Back to feed
arXiv cs.LG
arXiv cs.LG
5/11/2026
A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence

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

Short summary

SwiGAN, a Conditional GAN framework, generates plausible drought propagation patterns through 2050 using the Soil Wetness Index, addressing 30% of French insurance indemnities. By simulating realistic spatio-temporal climate trajectories, it enables insurers to design adaptive risk strategies beyond 1-year regulatory horizons. The methodology is generalizable to other climate perils and actuarial applications.

  • •Conditional GAN model (SwiGAN) simulates drought patterns and climate scenarios for long-term insurance risk management
  • •Generates spatio-temporal climate trajectories through 2050, addressing 30% of French natural catastrophe losses
  • •Methodology generalizable to other climate hazards and economic scenario generation for actuarial applications

Generated with AI, which can make mistakes.

#ai-tools#research-breakthrough#regulation-policy#industry-adoption
Read full article at arXiv cs.LG

Is this a good recommendation for you?

Explore more

Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa
arXiv cs.LG
Distributional Reinforcement Learning via the Cram\'er Distance
arXiv cs.LG
Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models
arXiv cs.CL
ESA satellite telemetry anomaly detection
arXiv cs.LG