Description: Since the issuance of IFRS 17 in 2017, the global insurance accounting standard, insurers across the globe have been making huge investment in assessing the impact and implementing the new standard. In late 2018, FASB issued ASU 2018-12 that revises key elements of the U.S. GAAP measurement models and disclosure requirements for long-duration contracts issued by insurers (also called FASB Targeted Improvements). Both standards are becoming effective 1/1/2021 (though IFRS 17 may potentially be deferred by a year). U.S. insurers that have international parents or subsidiaries, and report under a multi-GAAP environment will likely have to adopt both standards. By now most insurers should have nearly completed impact assessment for IFRS 17, designed road map for or have started the implementation journey. With the new U.S. GAAP requirements becoming effective concurrently, it is important to embark on the implementation journey with both standards in mind, and maximize the synergy to achieve cost-saving and design efficiency. This session will briefly introduce the two standards with a conceptual comparison, along with a discussion on where efficiency and synergy can be achieved in a concurrent implementation.

Description: The introduction and effective application of US GAAP LDTI have increased the scrutiny of those reviewing the updated financial results. That is because LDTI has resulted in significant changes in actuarial reserving and deferred acquisition cost (DAC) models for long-duration contracts, including significant changes to data, assumptions and required disclosures. In light of the changes, greater reliance has been put on companies’ model risk management (MRM) teams and model validations to assess the conceptual soundness and accuracy of their companies’ actuarial models and to assess that the underlying data and process utilized are suitable and appropriate. This article covers a brief background on model validation, walk through a sample LDTI-focused model validation framework/test plan, then provide insights on common LDTI-related validation observations based on the authors' prior validation experience.