Climate Risk Modeling

Use AI to model climate risks and improve financial risk management and compliance.

Business impact

• forecast accuracy — Improves precision of climate risk predictions affecting financial outcomes
• risk mitigation — Reduces exposure to climate-related financial losses through scenario analysis
• portfolio performance — Optimizes asset allocation by incorporating climate risk factors
• regulatory compliance — Ensures adherence to evolving climate-related financial regulations and reporting
• investment returns — Identifies climate-resilient opportunities to enhance long-term financial gains

Data requirements

• Climate data (Numeric) — Provides physical and transition risk variables for scenario generation
• Geospatial data (Image) — Enables location-specific risk assessment of assets and supply chains
• Financial portfolios (Structured) — Supplies asset-level data to quantify exposure and impacts
• Macroeconomic indicators (Numeric) — Informs transition risk modeling including policy and economic shifts
• Regulatory frameworks (Text) — Guides compliance requirements and scenario constraints

AI methods and techniques

• Predictive AI — Forecasts climate impacts on financial metrics using historical and scenario data
• Agentic AI — Supports interactive querying and scenario exploration by users
• Generative AI — Generates enriched climate scenarios to augment existing models

AI models and model families

GPT-4, Claude, Custom climate risk simulation models, Agentic AI frameworks

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Barclays, Capgemini SE, HSBC, Nat West Group, Rabobank.