Description: The NAIC continues to move ahead with the GOES (generator of economic scenarios) project for the transition to a new real-world economic scenario generator (ESG) prescribed for principle-based reserve and capital calculations. This session will provide a detailed project update from the regulator and practitioner perspective. An overview of model details and methods that have been decided, what remains to be determined, and the projected timeline for implementation will be discussed. Recent developments such as the industry field test and NAIC model office testing to assess the potential impact on reserves and capital will be discussed as will a review of the implementation of the stochastic exclusion ratio test (SERT). We will also discuss the components of the ESG model and the associated scenario sets the NAIC has exposed with respect to target and acceptance criteria. At the conclusion of this session, attendees will understand the current status of the NAIC GOES project and its projected timeline and will be able to describe the components of the model and how the model has been calibrated and validated. Attendees will also appreciate the remaining challenges in bringing the model to full implementation.

Description: This report provides insights into statistical methods for imputing race and ethnicity in the context of insurance industry disparities. It discusses various imputation techniques, performance metrics, and the evolution of methods over time. The aim is to address the lack of consistent data collection on race and ethnicity in insurance datasets.

Description: Discuss various analytics and use cases using payer published reimbursement contract data under federal mandate of Transparency in Coverage. These contract rates are publicly available in machine readable format (MRF) starting July 1, 2022, and refreshed monthly. By attending the session, you will have a better understanding of the state-of-art development and analytics using MRF data, including the technologies used to extract and analyze huge MRF files, validation and limitations of published rates, and their use cases.

Description: "Discuss research conducted at the Cleveland Clinic to understand levels of anxiety and depression across Northeast Ohio. Specifically, learn how advanced analytical techniques were utilized with social media data from Twitter and Google to estimate the need for psychological support by geography and overtime. The panel will go further to demonstrate how natural language processing (NLP) and machine learning could be used to make operational decisions at a leading health care provider."

Description: A preview of the details and content of the new Certificate in Climate Risk to be offered by the SOA beginning in early 2023.

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 research report educates the actuarial community about the various shades of the flood risk as well as the state of the art in quantitative flood risk modeling.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: This article discusses the Modeling Actuarial Standard of Practice. Intent and questions related to a model across all practice areas is discussed

Description: This article introduces the reader to probabilistic methods used to model interactions in the financial market place. Three topics are specifically discussed, two concern modeling methods and the other concerns data analysis methods.