Quantifying Life Insurance Risk using Least-Squares Monte Carlo

TL;DR

This paper introduces a fast, efficient least-squares Monte Carlo framework to quantify long-term biometric risks in life insurance portfolios, aligning with solvency regulation requirements like Solvency II.

Contribution

It presents a novel, computationally efficient stochastic model for internal risk assessment that avoids nested simulations, suitable for regulatory compliance.

Findings

Provides a fast, easy-to-implement method for risk quantification.

Accurately captures long-term risks in profit-loss distribution.

Applicable under Solvency II and Swiss Solvency Test regimes.

Abstract

This article presents a stochastic framework to quantify the biometric risk of an insurance portfolio in solvency regimes such as Solvency II or the Swiss Solvency Test (SST). The main difficulty in this context constitutes in the proper representation of long term risks in the profit-loss distribution over a one year horizon. This will be resolved by using least-squares Monte Carlo methods to quantify the impact of new experience on the annual re-valuation of the portfolio. Therefore our stochastic model can be seen as an example for an internal model, as allowed under Solvency II or the SST. Since our model does not rely upon nested simulations it is computationally fast and easy to implement.