Description: Learn from panelists from different companies as they come together to discuss their experiences.

Description: Discussion of a newly released LTC experience study, including description of data and its increased scope compared to the prior report, reporting specifications, and findings.

Description: 1) Vision of the applications and development of data analytics in life insurance industry in Asia; 2) How big is the pie and what are the current obstacles insurers are facing; 3) Utilization of data and analytics target operating models as a competitive advantage, with focus on future readiness, controls, efficiency and effectiveness; 4) Potential roadmap ahead: a) Assessment of the current state of maturity levels of data and analytics initiatives; b) Identification of technology and competency gaps; c) Establishment of data integration and management process within BUs, alignment of advanced analytics tools across departments, with centralized data governance, technology structure and decision making framework; d) Definition of criteria and metrics to measure key outcomes and business value added. 5) Case study on agency behavior management: a practical example on how data analytics is being used in practice: (a) key business objectives: improve agent behaviors and maximize productivity with the lowest possible compensation costs; (b) data collection and current state assessment; (c) detailed predictive modeling process, modeling outcomes, and business implications; (d) implementation and monitoring process.

Description: 1) Vision of the applications and development of data analytics in life insurance industry in Asia; 2) How big is the pie and what are the current obstacles insurers are facing; 3) Utilization of data and analytics target operating models as a competitive advantage, with focus on future readiness, controls, efficiency and effectiveness; 4) Potential roadmap ahead: a) Assessment of the current state of maturity levels of data and analytics initiatives; b) Identification of technology and competency gaps; c) Establishment of data integration and management process within BUs, alignment of advanced analytics tools across departments, with centralized data governance, technology structure and decision making framework; d) Definition of criteria and metrics to measure key outcomes and business value added. 5) Case study on agency behavior management: a practical example on how data analytics is being used in practice: (a) key business objectives: improve agent behaviors and maximize productivity with the lowest possible compensation costs; (b) data collection and current state assessment; (c) detailed predictive modeling process, modeling outcomes, and business implications; (d) implementation and monitoring process.

Description: This article is the first installment of a three-part series that aims to provide an overview of ESGs and will focus on the general motivation for stochastic modeling, its advantages and its limitations. Future articles will further break down the key factors of ESGs and the application of these factors to better understand the Academy Interest Rate Generator (AIRG).

Description: Historical and actuarial perspectives on the coronavirus events of 2020.

Description: In 2006, a pioneering research paper was published on practical considerations for Economic Capital (EC), which has remained a staple on the Society of Actuaries (SOA) exam syllabus ever since. For many, that time represented the golden era of EC-often dubbed the œquantification of Enterprise Risk Management with nearly every major U.S. life insurer performing some form of EC calculation. However, following the 2008-2009 financial crisis, EC fell into disfavor in some circles. Several companies that appeared healthy from an EC perspective prior to the crisis were found to be far more vulnerable afterward.

Description: In this webcast, two points of view are presented - Seven Life Defining Decisions, which presents an individual perspective on lifetime financial choices, and Life-Cycle Fund Design, which discusses the design of a comprehensive life-cycle fund model that includes human capital risk, longevity risk, bequest motive, parameter uncertainty, and different types of annuities and provides a model for financial risk.

Description: This study presents a comprehensive approach of real option analysis to climate adaptation policies aiming to mitigate flood risk. The framework focuses on pricing catastrophic risk in coastal regions, allowing for economic growth and uncertainty in the mean sea level rise.

Description: The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.