This paper studies the actuarial credibility theory when the information about the loss model or the prior distribution of its parameters is imprecise or vague. This problem has been studied by many authors. For example, Gómez-Déniz (2009) assumes that the parameters of the prior distribution belong to some interval and proposes to calculate credibility premium based on the posterior regret Γ-minimax principle. Hong & Martin (2021) derive interval estimators for Bühlmann credibility premium when only partial information about the loss distribution and prior distribution are available. In this paper, we propose to represent the imprecise/partial/vague information about model parameters as fuzzy numbers. Based on some basic results in fuzzy set theory, we derive formulas for “fuzzy credibility premiums”. Our results extend those derived in the above two papers and provide an alternative approach to set credibility premium when the information for model/prior distribution is vague.