Description: The speakers will work through hands-on Excel examples of bootstrapping interest rate curves—including forward, spot, and par rates. The presenters will also cover models such as Hull-White and the Forward Market Model with an emphasis on derivative valuation and exposure calculations.

Description: Over the last 10 years, Artificial Intelligence (AI) healthcare foundation models have made tremendous strides when it comes to delivering highly accurate predictive results when compared to traditional models. This is especially true for models developed and trained on the ‘language’ of healthcare. When trained on healthcare data sequences (claims, EHR, CPT, ICD, pharmacy, lab, etc.), these models deliver far more accurate results as to WHAT beneficiary-level health events will occur and even WHEN they will occur.

Description: Review a recently completed research project for a multi-stakeholder data consortium on social determinants of health (SDOH). In this project, claims, external SDOH data, geospatial analysis, and machine learning models were used to examine several questions: How to identify SDOH vulnerabilities? How sensitive are the findings with respect to provider coding pattern variations? How much variability in cost can be explained by SDOH vs. morbidity? What are the key risk drivers revealed by machine learning models? The session will review the methodology and main findings from this project.

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: 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: With the mandated health plan transparency files, health plans and providers are now able to substantially enhance their provider contracting efforts. Although the files are large and cumbersome they are market disruptors and provide new insights for provider contracting efforts. Explore a demonstration on the type of information now available and how it can be used to enhance this from both a health plan and health care provider perspective.

Description: Explore a brief overview of federal income taxation of health insurers and selected technical tax topics during this panel discussion webcast. You'll dive into the evolution of heath insurers and healthcare-related services and the arising questions regarding their risk-bearing activities, as well as whether or not certain types of healthcare businesses qualify as "insurance companies" for federal income tax purposes. Also, expect to learn about loss reserves for guaranteed renewable contracts and how they can affect insurance company tax status. This webcast will continue to highlight how the IRS challenges loss reserves of health insurers as the panelists review the status of tax cases, IRS guidance and recent IRS activity regarding "margins."

Description: Dive into the released enhancements of MIM-2021, including a run through of its purpose and evolution, during this webcast. The Mortality and Longevity Strategic Research Program plans to release these updates in October. You will leave this webcast with an understanding of how to create a mortality improvement scale using the necessary steps and tools. Learn how to use MIM-2021 through instruction and demonstration that will properly prepare you to utilize this tool. There will be time for questions where you can ask about the projection process or provide feedback.

Description: This session will discuss essential theories and practical applications of deep learning for actuaries. Participants will gain insights on leveraging these cutting-edge techniques to enhance actuarial models and drive predictive accuracy.

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.