Redesigning rating systems is becoming an important issue for banks and other financial institutions in processing the implementation of the Basel II and III (www.bis.org/bcbs/basel3.htm) accords. The available database for this task typically contains only the accepted credit applicants and is thus censored. To evaluate existing and alternative rating systems, we would actually need the full database of all past credit applicants. In this paper, we discuss how to assess the performance of credit ratings under the assumption that, for credit data, only some of the defaults and nondefaults are observed. The paper investigates criteria based on the difference of the score distributions under default and nondefault, such as the Kolmogorov-Smirnov statistic, the accuracy ratio and the area under the curve. We show how to estimate bounds for these criteria under typical circumstances, ie, with the bank only storing the data of accepted credit applications. We show that these bounds can be helpful to assess discriminatory power even when some of the data is not available.