Description: This report is presented in the form of a Tableau dashboard and is the sixth public release from this report series and contains the results of an excess mortality analysis for the fourth quarter of 2021. Data from 30 companies representing approximately 69% of the industry’s face amount inforce were included in the analyses in this report. A total of 3.2 million death claims from individual life policies from 2015 through December 31, 2021 make up the basis of the analyses. The report also compares the experience under COVID-19 of the insured population versus the general population.

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: This publication presents an exploratory framework that applies volatility signals—specifically the VIX and MOVE indices—to asset allocation decisions. Rather than prescribing a definitive model, the work highlights a potential pathway for linking equity and bond market risk expectations to portfolio construction. The analysis demonstrates how relative volatility between asset classes can inform dynamic weighting, opening the door to more adaptive strategies in shifting market environments. Importantly, this is not intended as a finished methodology, but as a foundation on which more experienced professionals can build. Readers with advanced backgrounds in finance, risk management, or quantitative analysis are encouraged to refine, stress-test, and extend the framework to suit their own investment contexts. By positioning the concept as a starting point rather than a conclusion, the publication invites collaboration and deeper exploration into how volatility-based metrics may enhance long-term allocation models.

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: This report details the 2025 HMI and FMI scale recommendations for individual life insurance mortality valuation under AG38 and VM20 using insured population data.

Description: Learn about the industrial practice for constructing the mortality table from 0 to 1 through journeys in Hong Kong and Singapore, including key takeaways. Hear speakers share their biggest challenges during their process, how they resolved them, and discover the most common technical issues faced in conducting the mortality tables.