Description: This project obtains ensemble simulations of surface atmospheric temperature over North America.

Description: This report proposes a methodology to isolate crop yield improvement due to technological change and favorable and unfavorable weather.

Description: The Modeling Section, Predictive Analytics and Futurism Section, Committee on Life Insurance Research, Product Development Section and Reinsurance Section releases a report that can help to educate actuaries on how best to implement predictive modeling into relevant areas of actuarial practice. This report includes a review of existing literature and current industry practices, as well as a comprehensive set of considerations for predictive modeling in insurance applications.

Description: The objective of this paper is to provide a practical guide with concrete case studies to help actuaries implement artificial intelligence and machine learning (AIML) to help accelerate the speed of analysis for life and annuity actuarial modeling. We do so by researching the existing literature regarding AIML and its applicability to stochastic modeling and exploring case studies.

Description: The Modeling Section, Predictive Analytics and Futurism Section, Committee on Life Insurance Research, Product Development Section and Reinsurance Section releases a report that can help to educate actuaries on how best to implement predictive modeling into relevant areas of actuarial practice. This report includes a review of existing literature and current industry practices, as well as a comprehensive set of considerations for predictive modeling in insurance applications.

Description: This project obtains ensemble simulations of surface atmospheric temperature over North America.

Description: This report provides actuaries and other professionals working with risk, data and analytics or program evaluation in the health care space with considerations and frameworks related to the use of determinants of health (DOH) in data and actuarial work streams. Such frameworks can leverage new data sources to enrich health care data analysis to move beyond traditional data elements, such as age, gender, zip code or health diagnosis.