Logistic Regression for Insured Mortality Experience Studies

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 paper, we present a logistic regression model based 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 not limit to: a) test statistical significances or credibility of potential mortality drivers; b) estimate normalized mortality, slopes, and differentials; c) quantify study reliability; and d) extrapolate for under-experienced mortality, smooth between select and ultimate estimations, and assist basic experience table developments