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Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/473

Title: 二手車買賣貸款逾期還款預警模式之研究
Warning model for predicting overdue repayment of the loan in purchasing second-hand car
Authors: 詹益山
Yih-San Jan
Contributors: 陳永璋
Yung-Chung Chen
經營管理研究所
Keywords: 羅吉斯迴歸;區別分析;二手車
Logistic regression;Overdue repayment;Second-hand car
Date: 2007
Issue Date: 2011-05-23 13:40:21 (UTC+8)
Publisher: 高雄市:[樹德科技大學經營管理研究所]
Abstract: 隨著緊湊忙碌的生活及所得水準不斷提高,汽車成為多數人們不可或缺的生活必備交通工具。惟因價值觀及所得、消費能力不相同,部份消費者選擇了相對於新車較為便宜的二手汽車成為代步器具。依據交通部監理機關統計,二手車過戶數已於1995超越新車領牌數,由此可見二手車買賣台數超越新車供應市場,已成為汽車交易市場中一個很重要的另一供給者,意即大多數汽車消費者已經選擇二手汽車當做交通工具。在這重要的交易市場裡,因為適當的分期商品可以協助購買者提高購買能力,在二手車市場裡扮演是否成交的重要因素之一,故二手車買賣貸款經營之良窳間接影響到二手車市場的消長。
以金融放款業者而言,降低逾期放款比率一直是積極追求的目標,如果能事前確實審核貸款申請人之資格、嚴格執行放款條件、對抵押品公平鑑價,欲控制逾期狀況改進呆帳比率定會有莫大幫助。所以是否能夠預先篩選出高風險的客戶並防範於未然,對二手車買賣貸款經營規模、間接地協助二手車市場正常發展等而言,將會是很重要的工作。
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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