  Insurance companies make extensive use of Monte Carlo simulations in their
capital and solvency models. To overcome the computational problems associated
with Monte Carlo simulations, most large life insurance companies use proxy
models such as replicating portfolios.
  In this paper, we present an example based on a variable annuity guarantee,
showing the main challenges faced by practitioners in the construction of
replicating portfolios: the feature engineering step and subsequent basis
function selection problem.
  We describe how neural networks can be used as a proxy model and how to apply
risk-neutral pricing on a neural network to integrate such a model into a
market risk framework. The proposed model naturally solves the feature
engineering and feature selection problems of replicating portfolios.
