Excited to post a new paper on safely incorporating deep learning models into the actuarial toolkit. The paper covers several important aspects of deep learning 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.
This paper was written under the research grant program of the AFIR-ERM section of the International Actuarial Association, to whom I am very grateful.
Here is an exhibit from the paper showing confidence bands derived using quantile regression:
Here is another exhibit, showing the impact of different hyperparameter choices:
Please read the full paper here if of interest: