Description: Modern actuarial work, being quite data intensive, provides many opportunities for actuaries to use the statistical skills that are part of their basic training. Furthermore, modern computing allows us to analyze vast quantities of data intensively, and each package will provide statistical 'answers' to any questions the actuary might ask of it.

Description: At the conclusion of this session, attendees will be equipped to apply relative risk curves in their actuarial work, including the ability to examine dose-response relationships, apply population attributable fractions (PAFs), and develop customized risk models for cause-specific mortality and morbidity outcomes based on individual-level exposures to selected risk factors. These skills are becoming increasingly important as actuaries adopt novel applications of big data and dynamic health information to refine risk assessments and keep pace with global health trends.

Description: Sarah Rockhill, MPH, Geospatial Epidemiologist at CDC will discuss the latest in the Public Health 3.0 model in which leaders serve as Chief Health Strategists, partnering across multiple sectors and leveraging data and resources to address social, environmental, and economic conditions that affect health and health equity.

Description: Virtual - Session 1D: Pharmacy Risk Mitigation - Pricing, Programs, Pools, and Performance Guarantees

Description: Session 10B: Comprehensive PBM Performance Evaluations Including TCOC Impact

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: At the end of this session attendees will understand how their company's existing cash flow models can be leveraged to meet the new requirements for principle-based reserves under VM-22.

Description: The upcoming VM-22 standard promises significant changes for the non-VA life insurance industry, impacting regulatory compliance, risk management, and financial reporting. This presentation equips insurance companies to navigate this crucial transition. We'll delve into key considerations from capital, modeling, and hedging perspectives, providing actionable insights for optimizing your VM-22 implementation strategy.

Description: Come learn from a panel of LTC industry professionals as they discuss setting goals for LTC wellness, measuring success of the programs, and integrating them with other LTC process (e.g. assumption setting) in an effort to create sustainable programs.

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.