Description: in the insurance industry, machine learning models are increasingly used to make decisions that affect people's lives. In order to ensure that models are fair, they should be audited for bias. There are no hard and fast rules for bias audit; metrics that can be used to evaluate fairness vary depending on the context in which the model is deployed. The presenters in this session present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process.

Description: As the new VM-22 framework reshapes reserving for non-variable annuities, actuaries face both opportunities and challenges. Our latest insight and highlights what you need to know about assumption setting, model validation and asset optimization ahead of 2026.