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: Hardly a day goes by without a feature story about the latest reverberation from the expanding use of Artificial Intelligence/Large Language Models (tools) in a variety of settings. The pace of use (and misuse) of these tools, such as ChatGPT and Bard, has been explosive and shows no sign of abating. Many experts would argue we are on the cusp of a permanent paradigm shift in teaching/academics and overall application of technology. With these issues in mind, the Society of Actuaries (SOA) Research Institute’s Aging and Retirement program is interested in an exploration of this topic from a variety of perspectives. The result of this effort is intended to provide a useful resource for readers to become well-grounded on the issues surrounding the impact of these tools in a retirement context and set the stage for future research.

Description: The Society of Actuaries (SOA) Research Institute received a grant from the U.S. National Science Foundation (NSF) to educate insurance actuaries on the use of wildfire risk datasets and models to be developed by researchers from academia and the U.S. National Aeronautics and Space Administration (NASA).

Description: Artificial intelligence (“AI”) adoption in the insurance industry is increasing. One known risk as adoption of AI increases is the potential for unfair bias. Central to understanding where and how unfair bias may occur in AI systems is defining what unfair bias means and what constitutes fairness.

Description: Society of Actuaries (SOA) Research Institute’s Committee on Finance Research (CFR) and SOA Investment Section are interested in an exploration of the impact of Artificial Intelligence on investment professionals from a variety of perspectives. The result of this effort is intended to provide a useful resource for readers to become well-grounded on the issues surrounding the impact of these tools in an investment context and set the stage for future research.

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: Ever wanted to do a Kaggle data science competition, but didn't know where to start? This presentation meets the needs of actuaries interested in predictive analytics and competitive data science. The presentation will describe the nature of competitive data science, and provide insights into lessons learned by competing in the Airbus Ship Detection Challenge at Kaggle, as well as other Kaggle competitions. The presenters will provide an introduction to neural networks and describe the nature of the Airbus Ship Detection Challenge and other Kaggle competitions, plus a solution approaches that can be used to obtain top scores using Convolutional Neural networks (CNNs), random forests and boosted trees.

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

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

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