Climate Risk Assessment

Use AI to assess and manage physical and transition climate risks for resilience and compliance.

Business impact

• forecast accuracy — Improves precision of climate event predictions enabling proactive responses
• risk mitigation — Reduces exposure to climate-related operational and financial risks
• expected credit loss — Refines credit risk estimates by incorporating climate transition and physical risks
• preparedness lead time — Extends warning periods for extreme weather events to enable early action
• operational resilience — Strengthens ability to maintain operations during climate disruptions

Data requirements

• Historical climate data (Numeric) — Used to train models on past climate patterns and trends
• Satellite imagery (Image) — Provides real-time environmental monitoring and verification
• Paleoclimate simulations (Numeric) — Offers long-term climate context for model robustness
• Financial and credit data (Structured) — Integrates climate risk into credit loss and portfolio models
• Geospatial data (Numeric) — Maps risk exposure across locations and assets
• Climate scenario projections (Numeric) — Enables stress testing under various future climate conditions
• Corporate climate disclosures (Text) — Supports verification and assessment of transition risks

AI methods and techniques

• Predictive AI — Forecasts climate events and financial impacts based on historical and scenario data
• Generative AI — Synthesizes insights from complex climate reports and disclosures for decision support
• Symbolic AI — Incorporates rule-based climate risk frameworks and regulatory requirements

AI models and model families

GPT-4, Claude, FourCastNet, Custom climate risk ML models

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: Barclays, CMCC Foundation Euro Mediterranean Center Climate Change, Clima Sens, Nat West Group, Rabobank, Zendesk.