Efficient Valuation of SCR via a Neural Network Approach

TL;DR

This paper introduces a neural network method to efficiently and accurately estimate the Solvency Capital Requirement (SCR) for insurance portfolios, significantly reducing computational costs compared to traditional nested Monte Carlo simulations.

Contribution

It presents a novel neural network approach integrated into the nested simulation framework for SCR calculation, improving efficiency and accuracy.

Findings

Neural network approach reduces computational time substantially.

The method maintains high accuracy in SCR estimation.

Effective for large portfolios of Variable Annuities.

Abstract

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.