  Correct risk estimation of policyholders is of great significance to auto
insurance companies. While the current tools used in this field have been
proven in practice to be quite efficient and beneficial, we argue that there is
still a lot of room for development and improvement in the auto insurance risk
estimation process. To this end, we develop a framework based on a combination
of a neural network together with a dimensionality reduction technique t-SNE
(t-distributed stochastic neighbour embedding). This enables us to visually
represent the complex structure of the risk as a two-dimensional surface, while
still preserving the properties of the local region in the features space. The
obtained results, which are based on real insurance data, reveal a clear
contrast between the high and the low risk policy holders, and indeed improve
upon the actual risk estimation performed by the insurer. Due to the visual
accessibility of the portfolio in this approach, we argue that this framework
could be advantageous to the auto insurer, both as a main risk prediction tool
and as an additional validation stage in other approaches.
