Description: This document is a call for essays issued by the Society of Actuaries Research Institute. It seeks submissions that explore the impact of Artificial Intelligence/Large Language Models on retirement professionals and retirees. The document provides background information, research objectives, and a list of illustrative topics and questions that the essays can address. The submission guidelines, timeline, and evaluation criteria are also outlined.

Description: "The following video Dave Friesen provides a comprehensive overview of the Artificial Intelligence and Data Science learning track. It covers the role of AI in actuarial work, ethical considerations, and the challenges and limitations of AI. The document also discusses the importance of cloud ecosystems, AI tools, and automation trends, emphasizing the evolution of platforms like Python and low-code solutions. Additionally, it highlights the criticality of lifecycle management, governance, and data foundations in AI applications.

Description: This Q&A article discusses the use of machine learning to analyze unstructured data. Supriya Ramachandra, a data scientist at SIKKA, explains how her team uses natural language processing techniques, technical aspects of neural networks, LSTM modeling, and natural language processing. Machine learning allows for the classification of large amounts of unorganized data, providing actionable information for actuaries.

Description: Article about Prompt Engineering Tips.

Description: PartnerRe Life and Health is rapidly growing its presence in global markets. This growth means that our medical underwriting team has to handle 25 percent more cases every year. To manage these additional cases without increasing headcount at the same pace, the team has to operate more and more efficiently. In a market where response time is a key differentiator, we improve efficiency by using machine learning to triage and prioritize the underwriting team's case work. In this talk, we describe the model pipeline end to end. That is, we cover the entire implementation chain from accessing data to the eventual evaluation of the added value. In particular, we discuss often neglected topics such as model risk mitigation, technical deployment of the model and integration into business processes.

Description: We explore four plausible scenarios for how AI might reshape the future, focusing on employment as the main driver. At one extreme, AI boosts productivity, sparks entrepreneurship, and leads to near-full employment through human-AI collaboration and reformed education. At the other end of the spectrum AI-induced mass layoffs trigger an economic spiral, with soaring unemployment, creative stagnation, and societal resentment. The middle path presents a fragmented transition with a two-tier workforce, periodic recessions, and variable adaptation success. Lastly AI fizzles due to poor returns and rising costs, causing economic stagnation and investor losses without widespread disruption. Each scenario also includes potential impacts on the insurance industry, ranging from innovation and growth to regulatory strain and investment losses in the others. Proactive scenario planning, by preparing for multiple futures by identifying early warning signals, adapting plans, and strengthening institutional resilience is encouraged. The article closes with four additional speculative scenarios, including government-controlled AI, AI dominance, militarized AI, and mass surveillance. Ultimately, the piece argues that while AI’s future is uncertain, the choices made today will shape whether it becomes a partner, a threat, or a disappointment.

Description: Short Form Professional Development Videos - Explaining AI Black Box Part 2 Recording

Description: This newsletter provides a regulation outlook for the use of AI/ML in insurance industry, interpretations of regulatory expectations and what insurers should consider when managing risks associated with the development, implementation and use of AI/ML models/tools.

Description: Neural Networks are no longer limited to classification and regression solutions to numeric problems. CNNs add the ability to classify images, and GANs can create new images. Learn how these capabilities can provide competitive advantages to early adopters, how they work, and how to implement them.

Description: There is enormous interest by investors and business leaders in the potential for artificial intelligence technologies, often powered by predictive modeling and machine learning, to transform the way that companies do business.