Defining whether a financial institution is systemically important (or not) is challenging due to: the inevitability of combining complex importance criteria such as institutions' size, connectedness and substitutability; the ambiguity of what an appropriate threshold for those criteria might be; and the involvement of expert knowledge as a key input for combining those criteria. The proposed method, a fuzzy logic inference system, uses four key systemic importance indicators that capture institutions' size, connectedness and substitutability, and a convenient deconstruction of expert knowledge to obtain a systemic importance index; key systemic importance indicators were designed from data from large-value payment systems as well as balance sheets. This method allows combination of dissimilar concepts in a nonlinear, consistent and intuitive manner, while considering them as continuous (nonbinary) functions. Results reveal that the method imitates the way experts themselves think about the decision process regarding what a systemically important financial institution is within the financial system under analysis. The importance index is a comprehensive relative assessment of each financial institution's systemic importance. It may serve financial authorities as a quantitative tool for focusing their attention and resources where the severity resulting from an institution failing or near-failing is estimated to be greatest. It may also serve for enhanced policy-making (eg, prudential regulation, oversight and supervision) and decision-making (eg, resolving, restructuring or providing emergency liquidity).