Information about financial risk almost always contains a problem of class imbalance. Class-imbalanced data refers to the asymmetric categories of data, and it is divided into a major class and a minor class. If we guide all information into the training sample to model of this situation, it may happen that the accuracy rate of the major class is high, but the accuracy rate of the minor class is too low. Many risk assessment models have been developed in many studies, but most of them only use sampling methods to deal with the class-imbalanced data; this may cause the distortion of information. In order to effectively solve the problem of class imbalance in credit risk assessment, this paper proposed a novel credit risk assessment model using a granular computing technique to construct a risk assessment model to provide a better insight into the essence of data and effectively solve class imbalance problems. On the other hand, in order to improve the lack of granular computing and enhance the efficiency of the credit risk assessment model, this paper adds a new index, "% of minor class (PM)," to avoid a situation in which minor class data spread to the major class granular. Finally, this paper also compares the results of the area under the receiver operating characteristic curve (AUC) and G-means methods for dealing with classimbalanced data. The results demonstrate that the proposed granular computing credit assessment model would have better results than other sampling models.