  As part of the new regulatory framework of Solvency II, introduced by the
European Union, insurance companies are required to monitor their solvency by
computing a key risk metric called the Solvency Capital Requirement (SCR). The
official description of the SCR is not rigorous and has lead researchers to
develop their own mathematical frameworks for calculation of the SCR. These
frameworks are complex and are difficult to implement. Recently, Bauer et al.
suggested a nested Monte Carlo (MC) simulation framework to calculate the SCR.
But the proposed MC framework is computationally expensive even for a simple
insurance product. In this paper, we propose incorporating a neural network
approach into the nested simulation framework to significantly reduce the
computational complexity in the calculation. We study the performance of our
neural network approach in estimating the SCR for a large portfolio of an
important class of insurance products called Variable Annuities (VAs). Our
experiments show that the proposed neural network approach is both efficient
and accurate.
