Geographic representation matters more than model complexity for insurance risk prediction—simple coordinate + environmental feature combinations often outperform complex image-based approaches in zone-level claim frequency models.
This study shows how to improve motor insurance claim prediction by adding geographic data to standard actuarial models, even when location information is limited. Researchers tested environmental features from maps and satellite imagery on insurance claims data, finding that combining coordinates with environmental data works best, while image embeddings help when map data isn't available.