Logistic Regression for Insured Mortality Experience Studies

Abstract

Properly adapted statistical modeling methodology can be a powerful tool for coping with a broad range of challenges related to life and annuity insurance industries' experience studies. In this article, we present a logistic regression model based on U.S. insured mortality experience study with a focus on gaining study efficiency and effectiveness by addressing multiple analytical predicaments within one statistical modeling framework. These predicaments include but are not limited to (a) testing statistical significances or credibility of potential mortality drivers, (b) estimation of normalized mortality, slopes, and differentials, (c) quantification of study reliability, and (d) extrapolation for under-experienced mortality, smoothing between select and ultimate estimations, and development of basic experience tables.