Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit

Abstract

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.

Keywords: Deep learning, actuarial science

JEL Classification: G22, G23