Estimation of future discretionary benefits in traditional life insurance

TL;DR

This paper introduces analytic formulas to estimate bounds for future discretionary benefits in life insurance, providing a computationally efficient alternative to Monte Carlo methods for Solvency II reporting.

Contribution

The paper develops and validates analytic bounds for FDB, offering a practical estimation method that reduces reliance on costly simulations.

Findings

Analytic bounds closely match Monte Carlo estimates.

The method simplifies FDB calculation for real-world applications.

Comparison shows good agreement with reported data.

Abstract

In the context of life insurance with profit participation, the future discretionary benefits ($FDB$), which are a central item for Solvency~II reporting, are generally calculated by computationally expensive Monte Carlo algorithms. We derive analytic formulas to estimate lower and upper bounds for the $FDB$. This yields an estimation interval for the $FDB$, and the average of lower and upper bound is a simple estimator. These formulae are designed for real world applications, and we compare the results to publicly available reporting data.