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A Deep Learning Integrated Cairns-Blake-Dowd (CBD) Sytematic Mortality Risk Model

Author

Listed:
  • Joab Odhiambo

    (School of Mathematics, University of Nairobi, Nairobi City 30197-00100, Kenya)

  • Patrick Weke

    (School of Mathematics, University of Nairobi, Nairobi City 30197-00100, Kenya)

  • Philip Ngare

    (School of Mathematics, University of Nairobi, Nairobi City 30197-00100, Kenya)

Abstract

Many actuarial science researchers on stochastic modeling and forecasting of systematic mortality risk use Cairns-Blake-Dowd (CBD) Model (2006) due to its ability to consider the cohort effects. A three-factor stochastic mortality model has three parameters that describe the mortality trends over time when dealing with future behaviors. This study aims to predict the trends of the model, k t ( 2 ) by applying the Recurrent Neural Networks within a Short-Term Long Memory (an artificial LSTM architecture) compared to traditional statistical ARIMA (p,d,q) models. The novel deep learning (machine learning) technique helps integrate the CBD model to enhance its accuracy and predictive capacity for future systematic mortality risk in countries with limited data availability, such as Kenya. The results show that Long Short-Term Memory network architecture had higher levels of precision when predicting the future systematic mortality risks than traditional methods. Ultimately, the results can be implemented by Kenyan insurance firms when modeling and forecasting systematic mortality risk helpful in the pricing of Annuities and Assurances.

Suggested Citation

  • Joab Odhiambo & Patrick Weke & Philip Ngare, 2021. "A Deep Learning Integrated Cairns-Blake-Dowd (CBD) Sytematic Mortality Risk Model," JRFM, MDPI, vol. 14(6), pages 1-12, June.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:6:p:259-:d:570932
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    References listed on IDEAS

    as
    1. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2011. "Mortality density forecasts: An analysis of six stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 48(3), pages 355-367, May.
    2. Susanna Levantesi & Virginia Pizzorusso, 2019. "Application of Machine Learning to Mortality Modeling and Forecasting," Risks, MDPI, vol. 7(1), pages 1-19, February.
    3. Hainaut, Donatien, 2018. "A Neural-Network Analyzer for Mortality Forecast," LIDAM Reprints ISBA 2018027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Philippe Deprez & Pavel V. Shevchenko & Mario V. Wuthrich, 2017. "Machine Learning Techniques for Mortality Modeling," Papers 1705.03396, arXiv.org.
    5. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    8. Hainaut, Donatien, 2018. "A Neural-Network Analyzer For Mortality Forecast," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 481-508, May.
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    Cited by:

    1. Corsaro, Stefania & Marino, Zelda & Scognamiglio, Salvatore, 2024. "Quantile mortality modelling of multiple populations via neural networks," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 114-133.

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