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

Paper: arXiv 2605.06678

Authors: Antoine Heranval, Olivier Lopez, Didier Ngatcha, Daniel Nkameni

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.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative application of Wasserstein Conditional GANs to climate risk modeling, specifically addressing spatio-temporal drought propagation and soil subsidence for insurance stress testing. It combines robust deep learning machinery with realistic regional climate data, providing strong practical and empirical utility for long-horizon risk management.

Research Flowchart

  flowchart TD
    A[Goal: Model long-term spatio-temporal<br>drought risks for insurance via AI] --> B[Data Inputs: Soil Wetness Index SWI maps<br>& Climate Change Scenarios up to 2050]
    B --> C[Methodology: Conditional Wasserstein GAN<br>Architecture SwiGAN]
    C --> D[Computational Process: Generate & simulate<br>future spatial drought propagation patterns]
    D --> E[Outcome: Realistic SWI risk trajectories<br>for long-term actuarial & solvency strategies]