Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

Description: Python is the leading programming language for data science. Python programming is one skill that actuaries should acquire. It facilitates analytic work, and the available visualization tools help to communicate ideas. In this article, I use a traditional actuarial problem, mortality modeling, as an example.

Description: Feature article discussing the usefulness of Lorenz and Lift curves.

Description: Read the April 2019 issue of Predictive Analytics & Futurism published by the Predictive Analytics & Futurism Section of the Society of Actuaries.

Description: The presenters will explore policyholder experience data for long-term products, risks of over-fitting data in developing behavioral models, and ways to mitigate these risks and develop a sustainable and coherent framework to translate complex policyholder behavior experience data to behavioral models using company- and industry-level data. At the conclusion of the session, attendees will be able to: - Identify key drivers of policyholder behavior for long-term product - Understand the risks of over-fitting data and a false sense of precision in developing behavioral mode - Understand techniques that can be used to mitigate these risk

Description: Predictive models can improve the traditional actuarial assumption-setting process. Data science techniques can provide deeper insights into experience through their ability to identify key drivers, rank importance and account for interactions. The presenters will cover ways in which companies can utilize predictive analytics to set assumptions, including the various methods available and practical approaches for implementation. They will also discuss the advantages and challenges of using predictive models for setting assumptions for life, annuity and disability products.

Description: To expand our understanding of the theory of behavioral economics and its application to life and health insurance policyholder and annuitant behavior, the Society of Actuaries' Committee on Knowledge Extension Research, Committee on Life Insurance Research and the Financial Reporting Section issued a call for papers, inviting actuaries, academics, economists, psychologists, sociologists, researchers and other professionals to explore this topic from a variety of perspectives.

Description: This dissertation by Tia Sawhney will help you understand this issue and start measuring it, or start promoting equitable public policy.

Description: Important tools used in property & casualty P&C insurance—predictive modeling and data mining—can be applied to more effectively model policyholder behavior risks in variable annuity VA contracts. In this article, a case study is used to demonstrate how a predictive modeling approach can improve upon traditional methods used to model VA lapse behavior.