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

Joab Odhiambo, Patrick Weke and Philip Ngare
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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

JRFM, 2021, vol. 14, issue 6, 1-12

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.

Keywords: systematic mortality risk; deep learning; long short-term memory; CBD; recurrent neural networks (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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