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Warning model for predicting overdue repayment of the loan in purchasing second-hand car
Logistic regression;Overdue repayment;Second-hand car
|Issue Date: ||2011-05-23 13:40:21 (UTC+8)|
有鑒於此，本研究將運用利用區別分析（Discriminant Analysis）及羅吉斯迴歸（ Logistic Regression ）等方法，找出影響之因素並建立預警模式，為二手車買賣貸款建立授信風險預警模式，提供判斷逾期發生的風險指標，並希望藉由此模型之建立及輔助，能夠加強貸款客戶之風險管理。
Volume of the second-hand car business in Taiwan is lot more than the volume of the new car since 1995. Part of the consumers, which from middle class and above mainly buy new cars. They pay more attention to the reliability of the vehicles instead of the price. But some consumers from below the middle class then take the second-hand car as the main consideration. It is because of the lower price consideration.
The high rate of overdue loans is always an issue that financial organizations want to improve. With the increase of consumer’s credit extension and personalized financial management, repayment has become a strategy of car promotion. In order to minimize risks so as to cut down dues and earn more profit, it becomes more important for banks to develop and apply loan credit rating model.
The aims of this research are to investigate factors affecting the credit risk of applicants, and establish an assessment system for the credits. It is hoped that this system can help the user to quickly and objectively detect the risk status of loan candidates. Furthermore, the system can be taken as a basis of loan approval. Rates of overdue repayment will be brought down, operational performance enhanced, and profit will increase.
This study adopts logistic regression model and discriminant analysis model to compare and diagnose the data. The outcome reveals that the accuracy rate of logistic regression model is 70%~80%, compared to 57%~60% of discriminant analysis model. While the figures are very close, the latter is higher than the former. However, both models can be employed as criteria in loan examination.
|Appears in Collections:||[經營管理研究所] 博碩士論文|
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