Due to the global economic recession, enterprises are facing strong financial stress. For this reason, banks or financial institutions are suffered from serious financial risk. In order to reduce the global financial risk, banks or financial institutions need to develop to their own internal measures for assessing the borrower’s credit according to the New Basel Capital Accord (BASEL II). Most risk assessment models found in literature were constructed for the publicly traded companies. However, 90% of enterprises in Taiwan are small and medium enterprises. It is not quite appropriate to apply the risk assessment models for publicly traded companies directly to those banks or financial institutions whose borrowers are mainly small and medium enterprises. Furthermore, most available risk assessment models use classification methods (such as the discriminate analysis model and logistic regression model) to construct the models and classify the loan borrowers into default and non-default groups. Although the total accuracy rate of classification may be good, but the accuracy rate for a particular group (such as the default group) is significantly higher than the other. It is often found that the accuracy rates for both groups are not balanced. It causes serious problem in practice use for the financial institutions. Therefore, the objective of this study is to develop a two-stage risk assessment model to improve the unbalanced accuracy rate for different group, furthermore, to increase the total accuracy rate. This study utilizes logistic regression at the first stage and support vector machine (SVM) at the second stage to construct this two-stage risk assessment model. Finally, a real case from a Taiwanese financial institution is utilized to demonstrate the effectiveness of the proposed procedure.