  Managing and hedging the risks associated with Variable Annuity (VA) products
require intraday valuation of key risk metrics for these products. The complex
structure of VA products and computational complexity of their accurate
evaluation have compelled insurance companies to adopt Monte Carlo (MC)
simulations to value their large portfolios of VA products. Because the MC
simulations are computationally demanding, especially for intraday valuations,
insurance companies need more efficient valuation techniques. Recently, a
framework based on traditional spatial interpolation techniques has been
proposed that can significantly decrease the computational complexity of MC
simulation (Gan and Lin, 2015). However, traditional interpolation techniques
require the definition of a distance function that can significantly impact
their accuracy. Moreover, none of the traditional spatial interpolation
techniques provide all of the key properties of accuracy, efficiency, and
granularity (Hejazi et al., 2015). In this paper, we present a neural network
approach for the spatial interpolation framework that affords an efficient way
to find an effective distance function. The proposed approach is accurate,
efficient, and provides an accurate granular view of the input portfolio. Our
numerical experiments illustrate the superiority of the performance of the
proposed neural network approach compared to the traditional spatial
interpolation schemes.
