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: Biases can creep up in models in numerous ways. After discussing various forms of bias, presenting a framework useful to manage bias in models, and contextualizing this effort in the current environment, this session will focus on practical issues faced by the industry.

Description: James Dunseth's MBA journey at Yale’s School of Management and his startup internship taught him invaluable entrepreneurial lessons. Key takeaways include the importance of customer feedback, flexibility, technical skills, effective metrics, and achieving product-market fit. These insights emphasize customer-centric approaches, agile methodologies, technical understanding, and strategic metric use to guide business decisions and foster innovation.

Description: Over the last two years, we experimented with online peer assessment platforms in undergraduate courses and saw them deliver major learning advantages and be highly popular with students. We explain why peer assessment shows great promise for actuarial science education and the main factors to consider when designing peer assessment activities. We also detail our use of online platforms in two undergraduate courses. By sharing our experience, we hope to provide you, our fellow educators, with a solid starting point for experimenting with this format.

Description: This session is an in-depth interview of two actuaries turned Artificial Intelligence (AI) experts. We aim to have a conversation that gleans into each of their journeys and what led them to the path they are now on. We will open up the last ten minutes for audience questions. At the conclusion of this session, attendees will know Eric and Joe much better, have an understanding of what AI/machine learning/data scientist work is like for actuaries, and, for those who are curious to get more involved with AI, some encouragement to pursue their interests.

Description: Feature article discussing image recognition and how it may affect actuaries in the future.

Description: In this article, Extraction, Transformation and Loading tools are discussed and information on what makes a tool successful.

Description: an article to offer readers the background of the early career award and its benefits to the actuarial profession and academic community

Description: As AI becomes more pervasive, we must consider the potential repercussions and risks associated with its widespread use. The SOA seeks essays that delve into the multifaceted risks posed by (AI) over both the short and long term.

Description: This study would assess the impact of recent patient care innovation(s) on health care and the health insurance industry.