Insurance Supervision under Climate Change: A Pioneer Detection Method

TL;DR

This paper introduces the Pioneer Detection Method, a novel supervisory tool designed to improve insurance market resilience to climate change by integrating expert opinions and detecting trend shifts in risk assessments.

Contribution

The paper presents a new method that combines temporal analysis and expert convergence to better estimate loss distribution tails in climate-related insurance risks.

Findings

Outperforms traditional pooling methods in simulations

Enhances welfare in markets with few private insurers

Effective in identifying climate-induced risk trend shifts

Abstract

We present the Pioneer Detection Method, a supervisory tool we developed to enhance resilience in insurance markets facing the challenges posed by climate change. Based on a theoretical model of the insurance industry, we consider a scenario in which independent experts determine premiums according to their individual risk assessments. Due to the segmented nature of the private insurance market, accurately estimating the tail parameter of loss distribution is difficult, especially given the rarity of extreme events. Our method leverages temporal directional change and convergence to integrate expert opinions, giving greater emphasis to those who effectively identify trend shifts after climate stress. A series of simulations reveals that the Pioneer Detection Method outperforms traditional pooling methods within a Bayesian framework. Furthermore, this approach appears to be notably effective in improving welfare in an insurance market with a limited number of private entities.