Description: Data is an essential component of financial analysis. In this session, the presenters will discuss data governance/management framework and how it can be applied to US GAAP LDTI and VM-51 regulations. This session will also discuss the important relationship between actuaries and IT and how they can work together towards best practices for extracting, managing and using data needed for various actuarial models. Real life case studies relevant for valuation actuaries (e.g. data needs for LDTI and VM-51) will also be presented and examined, highlighting best practices as well as challenges and pitfalls for setting up and governing data processes along with areas for where good communication and strong relationships are important to success.

Description: This paper improves Chapter 2 of 'Mathematics of Pension Funding' - Part 7 Study Notes. Key formulas are derived in a more general fashion than in the Study Notes.

Description: In year’s past, we presented the Practical Predictive Analytics Seminar (PPAS) as an add-on to the Life and Annuity Symposium or attached to the Predictive Analytics Seminar, this year it will take place as a stand-alone virtual Seminar.

Description: This article provides information on how to navigate the Internet's World Wide Web and tap into resources of interest to actuaries.

Description: The following case study walks through the steps of a predictive modeling exercise to illustrate one way analytics can streamline the underwriting paradigm.

Description: Presents a theoretical basis for a computerized methodology for estimating biological age - a measure of time to death.

Description: Read the October 2020 issue of Actuarial Technology Today published by the Technology Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: This session will cover assumption determination for annuities under VM-21 with emphasis on contract holder behavior assumptions, prudent estimate mortality assumptions and measuring credibility.

Description: Predictive modeling continues to gain traction in the life insurance industry with the rapid growth of available data and maturity of open source tools. New methods and technology in predictive analytics have enabled the evaluation of extremely large data more quickly and allow us to incorporate features previously not considered. The presenters will provide background on predictive modeling tools and algorithms utilized by actuaries and data scientists for life insurance, highlighting applications from marketing to underwriting.