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: This report explores the adoption and impact of artificial intelligence and large language models in the Greater China insurance industry, drawing from surveys and case studies.

Description: The aim of this research is to survey emerging data analytics techniques and discuss their evolution and growing use in the actuarial profession. Data analytics’ applications in life and non-life insurance will also be provided.

Description: Winning essays form the Actuarial Practice Innovation Call for Essays.

Description: Winning essays form the Actuarial Practice Innovation Call for Essays.

Description: Electronic Health Records, or EHRs, are a major worldwide initiative to eliminate paper recordings – including Attending Physician Statements (APSs) – of an individual’s medical history and current treatment.

Description: Decentralized insurance alternatives is a rapidly expanding sector of Decentralized Finance (DeFi), an emerging and growing financial ecosystem powered by blockchain technologies. The purpose of this report is to review and discuss the business models and operating designs of decentralized insurance alternatives, which may have significant implications for traditional insurance businesses, as well as the actuarial profession.

Description: The COVID-19 pandemic initially caused some health care services to shift away from in-person transactions to a digital form, including telehealth, e-mails, remote patient monitoring and other forms of communication. If sustained, numerous effects and counter-effects both in the cost and quality of care as well as long-term outcomes for patient health may emerge.

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

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.