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We introduce a distance between kernels based on the Wasserstein distances between their values, study its properties, and prove that it is a metric on an appropriately defined space of kernels. We also relate it to various modes of convergence in the space of kernels. Then we consider the problem of approximating solutions to forward--backward systems, where the forward part is a Markov system described by a sequence of kernels, and the backward part calculates the values of a risk measure by operators that may be nonlinear with respect to the system's kernels. We propose recursively approximating the forward system with the use of the integrated transportation distance between kernels and we estimate the error of the risk evaluation by the errors of individual kernel approximations. We illustrate the results on stopping problems and several well-known risk measures. Then we develop a particle-based numerical procedure, in which the approximate kernels have finite support sets. Finally, we illustrate the efficacy of the approach on the financial problem of pricing an American basket option.