Description: Cover a variety of topics centered around the unsupervised learning technique called 'clustering'. The presenters will take you through a wide array of topics that range from 'What is Clustering?' to 'How can I use this technique to solve problems I didn't even know existed?' In the health care industry, everyone is looking for an edge in reducing total spend-cluster analysis finds the unambiguous in an ambiguous framework of results.

Description: This is the second of a two-part article introducing and elaborating an “Adaptor” strategy for ERM. Such a strategy is intended to fulfil two purposes: first, switching among four styles of risk-coping strategies in order to track qualitative changes in a business’s risk environment; and, second, to support the business in navigating with resilience through all the seasons of a business-economic cycle, i.e., of boom, bust, moderate, uncertain. This latter requires careful study of the nature of resilience, the familiar idea of bounce back, and the less familiar idea of bounce forward. Part I of our article is underpinned therefore by a supplementary document (available from https://riskviews.wordpress.com/adaptor-exceptionalism/), which addresses the issue of structural change in a business or economy. It draws heavily on ideas taken from the discipline of control engineering, as well as some from ecology, social anthropology, and economics. The entire two-part article is thus an exercise in cross-disciplinary Systems Thinking.

Description: This is the first of a two-part article introducing and elaborating an “Adaptor” strategy for ERM. Such a strategy is intended to fulfil two purposes: first, switching among four styles of risk-coping strategies in order to track qualitative changes in a business’s risk environment; and, second, to support the business in navigating with resilience through all the seasons of a business-economic cycle, i.e., of boom, bust, moderate, uncertain. This latter requires careful study of the nature of resilience, the familiar idea of bounce back, and the less familiar idea of bounce forward. Part I of our article is underpinned therefore by a supplementary document (available from https://riskviews.wordpress.com/adaptor-exceptionalism/), which addresses the issue of structural change in a business or economy. It draws heavily on ideas taken from the discipline of control engineering, as well as some from ecology, social anthropology, and economics. The entire two-part article is thus an exercise in cross-disciplinary Systems Thinking.

Description: Amid the growing adoption of AI/ML models for underwriting and pricing, Life and Annuity carriers are facing increasing regulation in North America on Model Risk Management (MRM) and the use of AI. Colorado has set a precedent in the US by requiring reporting in June 2024, with other states looking to follow suit. To ensure compliance, Chief Risk Officers and Heads of MRM, working with their business partners, will need to evolve their MRM and AI risk frameworks, enhance model validation capabilities, and strengthen governance. This session will present: - Key trends and challenges faced by carriers amid increasing model landscape complexity and regulatory expectations - Regulatory outlook and implications for carriers' MRM and AI risk programs - An approach to enhance MRM and AI risk frameworks, including specific model validation program updates to address the unique risks of AI/ML models - Opportunities to enhance both MRM validation effectiveness and efficiency through the adoption of more risk-based approaches, smart sourcing, and automation - Case studies of how leading carriers are evolving their MRM and AI risk programs Upon attending the session, the attendees will develop a good understanding of increasing regulatory expectations, the required MRM and AI risk framework enhancements, and approaches to design and execute an effective MRM validation program in a cost-efficient manner.

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: How do social determinants of health (SDOH) affect health outcomes in the United States, how might SDOH factors be incorporated into financing and risk adjustment of Medicaid, and what implications might this have for market participants?

Description: This report provides actuaries and other finance professionals with an example of running the Medicaid Unwinding model with results at the national level on projected changes in both acuity and enrollment for Medicaid and Children’s Health Insurance Program.

Description: This project models and forecasts premature cardiovascular mortality, with a focus on two main risk factors - obesity status and education attainment level. It investigates the role of obesity and education in determining age-sex-specific premature cardiovascular mortality.