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.

翻译：对投保人进行正确的风险评估对汽车保险公司具有重要意义。尽管目前该领域使用的工具在实践中已被证明相当高效且有益，但我们认为汽车保险风险评估流程仍存在较大的发展与改进空间。为此，我们提出了一种结合神经网络与t-SNE（t分布随机邻域嵌入）降维技术的框架。该框架能够在保留特征空间局部区域特性的同时，将复杂风险结构可视化为二维曲面。基于真实保险数据获得的结果揭示出高风险与低风险投保人之间的明显对比，并且确实优于保险公司实际采用的风险评估方法。由于该框架能够直观展示保险组合结构，我们认为它既可作为主要风险预测工具，也可作为其他方法的补充验证阶段，从而为汽车保险公司带来优势。