AI-Assisted Climate Risk Stressing
Purpose
Combine external climate models with AI pattern recognition to simulate event frequency/severity shifts under climate scenarios.
Primary users
Catastrophe risk teams, climate risk specialists, ERM, underwriting and reinsurance teams.
Where it fits (process/stage/trigger)
Climate stress testing, ORSA / internal risk assessment, and strategic planning cycles.
Key capabilities / workflow
Scenario mapping; hazard/exposure alignment; AI detection of non-linear changes; distribution shifts; loss simulation; KPI impact reporting.
Inputs
Climate projections, hazard data, exposure data, catastrophe models, loss data.
Outputs / deliverables
Adjusted peril assumptions, scenario loss distributions, portfolio loss metrics (AAL/PML), KPI impact summaries, audit-ready documentation.
Value
More realistic climate-stressed assumptions; improved risk pricing, reinsurance strategy, and capital planning under climate uncertainty.
