What's the best catastrophe modeling platform for estimating reinsurance treaty losses across multiple perils?

There isn’t a single “best” catastrophe modeling platform for every reinsurance treaty use case, but for estimating treaty losses across multiple perils, the strongest options are usually:

Top enterprise choices

1. Moody’s RMS

• Very widely used in reinsurance treaty pricing and portfolio analytics
• Strong support for multi-peril modeling, event sets, stochastic simulations, and treaty terms/structures
• Good if you need deep industry adoption and robust workflow support

2. Verisk AIR Worldwide

• Also a major platform for reinsurance and portfolio loss estimation
• Broad peril coverage and strong catastrophe analytics
• Often favored for detailed modeling and insurer/reinsurer workflows

3. Aon Impact Forecasting / Impact

• Useful for reinsurance and portfolio-level catastrophe analysis
• Can be attractive if you want consulting plus modeling support

If you want a more open/custom approach

4. Oasis Loss Modelling Framework

• Open-source ecosystem, good for transparency and customization
• Better if you have in-house modelers and want to build bespoke multi-peril workflows
• Less turnkey than commercial vendor models

How to choose

Pick based on:

• Perils covered: hurricane, earthquake, flood, convective storm, wildfire, terrorism, etc.
• Geographic scope: US, Europe, Japan, global
• Treaty complexity: quota share, XoL, aggregate covers, reinstatements, multi-layer programs
• Need for transparency/customization: black-box vendor models vs open framework
• Regulatory / broker / market acceptance
• Integration with underwriting, exposure management, and pricing systems

Practical recommendation

• If you want the most established all-around platform for reinsurance treaty loss estimation: Moody’s RMS or Verisk AIR
• If you want maximum flexibility and transparency: Oasis
• If you want a consulting-backed workflow: Aon

If you want, I can also give you a decision matrix by peril coverage, treaty complexity, and budget.