EVALUATING UNCERTAINTY WITH CONFIDENCE: A STUDY OF THE REPRESENTATIVE SCENARIO METHOD

The U.S. insurance industry is undergoing a fundamental shift from traditional formula-based reserving to principle-based reserving (PBR), driven by the increasing complexity of insurance products and heightened exposure to market risks such as interest rate and equity volatility. In response, working groups within the American Academy of Actuaries are developing and refining methodologies that better reflect the economic reality of modern insurance contracts.Traditional reserving methods have relied on static, conservative assumptions within closed-form valuation models to ensure solvency under adverse conditions. However, the growing sophistication of insurance products—often embedded with derivative-like features—necessitates more dynamic and realistic approaches. PBR incorporates financial engineering techniques and focuses on the economic value of liabilities, with the Monte Carlo simulation emerging as the preferred tool for capturing complex, path-dependent behaviors.Despite its accuracy, the Monte Carlo method is computationally intensive, particularly given the scale of in-force policy blocks, the complexity of model assumptions, and the extensive parameter space. As a result, improving computational efficiency has become a critical area of research. This paper explores scenario selection—a targeted approach to reducing computational burden without compromising the integrity of valuation outputs. By carefully selecting representative scenarios that preserve statistical properties and risk exposures, actuaries can maintain valuation precision while optimizing resource use.This study highlights scenario selection as a promising technique in supporting the actuarial profession's transition to PBR, underscoring its practical significance in meeting regulatory expectations and adapting to a rapidly evolving financial landscape.