SCOR in FORM - December 2013 Logistic Regression for Insured Mortality Experience Studies

No part of this publication may be reproduced in any form without the prior permission of the publisher. SCOR has made all reasonable efforts to ensure that information provided through its publications is accurate at the time of inclusion and accepts no liability for inaccuracies or omissions. Findings of industry mortality experience studies are used by (re)insurers and regulators as the basis for developing liability expectations, reserve guidelines, and solvency capital requirements. In this paper, we introduce a logistic regression based modeling approach for analyzing the US insured mortality experience, including at advanced ages where less credible experience data are available. As a validation for applications, we create industry experience tables based on the model-estimated mortality and compare them to standard industry experience tables produced by the Society of Actuaries (SOA). Our conclusion is that a properly designed logistic modeling approach can enhance industry experience studies in: a) testing mortality drivers' statistical significance in explaining mortality variations; b) estimating normalized mortality slopes and differentials such as how mortality varies by duration or between underwriting classes while product and age distributions are controlled; and c) addressing analytical challenges such as extrapolating for ultimate mortality, smoothing between select and ultimate estimations, and constructing multi-dimensional experience tables. Abstract 4