AI and Machine Learning for Climate Risk Modelling

Explains how AI can support hazard classification, exposure mapping, loss estimation, scenario generation, and documentation.

Overview

This note by Jonas Osman Abdelghafour sits within the AI for Climate Risk pillar of the Climate Risk Modelling Library. It sets out the business problem, the regulatory context and the risk-management implications for banks, insurers, reinsurers, pension funds, asset managers and supervisory risk teams. It is written as professional commentary on established practice, not as a description of any proprietary implementation.

Why it matters

Climate exposure reaches the balance sheet through credit quality, underwriting results, asset values, reserves and capital. The practical difficulty is rarely the existence of a technique; it is deciding which risks are material, over what horizon, under whose ownership, and with what tolerance — and then being able to explain those judgements to a board and to a supervisor.

Validation and assumption governance

Whatever approach an institution adopts, the assurance expectations are consistent: assumptions have named owners, limitations are stated rather than implied, uncertainty is presented alongside results, independent validation provides effective challenge, and material changes trigger re-review. Confidence in a climate number comes from that evidence trail, not from the sophistication of the method behind it.

Governance and reporting

Model inventory, tiering, limitations, review cycles, change control and approval sit alongside the quantitative work. Outputs are translated into decision-useful KPIs, heatmaps and risk appetite metrics for executives, boards and supervisors.

Full research write-up in preparation. For collaboration or advisory work with Jonas Osman Abdelghafour, see the contact page.