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: The purpose of this report is to demonstrate how artificial intelligence applications have grown since their infancy and invite the reader to consider the vast data at our disposal for the future.

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

Description: Article about Prompt Engineering Tips.

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: "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.