The properties of risk measures in the form of infimal convolution are analyzed. The dual representation of such measures, their subdifferential, extremum conditions, representation for optimization and use in constraints are described. The results of the study are demonstrated by examples of known risk measures of such structure. This allows systematization of the available results and facilitates a potential search for new variants of risk measures.