The author presents the selected methods of actuarial science that apply fuzzy set theory as a representation of information vagueness and imprecision. The fuzzy representation of the real-world-problems was proposed due to the nature of the decision maker: propensity for simplification and tendency to describe the problems rather in linguistic than in mathematical manner. In this paper he demonstrates the fuzzy methods that aim at particular actuarial problems: internal rate of return, net present value, underwriting, evaluation of tax liabilities and insurance rate, and classification of individual risk. Among the other the concepts of fuzzy zooming and fuzzy interest curve are described. From the technical standpoint also the actuarial applications of fuzzy pattern recognition are presented. In details he shows the fuzzy c-means algorithm. Finally the author discusses using of expert systems based on fuzzy decision rules in actuarial science. In conclusions he mentions the constraints of presented solutions and the future research area.