store companies in the retail industry provide the service of installment payment by credit card to reduce customers’ burden each month. It also lowers down the threshold of the product purchase and inspires consumers’ purchase willing. Moreover, under the marketing by the credit card issue banks, the number of credit cards outstanding is soaring high year by year. The consumers now can expand their credit easily by using the credit cards. Therefore, under the promotion by both banks and firms, consumers’ abilities of installment payment are strongly overvalue. One can use his credit freely without any limitation. As a result, in the end of 2005, there was a card debt crisis which shocked the nation. The crisis not only caused consumers to have heavy debt burden, but also struck card issue banks and many industries, which leaded firms to the serious loss of the overdue payment. In order to prevent firms from having such huge loss again, a well-designed credit risk evaluation model is extremely necessary. However, the credit risk evaluation model which firms use now is simple. Most of them only use the experience from their employees to control the credit risk, which can cause bias easily. In this research, a more objective and precise statistics model- classification and regression tree (CART) - is used for the construction of the credit risk evaluation model. It also uses consumers’ data from the database of the Eastern Home Shopping and Leisure Company as the source for the verification of the model we construct. It’s expected that under the consideration of various variables, the credit risk evaluation model with higher precision could be established to help firms lower the loss of the overdue payment.
Subjects
credit risk
classification and regression tree model
CART
Type
thesis
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