Correct scoring of a driver's risk 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 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.

翻译：对驾驶员风险的正确评分对汽车保险公司来说非常重要。虽然目前在这一领域使用的工具在实践中证明是相当有效和有益的,但我们认为,在汽车保险风险估算过程中,仍然有很大的发展和改进的余地。为此,我们开发了一个框架,其基础是神经网络与维度降低技术(T-SNE)相结合,同时采用多分布式随机邻居嵌入),这使我们能够将风险的复杂结构作为两维面来显示,同时保持地貌空间中当地区域的特点。 所获得的结果基于真实的保险数据,揭示出高低风险投保人之间的明显差异,而且确实改进了保险人进行的实际风险估算。 由于这一方法中组合的视觉可及性,我们认为,这一框架对于汽车保险人来说可能是有利的,既作为一种主要的风险预测工具,又作为其他方法中的一个额外的验证阶段。