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Application of Machine Learning to Mortality Modeling and Forecasting

1
Department of Statistics, Sapienza University of Rome, Viale Regina Elena, 295/G, 00161 Rome, Italy
2
Ernst and Young Advisory, Via Meravigli, 12, 20123 Milano, Italy
*
Author to whom correspondence should be addressed.
Risks 2019, 7(1), 26; https://doi.org/10.3390/risks7010026
Received: 30 November 2018 / Revised: 1 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Special Issue New Perspectives in Actuarial Risk Management)
Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy. View Full-Text
Keywords: mortality; forecasting; machine learning; Lee-Carter model mortality; forecasting; machine learning; Lee-Carter model
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Levantesi, S.; Pizzorusso, V. Application of Machine Learning to Mortality Modeling and Forecasting. Risks 2019, 7, 26.

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