Description: This session will explore the evolving landscape of accident and health valuation, with a special focus on GAAP Long Duration Targeted Improvements. Discussion will delve into the intricacies of change brought by GAAP LDTI for calculation of both Lifetime Future Policy Benefits (LFPB) and Deferred Acquisition Costs, as well as potential downstream impacts to other activities related to general GAAP and STAT reserving such as experience analysis, model system conversion, financial planning and analysis, and statutory margin analysis. Attendees should be able to connect accounting and financial reporting concepts under GAAP LDTI to opportunities for technology modernization, improved transparency of financial analytics, and important considerations unique to Accident and Health products.

Description: The time has arrived for ASU 2018-12, otherwise known as Long-Duration Targeted Improvements (LDTI). For health insurance products, the ASU will require considerable efforts to make the necessary changes to valuation systems, procedures, assumptions, and/or methodologies in order to be in compliance. This session is designed to cover LDTI implications specific to carriers of long-term care and supplemental health products, including changes to how attained age rated products will be approached, allocating and discounting the claim liability (IBNR), and how the treatment of Profits Followed by Losses (PFBL) will evolve under LDTI. By attending this session, you will be able to: • Understand LDTI’s key impacts on long-duration health insurance products. • Recognize the treatment of attained age rated products under LDTI. • Comprehend the requirements of allocating the claim liability to cohorts. • Grasp the requirements pertaining to the discounting of the claim liability. • Understand how PFBL will evolve under the new standard.

Description: This report provides a resource guide for asset-liability management practitioners, including methodology reviews, a survey of ALM practices, and guidance for applying ALM questions to real-world situations.