Description: Machine learning algorithms fit models based on patterns identified in data and can be very complex. In this report, we describe and illustrate a range of methods for interpreting machine learning models from the growing field of Interpretable Machine Learning (IML).

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: File for 2020 DATA VISUALIZATION CONTEST.

Description: This article explains the importance of centralizing modeling departments. It goes further and explains the importance of making all aspects of modeling reusable. It explains the consequences of decentralization.

Description: Read the July 2020 issue of Innovators & Entrepreneurs published by Entrepreneurial and Innovation Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

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: This article explores the evolving role of assumption management in the life insurance industry. The article highlights how behavioral, economic, and demographic assumptions are critical to pricing, forecasting, and explaining financial results, yet many insurers still struggle with fragmented practices and outdated processes. Drawing on survey insights from 15 U.S. insurers, the authors illustrate persistent challenges in governance, data integration, and automation. They argue for a more holistic, technology-enabled operating model centered on centralized assumption repositories. Such repositories provide a single source of truth, enable automated ingestion into models, support sensitivity testing, and enhance governance and transparency across actuarial, finance, and risk functions. The article offers guiding principles and design considerations for building scalable repositories that improve efficiency, consistency, and decision-making. Ultimately, it positions assumption management modernization not as a technical upgrade but as a strategic imperative to unlock clarity, agility, and competitive advantage in a complex market.

Description: The article is a chairperson’s update from Arthur da Silva on the Society of Actuaries (SOA) Emerging Topics Community, written to recap the past year’s accomplishments and outline potential priorities for the year ahead. Its intent is both informational while inviting community member participation and ideas for the year ahead. It also provides insight into the new AI subgroup and how it operates to help community members and other community leaders understand what it delivers and how to get involved.

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: A brief overview of recent AIT research papers in summarized format.