Insurance Supervision Under Climate Change: A Pioneers Detection Method

The paper builds on a theoretical model of an insurance market, where independent experts set premiums based on their individual risk evaluations. The segmented nature of the private insurance market hinders the understanding of the tail parameter of the loss distribution, and there's no direct way to eliminate bias, as extreme events are infrequent. The proposed supervisory tool uses temporal changes to consolidate expert opinions, pinpointing those who rapidly and accurately identify extreme climate-related events. The effectiveness of the Pioneers Detection Method is affirmed through a series of simulations, where it surpasses traditional pooling methods within a Bayesian framework. This supervisory approach also proves to be the most beneficial in improving welfare in a fragmented insurance market comprised of a few private insurance companies.