  The problem of asset liability management (ALM) is a classic problem of the
financial mathematics and of great interest for the banking institutions and
insurance companies. Several formulations of this problem under various model
settings have been studied under the Mean-Variance (MV) principle perspective.
In this paper, the ALM problem is revisited under the context of model
uncertainty in the one-stage framework. In practice, uncertainty issues appear
to several aspects of the problem, e.g. liability process characteristics,
market conditions, inflation rates, inside information effects, etc. A
framework relying on the notion of the Wasserstein barycenter is presented
which is able to treat robustly this type of ambiguities by appropriate
handling the various information sources (models) and appropriately
reformulating the relevant decision making problem. The proposed framework can
be applied to a number of different model settings leading to the selection of
investment portfolios that remain robust to the various uncertainties appearing
in the market. The paper is concluded with a numerical experiment for a static
version of the ALM problem, employing standard modelling approaches,
illustrating the capabilities of the proposed method with very satisfactory
results in retrieving the true optimal strategy even in high noise cases.
