Description: The U.S. health care industry has gone through a dramatic change during the past several years. The business model has moved from a risk selection model to a risk management model. People can shop online for their health care coverage. Everyone is welcome! Annual and lifetime dollar limits, underwriting and the pre-existing conditions exclusion are all in the history book now. Facing the challenges of the post-reform era, many health insurance carriers began using big data analytics on all aspects of their decision-making processes. The goal is to be able to compete on analytics in this quickly changing environment. However, becoming an analytics competitor is not easy. It requires the belief from the leadership and a group of dedicated analytical professionals to solve emergent business problems that existing strategies cannot address in an evolving market. These problems include marketing for acquisition and retention, coding accuracy, revenue growth, health care disparity, payment integrity, quality of care, health care cost reduction and, ultimately, personalized care management. In this section, audience will learn a basic understanding of the role of predictive analytics in US Healthcare marketplace.

Description: According to the Actuarial Standards Board’s Actuarial Standard of Practice No. 49 (ASOP 49), actuarially sound Medicaid managed care capitation rates will include a provision for underwriting gain in order to provide compensation for the risks assumed by an MCO. However, there is no generally accepted method for quantifying underwriting gain in managed Medicaid capitation rates. This article discusses a new model developed to address this void and calculate underwriting gain. The model was developed by researching existing financial literature and performing a statistical analysis of industry data to build out a framework that can be leveraged to estimate underwriting gain.

Description: In this article, we talk to Lei Rao-Knight, FSA, Vice President and Actuary at Prudential Financial. Lei discusses her model development experience in the Modeling Center of Excellence.

Description: In *From Distress to Distinction*, Chris Droussiotis explores a novel approach to valuing distressed companies using option pricing theory, drawing parallels to the financial challenges faced by startups. With over 30 years in banking and startup advising, he emphasizes that both distressed firms and early-stage ventures endure periods of negative cash flow but retain potential for recovery. He combines Discounted Cash Flow (DCF) and the Black-Scholes Option Pricing Model to evaluate distressed firms’ recovery prospects and debt sustainability. A case study on Spirit Airlines illustrates this approach: though its current equity value is negative, projected future cash flows suggest significant potential recovery. Droussiotis concludes that strategic valuation, using trend, peer, and expected analysis, can reveal hidden value in distressed companies, guiding effective investment and restructuring decisions.

Description: Session 4B: Impact of GLP-1 Drugs on Health Care Cost Trend and Clinical Outcomes

Description: This panel discussion dives into the NAIC's new principles-based reserving (PBR) requirements for fixed annuities (VM-22) set to be effective January 1, 2026. After a brief overview of the VM-22, this session will focus on practical implementation considerations and adapting cash flow models to the new requirements, tackling sensitivity runs, margin testing, the Stochastic Exclusion Test, and mapping prescribed assumptions for the Standard Projection Amount (SPA) to companies' unique products and features.

Description: Learn from panelists from different companies as they come together to discuss their experiences.

Description: Understanding Asian Capital Regimes

Description: 1) Vision of the applications and development of data analytics in life insurance industry in Asia; 2) How big is the pie and what are the current obstacles insurers are facing; 3) Utilization of data and analytics target operating models as a competitive advantage, with focus on future readiness, controls, efficiency and effectiveness; 4) Potential roadmap ahead: a) Assessment of the current state of maturity levels of data and analytics initiatives; b) Identification of technology and competency gaps; c) Establishment of data integration and management process within BUs, alignment of advanced analytics tools across departments, with centralized data governance, technology structure and decision making framework; d) Definition of criteria and metrics to measure key outcomes and business value added. 5) Case study on agency behavior management: a practical example on how data analytics is being used in practice: (a) key business objectives: improve agent behaviors and maximize productivity with the lowest possible compensation costs; (b) data collection and current state assessment; (c) detailed predictive modeling process, modeling outcomes, and business implications; (d) implementation and monitoring process.

Description: In the context of reinsurance, this paper addresses finding the quota share retention level and the retention limit, with a diffusion process being added to the claim process, that maximizes the adjustment coefficient in the Sparre Anderson model. From Actuarial Research Clearing House, 2006.