Description: Future mortality improvement has been a common assumption in life insurance and annuity pricing and forecasting for many years. However, experience over the last decade suggests this might be an assumption that merits reconsideration in the United States. This article will take a closer look at factors that impact mortality improvement trends and discuss why insurers may want to reevaluate product designs to better manage the associated mortality risk. We then introduce a product idea with this in mind, where premiums are based on a life expectancy index and insurers share the risk (and opportunity) or mortality improvement with policyholders.

Description: The presentation will cover conclusions, the mathematical techniques chosen (LightGBM), the rationale for that choice, and the technical tools used to collaborate among a dispersed team. (R, GitHub, CodeSpaces)

Description: In this session, we will delve into the powerful capabilities of two essential algorithms in the field of data science and machine learning: Generalized Additive Models (GAM) and CatBoost. These algorithms can play a crucial role in conducting experience studies for insurance.

Description: This report provides a resource guide for asset-liability management practitioners, including methodology reviews, a survey of ALM practices, and guidance for applying ALM questions to real-world situations.