Continuous compliance: a proxy-based monitoring framework

TL;DR

This paper proposes a proxy-based monitoring framework using Curve Fitting and Least Squares Monte Carlo to efficiently assess continuous compliance with Solvency II requirements in life insurance companies.

Contribution

It introduces a novel proxy-based approach for ongoing compliance monitoring, comparing two methodologies within a practical insurance context.

Findings

Curve Fitting and Least Squares Monte Carlo effectively estimate regulatory capital over time.

The proposed framework simplifies compliance monitoring in complex insurance models.

Guidelines for selecting and implementing proxy methods are provided.

Abstract

Within the Own Risk and Solvency Assessment framework, the Solvency II directive introduces the need for insurance undertakings to have efficient tools enabling the companies to assess the continuous compliance with regulatory solvency requirements. Because of the great operational complexity resulting from each complete evaluation of the Solvency Ratio, this monitoring is often complicated to implement in practice. This issue is particularly important for life insurance companies due to the high complexity to project life insurance liabilities. It appears relevant in such a context to use parametric tools, such as Curve Fitting and Least Squares Monte Carlo in order to estimate, on a regular basis, the impact on the economic own funds and on the regulatory capital of the company of any change over time of its underlying risk factors. In this article, we first outline the principles of the continuous compliance requirement then we propose and implement a possible monitoring tool enabling to approximate the eligible elements and the regulatory capital over time. In a final section we compare the use of the Curve Fitting and the Least Squares Monte Carlo methodologies in a standard empirical finite sample framework, and stress adapted advices for future proxies users.