climate-risk

This summer's extreme heat is exposing vulnerabilities in data center infrastructure, with grid operators curtailing power, nuclear plants shutting down, and insurers noting severe weather as top cause of loss, raising questions about the industry's preparedness for a warming world.

This white paper proposes using LSTM neural networks to detect structural breaks in property insurance loss reserving caused by climate-driven catastrophes, aiming to improve accuracy by 15–20% over traditional methods like Chain Ladder.

This paper proposes a deterministic climate-risk intelligence framework integrating orchestration, anomaly detection, and imbalance-aware ensemble learning for auditable ESG validation, addressing fragmented Scope 1-3 reporting data.