Description: The Life Meeting, is the result of combining the Life & Annuity Symposium (LAS) and the Valuation Actuarial Symposium (ValAct). The Life Meeting will offer sessions specific to pricing and valuation as well as sessions on emerging issues relevant to both groups. These common themes include InsurTech, diversity, accounting change and the current economic environment.

Description: A jurisdiction’s regulatory regime can make some insurance products capital intensive to write. This can increase incentives for insurers to enter into risk transfer arrangements, such as reinsurance, to support their business and growth objectives.

Description: This year’s Life Meeting was unable to take place in person as planned in New York City. We are looking forward to the Life Meeting returning to New York City in 2024!

Description: Join us for the It Takes a Village: Insurance Product Development in the Digital webcast. Focuses will be directed toward a variety of disciplines within the actuarial science industry. We will also take a deep dive into UW, Technology Product and Marketing and Distribution. Don't miss the opportunity to bring digital insurance products to life and discover what the process of product evolution.

Description: Learn how modernization of data, models, and technology are influencing workflows. This webcast will address how workflows are changing in response to new regulatory requirements and the impacts the changes have on modeling and end-to-end future state operating models. Industry leading actuaries will discuss: How team collaboration can increase quality and speed of reporting to internal and external stakeholders How to strengthen controls and compliance frameworks across finance and actuarial departments How to modernize actuarial input/output for strategic analysis and insights How to attract and retain top talent by improving opportunities for actuaries There will also be dedicated time to ask questions of those who have made the transition.

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. In this session, we present a flexible framework that highlights key considerations in bias evaluation that can be adjusted to suit your process. In this session, we will learn about different metrics to assess fairness, discuss open source tools that can be used for bias audit, and walk through a case study.