Strategic Briefing: Advancements in Spatial Risk Modeling

Traditional methods for incorporating spatial data into risk models are fundamentally limited. Using raw coordinates often produces artificial rectangular patterns in machine learning models, while postal‑code embeddings create artificial discontinuities at administrative boundaries that misrepresent true geographic risk. A new multi‑view contrastive learning framework overcomes these limitations by synthesizing visual satellite imagery and structured OpenStreetMap data‑such as the density of local shops and services‑into a single, powerful spatial feature.

The framework's value was proven in a French real estate pricing study where replacing coordinates with these embeddings consistently improved predictive accuracy across GLM, GAM, and GBM models. The embeddings also eliminated artificial grid‑like artifacts, leading to more realistic spatial effects. Critically, once trained, the model generates these rich features for any location using only its latitude and longitude, making it a highly scalable and efficient tool for deployment.

The strategic implications are substantial. These advanced embeddings enable more granular underwriting and accurate, data‑driven pricing. This unlocks "spatial transfer learning"‑the ability to price risk in new territories or data‑sparse regions by generalizing from a location's underlying characteristics, not just its loss history. This represents a major step toward more robust and adaptive risk management.