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.
SPEAKER
Ronald Richman
Ron is an experienced actuary and risk manager, currently Chief Actuary at Old Mutual Insure. Before this, Ron held several other senior roles, including Managing Head of Insurance Actuarial at SA Taxi and Associate Director at QED Actuaries and Consultants. Prior to these, he led the Enterprise Risk Management and Actuarial Functions for the AIG group within Africa. Ron is a Fellow of the Institute and Faculty of Actuaries (IFoA) and the Actuarial Society of South Africa (ASSA), holds practicing certificates in Short Term Insurance and Life Insurance from ASSA, and a Masters of Philosophy in Actuarial Science, with distinction, from the University of Cape Town. Ron chairs the Actuarial Society of South Africa’s ERM committee and is a member of the ASTIN board.
CHAIR
Greg Solomon
Greg Solomon works as an independent consulting actuary out of Hong Kong, where he has been living for the last decade. Under his Eigengrey brand, Greg also does advisory work in the fintech and insurtech space. Prior to this, Greg was with Swiss Re for over 20 years in 4 countries, he ran the global Peak Re L&H business, and set up and grew the Willis Towers Watson Asian life reinsurance broking unit. Greg believes in actuaries and in deep learning models, but has been waiting for someone like Ronald Richman to expertly connect them together.
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