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The development process of small and medium enterprises can be an epitome of Taiwan experience, and they play an extremely important role on the economic development of Taiwan. In the course of industrial development, enterprises often meet difficulties on the obtainment of funds owing to the unwholesome financial structure, and the government spares no efforts to assist small and medium enterprises to take root and acquire the fund accommodation smoothly. At the same time of cooperating with the government’s policy to offer small and medium enterprises financing, the banks frequently misjudge when doing the credit rating because of insufficient transparency of financial statement, resulting in the occurrence of overdue loans, and a bank’s bad debt. The bank should base on the principles of safety, fluidity, public welfare, revenue, and growth to mange the loan granting so as to achieve the goals of assisting the development of small and medium enterprises and creating the bank’s revenue simultaneously. The research hoped to find out the striking factors that are possible to cause overdue loans through the non-financial statement factors, and established a set of warning mode to provide reference for accreditors. The research took the small and medium enterprises that had been approved for a loan by the case bank from January 2003 to February 2008 as the research subjects, drew 200 normal enterprises and 69 overdue enterprises at random, referred to small and medium enterprises’ default factors on the loan under the bank’s internal audit, the possible factors outside of the financial statement, and the data of JCIC, and applied Logistic regression analysis to explore the significant factors that affected the overdue loans. The empirical results show that the four variables: the number of banks that an enterprise has liabilities with, whether the enterprise is a new applicant, whether the person-in-charge uses the circulation interest of credit card, and whether the net value is higher than the capital have a remarkable effect on whether there will be overdue loans. The accuracy of the overall differentiation achieved 84.8%, and via Press Q, the research’s discriminant analysis was tested and verified to be valid.
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