  In this paper, we propose a neural network-based method for approximating
expected exposures and potential future exposures of Bermudan options. In a
first phase, the method relies on the Deep Optimal Stopping algorithm, which
learns the optimal stopping rule from Monte-Carlo samples of the underlying
risk factors. Cashflow-paths are then created by applying the learned stopping
strategy on a new set of realizations of the risk factors. Furthermore, in a
second phase the risk factors are regressed against the cashflow-paths to
obtain approximations of pathwise option values. The regression step is carried
out by ordinary least squares as well as neural networks, and it is shown that
the latter produces more accurate approximations.
  The expected exposure is formulated, both in terms of the cashflow-paths and
in terms of the pathwise option values and it is shown that a simple
Monte-Carlo average yields accurate approximations in both cases. The potential
future exposure is estimated by the empirical $\alpha$-percentile.
  Finally, it is shown that the expected exposures, as well as the potential
future exposures can be computed under either, the risk neutral measure, or the
real world measure, without having to re-train the neural networks.
