  Insurance companies gather a growing variety of data for use in the insurance
process, but most traditional ratemaking models are not designed to support
them. In particular, many emerging data sources (text, images, sensors) may
complement traditional data to provide better insights to predict the future
losses in an insurance contract. This paper presents some of these emerging
data sources and presents a unified framework for actuaries to incorporate
these in existing ratemaking models. Our approach stems from representation
learning, whose goal is to create representations of raw data. A useful
representation will transform the original data into a dense vector space where
the ultimate predictive task is simpler to model. Our paper presents methods to
transform non-vectorial data into vectorial representations and provides
examples for actuarial science.
