Description: The discount rate is one of the most critical topics in IFRS 17 and may have important repercussions on product profitability. The presenters will cover practical implementation considerations, including alternative approaches to illiquidity premium calculation for business supported by fixed assets with credit risks. Other implementation considerations will also be covered, including granularity of setting the discount rate, application to new business, and implications on asset and liability modeling. The presenters will then discuss how discount rates could be utilized in an Economic Scenario Generator (ESG) to assist IFRS17 calculations, particularly for risk-neutral valuation of the time value of options and guarantees.

Description: Key performance indicators (KPIs) are essential in order to monitor and communicate a life insurance companies’ financial results, and effectively manage the business. This presentation will illustrate how the currently used KPIs may develop and evolve with the adoption and implementation of IFRS 17. Will the IFRS 17 insurance revenue be an appropriate measure for growth? Will the profitability and new business value measures be different? The panelists will discuss the implications of IFRS 17 on the existing KPIs and the possible changes that may lay ahead

Description: The discount rate is one of the most critical topics in IFRS 17, which may have important repercussion on product profitability. Having a low discount rate may indicate a low profitability of a group of contracts, while a realistic expectation may differ. A similar issue was prevalent especially for U.S. companies when discount rates were set using a market consistent framework. The session is intended to help develop a discount rate framework that aligns with the business practices and address the concerns around the market consistent framework. This session will cover practical implementation considerations to set and apply IFRS 17 discount rates. The panelists will discuss alternative approaches to allow for a level of illiquidity premium for business supported by fixed assets with credit risks. Discussions will also cover other implementation considerations including granularity of setting the discount rate, application to new business, implications on asset and liability modeling, and a planning aspect for a successful implementation of the discount rate setting process. The panelists will share case studies to illustrate different approaches of setting the discount rate and their potential financial impacts. At the conclusion of the session, attendees will be able to: understand key requirements of setting discount rates under IFRS 17; understand how to align the discount rate to the actual business practices; understand implications of the discount rate in actuarial modeling; understand potential impacts on financial results; and develop a realistic plan to set discount rates appropriate to the business practices.

Description: This session will look into life insurers’ anticipated business impacts as a result of the implementation of the new accounting standard for insurance contracts, IFRS 17. What will be the financial impact as of day one as a result of transition? How will embedded value for common life insurance products change? How will costs of reinsurance be affected? Will products have to be re-designed and/or re-priced? IFRS 17 may have more ramifications into the way insurers are doing business than we have anticipated so far. While we are working on the implementation of this new standard, we should not lose sight of the business impacts that will follow. At the conclusion of the session, attendees will be able to: Understand key impacts of the transition to IFRS 17; See illustrative examples of the impact on embedded value; and Understand key impacts on product design and pricing Country Relevance: US and Canada

Description: Uncertainty of a predictive model is a fact of life that many insurers could be overlooking at their peril without a framework for assessing it. The article provides a framework to understand the prediction uncertainty, which can be critical for the insurance operations using predictive models. Coin toss example explains model uncertainty and randomness inherent in making predictions to the number of heads expected. A mortality analysis case study illustrates how the Bayesian methodology can be used to develop the prediction uncertainty. In particular, the article shows that the Bayesian framework is a way to combine historical data and actuarial judgment, and directly address the model uncertainty through its posterior distribution.