  Unstructured data are a promising new source of information that insurance
companies may use to understand their risk portfolio better and improve the
customer experience. However, these novel data sources are difficult to
incorporate into existing ratemaking frameworks due to the size and format of
the unstructured data. In this paper, we propose a framework to use street view
imagery within a generalized linear model. To do so, we use representation
learning to extract an embedding vector containing useful information from the
image. This embedding is dense and low-dimensional, making it appropriate to
use within existing ratemaking models. We find that there is useful information
included in street view imagery to predict the frequency of claims for certain
types of perils. This model can be used as-is in a ratemaking framework but
also opens the door to future empirical research on attempting to extract the
causal effect from images that lead to increased or decreased predicted claim
frequencies. Throughout, we discuss the practical difficulties (technical and
social) of using this type of data for insurance pricing.
