| Neural network models are used across different fields as efficient tools to approximate nonlinear functions and dynamics, also in relation to forecasting. Accurate forecasts and a sound management of prediction uncertainty is of great importance at various decision-making levels. One of these contexts is life insurance, with respect to the actuarial valuations that rely on probabilistic assumptions about the future behaviour of financial and demographic phenomena. We focus on risk of longevity underestimation and show how the assessment of this risk is sensitive to model choices. In the context of the Lee-Carter model, being a benchmark mortality model, we use both autoregressive neural network model with exogenous variables and linear time series model to obtain both point and forecast distributions of death rates. We perform a numerical application to esimate the risk of longevity underestimation implied by both ARIMA and neural network models and monetize it in actuarial terms, based on Swedish mortality data. |
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