Lee and Carter go Deep Learning

Time Series Forecasting of Mortality using Neural Networks

Many of these advances have been in the fields of computer vision and
natural language processing, for example, the accuracy of models built to classify the 14 million images in the ImageNet database has steadily
increased since 2011 (Papers with Code, 2020). Characteristically, the models used within these fields are specialized to deal with the types of data that must be processed to produce predictions. For example, when processing text data, which conveys meaning through the placement of words in a specific order, models that incorporate sequential structures
are usually used.

Recently, interest in applying deep learning to actuarial topics has grown, and there is now a body of research illustrating these applications across the
actuarial disciplines, including mortality forecasting. Deep learning is a promising technique for actuaries, due to the strong links between these models and the familiar technique of Generalized Linear Models (GLMs). Wüthrich (2019) discusses how neural networks can be seen as generalized GLMs, that first process the data input to the network to create new variables, which are then used in a GLM to make predictions (this is called ‘representation learning’), which we illustrate in Figure 1. By deriving new features from input data, deep learning models can solve difficult problems of model specification, making these techniques promising analysing
complex actuarial problems, such as multi-population mortality forecasting.