Deep neural network models have substantial advantages over traditional and machine learning methods that make this class of models particularly promising for adoption by actuaries. Nonetheless, several important aspects of these models have not yet been studied in detail in the actuarial literature: the effect of hyperparameter choice on the accuracy and stability

of network predictions, methods for producing uncertainty estimates and the design of deep learning models for explainability. To allow actuaries to incorporate deep learning safely into their toolkits, we review these areas in the context of a deep neural network for forecasting mortality rates.

Outcomes

1.Gain insight into some of the aspects of machine learning models that require special focus when used in an actuarial context

2.Understand applications of these models in a large scale mortality modelling task

3.Be introduced to new methods for uncertainty quantification

4.Understand methods for producing inherently interpretable machine learning models