Introduction

Catastrophe modeling enables estimating potential future disaster impacts informing reinsurance pricing and capital allocation. Traditional approaches rely primarily on historical data, but severe catastrophes are rare requiring extrapolation. Generative models creating synthetic weather scenarios enable more robust catastrophe risk modeling incorporating scenarios not yet observed but plausible.

Generative Weather Modeling Approach

Diffusion models and other generative models trained on historical weather data generate plausible synthetic hurricane and flood scenarios reflecting realistic physical processes and correlations.

Risk Assessment Improvements

Using synthetic scenarios improves tail risk estimation (probability of severe catastrophes) enabling better catastrophe bond pricing and reinsurance decisions. Models avoid over-reliance on limited historical records.

Conclusion

Generative weather scenarios improve catastrophe risk assessment and reinsurance strategy.