首頁 > 網路資源 > 大同大學數位論文系統

Title page for etd-0716104-090643


URN etd-0716104-090643 Statistics This thesis had been viewed 1764 times. Download 9 times.
Author Kuo-Shun Lin
Author's Email Address No Public.
Department Management Business Administration
Year 2003 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 116
Title A WARNING MODEL FOR PREDICTING OVERDUE REPAYMENT OF LOAN
Keyword
  • risk evaluation
  • overdue repayment
  • logistic regression
  • discriminant analysis
  • discriminant analysis
  • logistic regression
  • overdue repayment
  • risk evaluation
  • Abstract The high rate of overdue loans is always an issue that financial organizations want to improve. The solutions to this problem are: thorough examination of an applicant’s qualifications before loan approval, careful appraisal of collateral, identification of factors which influence delinquent payment by a debtor, and exclusion of potential high-risk clients. “Prevention is better than cure.” Early preparation is indeed indispensable.
    Since regulations were relaxed, allowing new banks to launch in Taiwan, the number of banks here has increased sharply. Thus, an era of competition in the Financial industry was ushered in. Due to cutthroat rivalry, profit margins of all banks started to narrow. Moreover, owing to too many competitors, and a sluggish economy in real estate trading, risks of home mortgages are spiraling in financial institutions. Consequently, the quality of credit is lowered, and the rate of overdue repayment is skyrocketing, too.
      In addition, liberalization of the stock market and foreign exchange broaden the access to capital for enterprises. Banks, therefore, are not the only source of acquiring money any more. The loan business adapts from a sellers’ market to a buyers’ market. To appeal to clients, it is often the case that interest rates are cut, and even standards of loan approval are reduced. This spurs lax rules of loan approval, over-expansion of credit and rising percentages of over-borrowing and bad debt.
      
    With a view to gain efficiency, minimize man-made errors and shorten examination time, the time has come to utilize an automatic evaluation system.
      
    The aims of this research are to investigate factors affecting the credit risk of applicants, and establish an assessment system for mortgages. It is hoped that financial institutes can use this system 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 LR model and discriminant analysis model to compare and diagnose the data. It is found that factors having an impact on risks of loan applicants, such as occupation, educational level, annual income and the ratio of loan approval, are all significant statistics.
      
    The outcome reveals that the accuracy rate of LR model is 71.5%, compared to 71.7% 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.
    Advisor Committee
  • Yu-Chung Hung - advisor
  • Kuo-ping Lin - co-chair
  • Ming-chuan Pan - co-chair
  • Files indicate in-campus access only
    Date of Defense 2004-06-23 Date of Submission 2004-07-16


    Browse | Search All Available ETDs