Mortality forecasting using neural networks and an application to cause-specific data for insurance purposes

Paras Shah and Allon Guez
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Paras Shah: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, USA, Postal: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, USA
Allon Guez: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, USA, Postal: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, USA

Journal of Forecasting, 2009, vol. 28, issue 6, 535-548

Abstract: Mortality forecasting is important for life insurance policies, as well as in other areas. Current techniques for forecasting mortality in the USA involve the use of the Lee-Carter model, which is primarily used without regard to cause. A method for forecasting morality is proposed which involves the use of neural networks. A comparative analysis is done between the Lee-Carter model, linear trend and the proposed method. The results confirm that the use of neural networks performs better than the Lee-Carter and linear trend model within 5% error. Furthermore, mortality rates and life expectancy were formulated for individuals with a specific cause based on prevalence data. The rates are broken down further into respective stages (cancer) based on the individual's diagnosis. Therefore, this approach allows life expectancy to be calculated based on an individual's state of health. Copyright © 2008 John Wiley & Sons, Ltd.

Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:28:y:2009:i:6:p:535-548

DOI: 10.1002/for.1111

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