Description: "Modern actuarial work, being quite data intensive, provides many opportunities for actuaries to use the statistical skills that are part of their basic training. Furthermore, modern computing allows us to analyze vast quantities of data intensively and each package will provide statistical answersto any questions the actuary might ask of it. However, in this particular field, it is often worth taking a step back, and ensuring that we recall and consider the warnings that come with each of the statistical procedures we might attempt to use. This will allow us to ensure that we guard ourselves against drawing inappropriate conclusions, which could come back to haunt us later. By attending the session, you will: - Avoid drawing inappropriate statistical conclusions, while doing their work. - Leave informed about the types of pitfalls that exist and where those might come into play in actuarial work."

Description: Analyze the market volatility behavior in context with historical extreme events during this webcast. There will be a focus on understanding volatility clustering and relationships, both contemporary and temporal, in addition to key economic and capital market variables. You'll also learn from a comprehensive attribution analysis, which uses economic data, asset data, retail investor data and event data as explanatory variables. Dive into investment-related Reddit comments used to capture features of retail investors and tree-based models, including Random Forests and Gradient Boosting Machines to justify changes in volatility modeling. Expect this webcast to end with an example of liability-driven investment optimization that illustrates the potential impact of increased volatility assumption, how to adopt stochastic volatility models, how to reflect volatility clustering and return jumps.

Description: The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: We're introducing a new “cutting edge” wealth accumulation predictive framework, the predictive equity analytics (PEA) for life products (annuity, insurance, group employer benefits, retirement plan). PEA enables life insurers and policyholders to avoid market downturns and capture returns from equities (stocks, ETFs and mutual funds) predicted to increase in next one to three trade days via fund prediction advisory by insurers, additionally by individual policyholders. The capability does not exist in insurance markets nor in financial intermediary markets.

Description: As the speed of technological innovation increases, actuarial leaders are trying to determine how to use emerging technologies to unlock more business value and improve competitive positioning. This includes enabling new capabilities, improving process efficiency, and increasing the effectiveness of human capital. The session will discuss what we are hearing from clients who are in the process of transforming their business, how they are approaching these transformations, and what they are focusing on. This will include different aspects of transformation, including visualization dashboards, data science applications, no-code platforms, automation, data, and the cloud. Find out how organizations are thinking of human capital and collaboration between actuaries, IT, and other professionals.

Description: The Individual Life Experience Committee has numerous research projects underway. Take a look at three current areas of research: the analysis of the interaction of lapse and mortality experience using data from mandatory submissions; a deep dive into old age mortality; and an update on the committee's work to establish a predictive analytics framework that will be open for all actuaries and data scientists to use. By attending the session, you will: - Better understand the research conducted by the Individual Life Experience Committee. - Better understand the link between lapse and mortality experience, appreciate some of the nuances of older age mortality, and gain knowledge about a predictive analytics framework that will be released in the future.

Description: Actuaries today have unique opportunities to analyze, understand and use data for strategic decision making. With nearly unlimited computing power and a broad spectrum of data sources at our fingertips, we are enabled to support our actuarial judgment with comprehensive findings backed up by deep data analyses.

Description: Electronic Health Records are here to stay. Take a deep dive into the world of electronic health records and how the industry is poised to utilize this new form of digital medical data to transform the way policies are underwritten and ultimately, transforming the client experience. Listen to industry experts speak about what they are doing to use this data most effectively and how this ultimately develops into ground-breaking solutions for the industry.

Description: Are Accelerated Underwriting (AU) programs performing as expected? As mortality experience will (hopefully) be slow to emerge, it is pivotal that we look at alternative methods of monitoring early performance. From random hold outs, to post-issue APS's, and post-issue prescription profiles, explore popular monitoring techniques and lessons learned so far. Focus on confusion matrices that provide an early indication of mortality slippage relative to fluid-based underwriting decisions. Finally, explore statistical methods to determine what constitutes credibility and identify opportunities to think about mortality slippage more robustly.