Efficient valuation of SCR via a neural network approach

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., 2012) has suggested a nested Monte Carlo (MC) simulation approach to calculate the SCR. But the proposed MC approach is computationally expensive even for a simple insurance product. In this paper, we propose a neural network approach to compute the SCR that significantly reduces 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 type of insurance products called Variable Annuities (VAs). Our experiments show that the proposed neural network framework is both efficient and accurate.