Description: This primer paper seeks to introduce and update the actuarial profession to the topic of insurance policies and genetics and study the impacts on insurance and potential responses.

Description: This paper offers a technical overview of the mechanics of Generative AI, with several actual examples throughout. It is intended to be a practical introduction to the concept, not a comprehensive view.

Description: In this report, we will try to provide an in-depth review of current Machine Learning and Deep Learning models and will explain how those models works and their possible applications in the actuarial field. The recent studies and successes from the industry will also be discussed. We intend to bring more actuaries onboard about those technologies and start to think about how to bring them into our daily work.

Description: This document is from the March 2025 edition of the SOA Research Institute AI Bulletin, focusing on artificial intelligence initiatives within the actuarial community. It includes articles on strategic principles, professional development products, NAIC updates, and tips for prompting the language model.

Description: The document is a collection of essays titled "The Impact of Artificial Intelligence on Retirement Professionals and Retirees" published in August 2024 by the Society of Actuaries Research Institute. It explores the implications of AI and large language models on retirement planning, providing insights and discussions on the topic.

Description: Zero-Knowledge Proofs (ZKPs) represent an innovative suite of cryptographic algorithms that validate the accuracy of sensitive information without revealing the actual information itself. This report introduces the diverse applications and emerging opportunities that ZKPs offer to the insurance community, especially in the health insurance space.

Description: Supporting mathematical basis for adjusting mortality risk by applying epigenetic age acceleration measures

Description: This document is a Request for Proposals (RFP) for research studies related to Model Governance/Ethics for GenAI Models in the context of the life insurance industry. It outlines the background, scope of work, proposal requirements, selection process, submission guidelines, and conditions for potential research projects.

Description: The Society of Actuaries (SOA) sought to investigate methodologies and characteristics associated with actuarial innovation, aiming to expand the industry’s capabilities for creativity. The SOA engaged Milliman to conduct in-depth interviews and focus groups with select actuaries who have demonstrated innovative skills, in order to derive key behaviors and characteristics that encourage innovation.

Description: This study explores the use of geospatial imagery and high-resolution image technology in the property and casualty (P&C) insurance industry, focusing on improving risk determination through accurate building location data. It introduces new methods like structure footprints and high-resolution elevation information to enhance underwriting, rating, and claims settlement. The study includes two case studies: one on flood risk in Hillsborough County, Florida, and another on wildfire risk in three California communities, demonstrating the significant impact of precise location and elevation data on risk assessment and insurance premiums.