A Neural Network Model Approach to Longevity Risk Management

Giovanna Apicella (), Michele La Rocca (), Cira Perna and Marilena Sibillo ()
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Giovanna Apicella: University of Udine, Department of Economics and Statistics
Michele La Rocca: University of Salerno, Department of Economics and Statistics
Marilena Sibillo: University of Salerno, Department of Economics and Statistics

A chapter in New Perspectives in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2025, pp 26-37 from Springer

Abstract: Abstract Insurance activities intrinsically deal with the management of risks. According to the mark-to-model approach to the assessment of the insurer’s debt position, the models used to forecast the probability structures involved in the computation of the fair value of the liabilities are central. We use artificial neural networks to the purpose of forecasting in this context. We focus on a portfolio of endowment insurance policies and on the ex ante estimation of the related mathematical reserve. We consider the Lee-Carter model for the forecasting of the random number of the policies in-force at each future policy anniversary, with either linear time-series model or autoregressive neural network models (ARX-NN models)for the time index. We find out that ARX-NN models allow to reduce the bias that linear time series models may imply. In particular, narrower prediction intervals around central forecasts translates into a much milder upward shift of the portfolio reserve in the case where systematic reduced mortality than expected should occur along the insurance contracts’ duration.

Keywords: neural network models; mortality forecasting; longevity risk management; portfolio reserve (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-05551-4_3

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DOI: 10.1007/978-3-032-05551-4_3

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