Longevity risk management through Machine Learning: state of the art
Longevity risk management is an area of the life insurance business where the use of
Artificial Intelligence is still underdeveloped. The paper retraces the main results of the
recent actuarial literature on the topic to draw attention to the potential of Machine
Learning in predicting mortality and consequently improving the longevity risk quantification
and management, with practical implication on the pricing of life products
with long-term duration and lifelong guaranteed options embedded in pension contracts
or health insurance products. The application of AI methodologies to mortality
forecasts improves both fitting and forecasting of the models traditionally used. In particular,
the paper presents the Classification and the Regression Tree framework and
the Neural Network algorithm applied to mortality data. The literature results are discussed,
focusing on the forecasting performance of the Machine Learning techniques
concerning the classical model. Finally, a reflection on both the great potentials of using
Machine Learning in longevity management and its drawbacks is offered.