Sustainability & AI
AI climate risk modelling
Applying machine learning to assess physical and transition climate risks to assets, portfolios and supply chains.
Definition
Applying machine learning to assess physical and transition climate risks — hazards, exposure and financial impact — for assets, portfolios and supply chains.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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Overview
What it means
Machine learning enhances hazard modelling (downscaling projections, detecting extremes), exposure mapping from imagery, and translation into financial loss estimates. The results feed scenario analyses aligned with frameworks such as the NGFS reference scenarios used by central banks and financial institutions.
How it is used
Banks, insurers and asset managers use AI-assisted models for climate stress-testing and disclosure; corporates use them for site and supply-chain resilience planning. Model opacity and scenario dependence are recognised limitations requiring validation.
Why it matters
Climate risk is a data-sparse, deep-uncertainty problem — exactly where AI both helps and overpromises. Outputs inform real capital allocation, so model governance and honest uncertainty treatment are as material as the projections themselves.
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