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Abstract: The capital allocation problem of an insurance company relates to the decomposition of an aggregate risk capital into additive contributions corresponding to different risk sources (e.g., individual agents, business units, specific guarantees included in a set of contracts). The proportions of risk capital allocated to each part can be viewed as quantitative descriptions of the components of a whole, subject to a fixed sum constraint (full allocation). This interpretation suggests interesting connections between capital allocation principles and Compositional Data (CoDa) analysis. The purpose of this work is to introduce a novel allocation framework that prioritizes the compositional perspective. In particular, we propose an optimality criterion based on a (modified) compositional distance between relative losses and capital shares allocated to them. In this way, we explicitly recognize that the relevant information associated with risks arising from –and resources assigned to– each constituent part is indeed information relative to a total. Different from previous research on compositional capital allocations, our approach is not restricted to gradient allocation rules or specific risk measures like VaR or CVaR. We propose instead a general reformulation of the optimal capital allocation problem which is inherently consistent with the relative scale of compositional variables. The resulting capital allocation solutions are compared to other proposals in recent literature and evaluated empirically on a dataset of cyber losses.