Description: The Society's Group Disability Insurance Experience Committee has completed their study of termination experience for the period 1997-2006.

Description: Experience report for life insurance policies with face amounts of $1 million or higher.

Description: This article delves into the transformative realm of Business Intelligence (BI) tools. We explore the fundamental functionality and structure of these tools, and through case studies illustrate how they offer valuable insights into carrier experience and underwriting dynamics. From real-time data visualization to in-depth data analysis, discover the essential features that empower businesses to make better informed decisions.

Description: In recent years, vast amount of data from emerging experiences has helped insurers gain a deeper understanding of their policyholders and the factors driving their behaviors. However, due to limitations in traditional experience study methods, this understanding typically remains at the cohort level. This series of articles proposes a novel machine learning-based model that analyzes and predicts policyholder behaviors at the individual policy level. Policyholder embedding representations are learned using a Recurrent Neural Network (RNN) that takes into account the sequence of historical transactions associated with a policy. Additionally, a Neural Network with a multi-head architecture is trained to predict the policyholder's next action based on the policyholder embedding and other policy attributes. The resulting model offers several advantages over traditional experience studies and assumption-setting methods, including the ability to set policy-level assumptions that improve reserve accuracy, predict multiple behaviors simultaneously while capturing the interrelationships between them, and generate policyholder embedding representations that are useful for segmentation.

Description: The Mortality Improvement Model, MIM-2021- V3. Different mortality projection methodologies are utilized by actuaries across applications and practice areas. As a result, the MIM Advisory Group (“Advisory Group”) developed a single framework to serve as a consistent base for practitioners in projecting mortality improvement.

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.

Description: Actuaries can empower their understanding of policyholder behavior with third party data. Modeling policyholder behavior should start from developing knowledge of who they are, instead of actuaries assuming what a rational behavior should be.

Description: The presenters will explore the current state of the North American mortality baseline and long-term improvement assumptions and determine what we can learn from the UK's approach. They will share research done by the Society of Actuaries and other organizations to describe how to build a better model, plus show the impact socio-economics and other factors have on longevity expectations. The presenters will offer suggestions for how U.S. and Canadian pension plan and insurance actuaries--and the industry itself--can best use this research to develop a better model for the risk management of pension plans and group annuities

Description: In this article, we introduce a novel machine learning-based model that analyzes and predicts policyholder behaviors at the individual policy level. Policyholder embedding representations are learned using a Recurrent Neural Network (RNN) that takes into account the sequence of historical transactions associated with a policy. Additionally, a Neural Network with a multi-head architecture is trained to predict the policyholder's next action based on the policyholder embedding and other policy attributes. The resulting model offers several advantages over traditional experience studies and assumption-setting methods, including the ability to set policy-level assumptions that improve reserve accuracy, predict multiple behaviors simultaneously while capturing the interrelationships between them, and generate policyholder embedding representations that are useful for segmentation.

Description: On September 14, 2022, The Wall Street Journal hosted its ‘Future of Health’ event, which was a virtual event featuring three sessions on some emerging cutting-edge topics that could impact the future healthcare environment as well as health care actuaries. This report summarizes the event.