Neural nets catch property risk ZIP codes miss

Neural networks are designed to handle that mess. During training, they generate embeddings – compact numerical representations that map how properties relate to each other across time and space. Think of an embedding as a coordinate in a multidimensional risk space. Two properties with the same roof age but different claim histories and weather exposures end up in different positions. Two properties with different construction types but similar loss patterns sit closer together. The model finds similarities the ZIP-code approach misses.

Verisk, which published this analysis, says the approach can produce wider separation between high-risk and low-risk properties than traditional methods. More separation means insurers can price more precisely, not just raise rates for everyone in a territory. It also feeds into underwriting decisions, policy lifecycle management and portfolio risk.

The implications go beyond pricing. If a carrier can see that a specific block has ten houses that behave like high-risk properties even though the ZIP code says otherwise, the carrier can adjust exposure. It can buy reinsurance for the properties that need it. It can flag a cluster before the next storm hits.

This is not theoretical. As more data streams in – weather telemetry, satellite imagery, sensor data from smart homes – the embeddings get richer. The model learns. The same network that now pulls apart subtle risk differences in a single ZIP code will eventually do it at the parcel level for entire regions.

Two neighboring houses survive the same storm differently. Insurers cannot control the weather. They can get better at understanding why.