Description: Join us for this session that will discuss various sources of risk and approaches to mitigating those risks in defined contribution (DC) solutions. Although additional topics may be added based on speaker availability, the key topics you will learn about will be:- Equity as a source of risk mitigation, which covers risks such as shortfall risk and inflation. - The role of LDI in DC plans and what it means in a DC context.- Approaches to inflation management in DC. If extra time remains, we may add a section on behavioral risks. Expect not to cover sources of legal risks to plans, but instead focus on investments.

Description: Due to competitive pressures, resource constraints, and the evolving availability of new evidence-based underwriting tools, many direct writing insurers are struggling with how to evaluate new data sources and incorporate them into their underwriting workflow to create an efficient accelerated underwriting program. Explore the results of a recent Individual Life AU Survey, where development was guided by a steering committee of direct writing companies. Learn more about trends that were identified through the study, including a focus on increased throughput, post-implementation monitoring, and minimization of mortality slippage.

Description: Join this panel discussion of approaches to post-COVID-19 mortality modeling presented by representatives from Social Security Administration, UK Insurance and Social Security, and academia.

Description: Learn what the Julia of the Julia-Python-R combination JuPyteR notebooks are named after! Julia is a programming language with several modern features to make actuaries' lives easier. It is highly capable for many tasks, whether you use it for modeling, data science, communicating results through interactive notebooks, or creating high-quality visualizations. It is a dynamic, interactive language that enables very fast computation while being as easy to learn and write as Python. Discover more about the programming language already used by the Federal Reserve, large banks, NASA, and many other industries. Julia Dive into an interactive walkthrough of the Julia ecosystem and how Julia is gaining adoption within the insurance space, while also using a range of open-source actuarial packages. You will explore how to elevate your analysis across different dimensions and distillg simpler ways to compose your actuarial models and analysis.

Description: LDTI requires that reinsurance recoverables be recognized in a manner consistent with the liabilities of underlying direct contracts. Consider implications of multiple ceded reinsurance contracts (e.g., coinsurance and YRT on the same business) and how this impacts the calculation of the net premium ratio (NPR) and other related approximations. Explore different options when only some of the policies within a cohort are reinsured. Multiple interpretations of the components of the NPR formula exist. Two main approaches will be presented-standalone and integrated approach. Other considerations to be discussed include implications of closed-block reinsurance transactions at transition and subsequent to transition, contra-DAC, market risk benefits. Finally, explore the short-duration YRT approximation (1/2 Cx) compared to the long-duration calculation.

Description: Explore real-life experiences implementing LDTI for traditional life and annuity products. Learn about strategies that worked well and pitfalls to avoid in the future. Topics include data and assumption management, building disclosures for attribution analyses and roll-forwards, discount rates, implementing new DAC calculations, reinsurance accounting methods, and risk-neutral ESGs and processes to calculate Market Risk Benefits.

Description: The Child Tax Credit and Social Security have demonstrated how simple rules based cash transfer programs can significantly lower poverty. Discover information from a paper that describes how the expansion of Social Security can more effectively and efficiently reduce poverty than the current alphabet of anti-poverty programs.

Description: The life insurance industry has a long tradition of trying to calculate the economic value with different forms of concepts. Especially with reinsurance, which is often a strategic part of a company's operation, it is important to understand the economics of the transactions given the complexities involved. However, despite the focus on economic value creation, the financial metrics demanded by various stakeholders often tie to one or more accounting basis.

Description: Artificial intelligence (AI) presents numerous opportunities in the insurance and financial industries. It also comes with heightened model, operational, legal and compliance, financial, and data risk. In this session, we will discuss speculation that emerging AI capabilities have sparked the fourth industrial revolution, examine what is different about this most recent round of capabilities, and examine why industry experts warn about the heightened risks the use of AI presents. We will review a framework - which includes AI ethical principles, standards, and best practices - to govern the use of AI. This will include a cross-reference of relevant Actuarial Standards of Practice as well as industry guidance like the NAIC Model Bulletin on the Use of AI Systems by Insurers. This framework will help actuaries and insurance professionals gain confidence that - if governed appropriately - we can reap the vast rewards of using AI systems in our industry.

Description: Behavioral Science is a powerful tool to enhance customer experience and improve outcomes across the life insurance value chain. This session will cover the foundations of behavioral concepts in a life insurance context and discuss practical areas that can benefit from behavioral science including customer engagement, disclosure, and retention. The session will leverage recent SOA research and specific experience in communication campaigns and call center best practices to translate theory to quantified results. Attendees will leave with an appreciation for potential challenges and practical steps to measure and demonstrate the value of implementing behavioral science.