Description: Exploring how artificial intelligence can enable insurance to expand into new places with new insights.

Description: Read this report to learn more about interpretable machine learning techniques for health insurance fraud detection.

Description: Regular update provided by the Chair of the Joint Risk Management Section on initiatives put forth by the section council for the current year.

Description: View the Avoiding Unfair Bias in Insurance Applications of AI Models

Description: View the Data Challenges in Building a Facial Recognition Model and How to Mitigate Them

Description: How the speed of change is accelerating and how we could react to this as a profession.

Description: A literature review report that looks at research on how artificial intelligence is used in actuarial work.

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.