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: Zero-Knowledge Proofs (ZKPs) represent an innovative suite of cryptographic algorithms that validate the accuracy of sensitive information without revealing the actual information itself. This report introduces the diverse applications and emerging opportunities that ZKPs offer to the insurance community, especially in the health insurance space.

Description: This study would assess the impact of recent patient care innovation(s) on health care and the health insurance industry.

Description: This literature review provides insights on emerging technologies that interact with the actuarial profession within the space of microinsurance for the purpose of establishing a frame of reference for actuaries to use in their work. Furthermore, it intends to spark readers’ interest in, and create a vision for, how microinsurance and its associated technologies may impact the actuarial industry in the future.

Description: This literature review provides insights on emerging technologies that interact with the actuarial profession within the space of microinsurance for the purpose of establishing a frame of reference for actuaries to use in their work. Furthermore, it intends to spark readers’ interest in, and create a vision for, how microinsurance and its associated technologies may impact the actuarial industry in the future.

Description: In this report, we will try to provide an in-depth review of current Machine Learning and Deep Learning models and will explain how those models works and their possible applications in the actuarial field. The recent studies and successes from the industry will also be discussed. We intend to bring more actuaries onboard about those technologies and start to think about how to bring them into our daily work.

Description: The quality of data is paramount to the success of today's data-driven enterprises. The insurance industry is no exception. Insurers use a variety of resources and techniques to validate data. From the Record of the Society of Actuaries, Volume 27, No. 1, Session 66IF of the Dallas Spring Meeting.

Description: The popular press plays an important role in public perception regarding automated vehicles. In contrast to the excitement generated two to three years ago, we are now in a period of “reverse hype” with most articles focused on the slow pace of development and concerns regarding possible negative impacts on traffic congestion and growth of suburbs. To help describe current trends and highlight differences between the cars we see on the street today and driverless vehicles, this report relies on three market-related definitions; safe, self-driving and driverless.

Description: This paper explores how molecular biology, specifically epigenetics, may impact the life insurance industry, and considers the effects of utilizing epigenetic biomarkers for underwriting.

Description: The Actuarial Innovation & Technology Program Steering Committee (AITPSC) is seeking researchers to produce a report that discusses the life cycle of innovation and how to keep up with the increasing pace of change in our field. Research format should include interviews and case studies from across the actuarial profession.