Credit risk models for credit rating transitions are important ingredients for credit risk management. This paper propose a formal model, conditional Markov chain model, for gauging credit risk and apply it to Taiwan's bank loans from 1998-2003. The model's central feature is to incorporate credit cycle and time-varying risk premium into transition matrices. There are three main contributions. First, we apply the methodology to bank loans in Taiwan, which is more elaborate than previous studies. Second, the empirical results show that credit cycle and risk premium have significant influence on default probabilities. That is, if we ignore credit cycle and risk premium, default probabilities may be underestimated. Third, the estimation procedures for assessing the credit risk of financial institutions are easy to follow and implement. On the whole, the proposed model can provide more reliable estimated results for credit risk of bank loans.