Encoding Neighbor Information into Geographical Embeddings Using Convolutional Neural Networks

Geographic information is crucial for estimating the future costs of an insurance contract. It helps identify regions exposed to weather-related events and regions exhibiting higher concentrations of socio-demographic risks such as flood or theft. In actuarial science, the current approach of estimating future costs in a territory is through one-hot encoding of zip codes, postal codes or company-defined polygon levels in statistical learning models. This method has two main drawbacks: it does not share information from similar risk territories and does not share information regarding neighboring areas. We propose the Convolutional Regional Autoencoder model, a method for generating geographical risk encodings using convolutional neural networks. This aims to replace the traditional territory variable for estimating future costs of insurance contracts. Experimental results demonstrate that encodings generated by our approach provide more useful features to predict insurance losses from a real dataset.