Speaker
Description
Mortality risk modeling and forecasting is one of the key tasks of social security institutions and insurance companies. Traditionally used stochastic mortality models, such as the Lee-Carter model, require fulfillment of formal assumptions that cannot always be met in real-life scenarios (e.g. time independence of age-specific improvement rates). Alternative approaches are based on deep neural networks. Previous work in the field primarily covers recurrent neural networks, typically used in time series forecasting problems, as well as convolutional neural networks and, more recently, also transformer-based architectures. This work aims to analyze the effectiveness of generative adversarial networks in generating and forecasting mortality data based on mortality population data from the Human Mortality Database. The time series specific generative adversarial network (GAN) architecture adjusted for mortality model mechanics is discussed with a focus on the ability to generate new, realistic mortality rates trajectories based on source data, taking into account diversity, fidelity and usefulness criteria. As GAN models do not make any initial assumptions on the distribution of the modeled phenomena, they might serve as an compelling alternative to currently used models for mortality rates simulation and forecasting used by insurance companies.
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