A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence

TL;DR

This paper presents SwiGAN, a Wasserstein GAN-based model that generates realistic future drought scenarios using the Soil Wetness Index, aiding climate risk management and insurance planning in France.

Contribution

The paper introduces SwiGAN, a novel AI framework employing Conditional GANs to simulate future climate-related drought patterns for risk assessment.

Findings

SwiGAN effectively models drought propagation up to 2050 in France.

Generated SWI sequences provide realistic drought scenarios under climate change.

The approach is adaptable to other climate hazards and actuarial applications.

Abstract

According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion USD between 1970 and 2000 to 180--200 billion USD between 2001 and 2020. Reports from organizations such as the IFOA and the WWF highlight the need for the insurance sector to adapt to this rapidly evolving context by developing medium- to long-term strategies that go beyond the one-year horizon of prudential regulations such as Solvency II. This paper introduces an artificial intelligence framework based on Conditional Generative Adversarial Networks (Conditional GANs) to generate future spatio-temporal trajectories of climatic indices. The approach focuses on the Soil Wetness Index (SWI), a key indicator used in France to assess drought severity. Drought accounts for approximately 30% of the indemnities paid under the French natural catastrophe insurance scheme. The proposed model, SwiGAN, simulates plausible drought propagation patterns up to 2050 for a region of France particularly exposed to this hazard. By generating realistic sequences of SWI maps, SwiGAN provides insights into drought dynamics under climate change scenarios and supports the design of adaptive risk management and insurance strategies. The methodology is also generalizable to other climate-related perils and actuarial applications such as economic scenario generation.