Description: 1991-1995 individual life experience studies table.

Description: Authored by a MIB Solutions team of Tom Rhodes, Stuart Klugman, Marianne Purushotham and Stacy Gill, the report summarizes the results of a study on the application and adoption of credibility theory within the life insurance and annuity industry. In addition to the report, numerical examples related to the application of credibility to company mortality and lapse data are provided in the four Excel files. This excel spreadsheet is a numerical example relating to the application of Credibility for mortality. Please see the SOA website research section for the complete report on Credibility Theory Practices.

Description: Authored by a MIB Solutions team of Tom Rhodes, Stuart Klugman, Marianne Purushotham and Stacy Gill, the report summarizes the results of a study on the application and adoption of credibility theory within the life insurance and annuity industry. In addition to the report, numerical examples related to the application of credibility to company mortality and lapse data are provided in the four Excel files. This excel spreadsheet is a numerical example relating to the application of Credibility for mortality - Limited Fluctuation Method. Please see the SOA website research section for the full report on Credibity Theory Practices.

Description: This report explains the concept and construction of the Market Consistent Embedded Value (MCEV) measure, as well as its usefulness as a profit/risk measure relative to other profit/risk measures currently used by insurance companies for various products. The knowledge from this research will assist actuaries, corporate leadaers, risk management professionals and others in understanding, implementing and comparing MCEV to current risk assessment tools utilized by insurance companies.

Description: Excel file to be attached to cover sheet on this same 2005-2007 individual life persistency study. Contains the actual persistency experience data from the study.

Description: Sent to potential participants to gather information on different preferred class structures.

Description: Read the Quo Vadis Reinsurance.

Description: Regulators observed a wide range of practice from early adopters of PBR in regard to the treatment of non-guaranteed reinsurance and adopted a formulaic solution on an interim basis for the 2020 Valuation Manual. A field test, supplemented with additional analysis performed using a model-office approach with representative models, is being performed over the first half of 2020 to inform regulators as they select a longer-term solution. This session will provide an update on this important issue as well as perspectives on how the resolution of this issue was approached and lessons learned for the future from those intimately involved in this work.

Description: The presenters will share information about: -Insights into causes of death through the use of predictive models, based on a reinsured U.S. life portfolio, using logistic regression models to understand three main causes of death: cancer, heart and external causes; -Opioid abuse is a widespread phenomenon with drug-related mortality rates rising steeply in the U.S. over the past two decades. But what does the most recent data tell U.S. about the outlook for Opioid-related mortality?; -'Deaths of despair' have been publicized as a cause for the poor performance of U.S. mortality in recent years. The presenters will be looking at the different causes under this label, their evolution and explore the drivers behind them.

Description: "This session will explore the critical challenges and solutions surrounding algorithmic fairness in the life insurance industry. The presenters will delve into the fundamental mechanisms of model bias, examining how predictive algorithms can yield outcomes that systematically discriminate against certain groups through flawed machine learning processes, erroneous assumptions, or biased data. Participants will gain insights into the various manifestations of bias in insurance models and understand their profound implications for both consumers and insurers. Real-world case studies and examples from the insurance industry will illustrate these concepts, while a discussion of recent regulatory developments will provide context for compliance requirements. This session is essential for actuaries, data scientists, and insurance professionals committed to developing and maintaining ethical, unbiased predictive models."