| dc.description.abstract | This study considered the modeling of systematic mortality risk for populations with finite data using the Poisson incorporated
Credibility regression model. For novelty, we have included the credibility regression approach to modelling mortality by
assuming the number of annual deaths follow a Poisson distribution. Our model shows improvement in precision levels when
estimating mortality risk compared to classical models used in European countries. We have illustrated that our model works
optimally when using Kenyan mortality data, comparing male and female lives under the different strategies, thus making better
predictions than the classical Lee–Carter (LC) and Cairns–Blake–Dowd (CBD) models. e mean absolute forecast error (MAFE),
mean absolute percentage forecast error (MAPFE), root mean square error (RMSE), and root mean square forecast error (RMSFE) under the incorporated credibility regression model are much lower than the values obtained without incorporation of the Buhlmann credibility approach. The findings of this research will help insurance companies, pension firms, and government
agencies in sub-Saharan countries model and forecast systematic mortality risks accurately. Finally, the results are essential in
actuarial modelling and pricing, thus making life assurance products affordable for most people in low-income African countries. | en_US |
