Description: At the end of this session attendees will understand how their company's existing cash flow models can be leveraged to meet the new requirements for principle-based reserves under VM-22.

Description: The upcoming VM-22 standard promises significant changes for the non-VA life insurance industry, impacting regulatory compliance, risk management, and financial reporting. This presentation equips insurance companies to navigate this crucial transition. We'll delve into key considerations from capital, modeling, and hedging perspectives, providing actionable insights for optimizing your VM-22 implementation strategy.

Description: The session begins with a brief introduction to Gen AI, including “what it is” and how to optimize outputs.Then, we will to provide some insight into what we are seeing within the Actuarial space – this would include the extent to which actuaries are using Gen AI on a day-to-day basis, how they are going about using it, and some predictions as to where this will go. Finally, we will dive into specific actuarial use cases – we have a few bespoke built actuarial demonstrations to show and will supplement this with a discussion on the broader spectrum of actuarial use cases. We plan for this to be the bulk of the session.

Description: Article discussing how geospatial imagery and high-resolution image technology are transforming property insurance.

Description: This session is a primer on how to apply Gestalt psychology to display quantitative information to improve neural receptiveness. While much of brain functioning is still a mystery to psychologists and neuroscientists, we do understand a great deal about which parts of the brain engage or disengage given visual cues. Actuaries are often tasked with presenting very technical information and are prone to focus more on the substance of numerical data rather than a pictorial presentation based on neural preferences. This presentation will discuss how to display quantitative information more effectively tailored to these preferences.

Description: An overview of guaranteed lifetime withdrawal benefits (GLWB) on registered index-linkedannuity (RILA) products and a stochastic analysis of market risks.

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: "Explore a general introduction to the opportunity of applying a relatively newer area in machine learning called uplift modeling, which has been successfully used in retail (Wayfair etc). The concepts are parallel to the intervention programs in health care, where we are interested in measuring the treatment effects and ultimately directing the right members to the most effective programs, driving the highest value for the programs both from a financial and health outcomes perspective. Uplift modeling can also be viewed in context of causal modeling where we are interested in assigning cause to a treatment relative to a member not receiving the treatment. Check out an introduction on the concept, seehow it relates and builds on existing work, and use a demo to showcase the potential of integrating this ML approach to current work."

Description: Market consistent valuations require path-dependent discounting for scenarios. IFRS 17's requirement to discount best-estimate cashflows using locked-in rates could give non-sensical results in many instances. This article describes a technique for calibrating each of the scenario's cashflows so that they can be discounted with other discount factors or curves without distorting the value of underlying options and guarantees.

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.