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: Come discuss the benefits and challenges of collaboration between academic institutions and industry stakeholders in shaping actuarial education and research agendas. Register for this event now.

Description: Understanding Asian Capital Regimes

Description: Analyze financial risk and explore the foundation for pricing, valuation and other functions served by actuaries in the modeling profession during this webcast. Expect a focus on the importance of understanding and preventing bias. Observe the different types of bias, how it can enter into models, how to identify it in models and best avoidance practices. Join our presenter who is pursuing a Masters in Economic Analytics at Clemson University as he incorporates his recent economic research into the webcast material.

Description: The post COVID-19 period exhibited a fast-changing capital market with higher interest rates, uncertain monetary policies, and revaluation of asset classes. At the same time, regulators introduced changes to reward better risk management, such as the scenario-based approach for reserving under Bermuda regulatory framework. For life reinsurers, while new opportunities arising from these changes, challenges also exist to be able to adjust reinsurance portfolios and ALM strategies appropriately. A holistic modeling framework can assist companies better position themselves in front of these challenges and other potential future unknowns. In particular, a well-designed stochastic and dynamic modeling framework enables the quantifications of such probabilities and potential impacts.

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.

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: 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.