Description: "Explore a general introduction to the opportunity of applying a relatively newer area in machine learning called uplift modeling, which has been successfully used in retail (Wayfair etc). The concepts are parallel to the intervention programs in health care, where we are interested in measuring the treatment effects and ultimately directing the right members to the most effective programs, driving the highest value for the programs both from a financial and health outcomes perspective. Uplift modeling can also be viewed in context of causal modeling where we are interested in assigning cause to a treatment relative to a member not receiving the treatment. Check out an introduction on the concept, seehow it relates and builds on existing work, and use a demo to showcase the potential of integrating this ML approach to current work."

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."

Description: The Individual Life Experience Committee continues to unlock valuable insights from the VM51 data as the National Association of Insurance Commissioners (NAIC) releases it. In this session, attendees will get a preview of individual life mortality patterns during COVID-19 for more than 100 companies in the NAIC data from 2020 to 2021, the first years of the pandemic. Attendees will also see early findings from the Committee's latest initiatives. These include integrating predictive analytics into its reporting products, assessing the impact of simplified and accelerated underwriting, exploring joint life mortality, evaluating company differences, and continuing its research into old-age patterns. Attendees will leave the session armed with additional knowledge for assessing best-estimate mortality for valuation.

Description: This session guides actuaries through the key steps of building a principles-based valuation model for a life product, starting with a brief overview of key requirements in LDTI and VM-20.

Description: Even in published scientific research, we find fraudulent data. What is an actuary to do? Let's consider our actuarial principles and standards of practice to question our data quality and truthfulness. In specific, the article addresses ASOP 56 on Modeling and considerations of model risk as well as ASOP 23 on Data Quality. Recent examples are given from a Harvard research fraud controversy as well as questionable sumo statistics.

Description: An overview of guaranteed lifetime withdrawal benefits (GLWB) on registered index-linkedannuity (RILA) products and a stochastic analysis of market risks.

Description: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.

Description: Provides a simplified explanation of the modeling and statistical terms currently in use by the profession, and provides a list of publications worth reading in regards to complexity science plus a list of free software.

Description: For actuarial projections to serve as the management tools that they are intended to be, the persons charged with making those projections need to consider whether the projection is sufficiently sophisticated so that it does not miss major items.It is important to consider the tax effect. This article covers several of the major tax issues that should be considered in any actuarial projection.

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.