Description: ALM and hedging can reap significant benefits from innovation in computation and operations research. Explore applications of static and dynamic optimization in two different ALM and hedging use cases. Insurers are increasingly moving offshore by establishing Bermuda captive reinsurers. In calculating the Bermuda Best Estimate Liability ('BEL'), reinsurers will need to segment assets to support liabilities in each treaty. A Linear Programming ('LP') problem can be formulated to tactically select assets in a way that minimizes the BEL and consequently the total asset requirement. Learn more about what US Variable Annuity Insurers face with the regulatory requirement to reflect 'future hedging strategy' in their statutory funding calculation. The traditional numerical method of finding the optimal hedging target and then allocating assets may encounter the curse of dimensionality with expanding product and economic environment complexity. A Reinforcement Learning algorithm may well fit this problem and provide insurers a solution for dynamic hedging.

Description: The industry sees extreme value in predictive modeling and its ability to help us meet customer demands and remain profitable, but do customers and regulators look at this as a favorable addition to our process? Hear from a dynamic panel of industry experts who will tackle a variety of issues and questions from the state of the regulatory environment to the responsibilities of the carrier.

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: 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: The aim of this project is to present proposals for Variable Uninsured Life Annuities—or “Value annuities.” The report and the accompanying models illustrate how Value annuities work.

Description: Crop insurance plays an important role in mitigating food production disasters and keeping a robust food production system in the United States. Crop insurance increases stability in food and fiber production, smoothing variability which would otherwise occur because of weather patterns and changes in international trade regulations and markets. Stability increases the ability of farmers to make long-term investments that increase production efficiency and keep productive land in agriculture.

Description: Feature article discussing model monitoring

Description: Feature article discussing futurism and a study that contains in-depth descriptions of the Delphi and TIA methods; listings of the rationales and thought processes, the plausible future developments that could influence the values of these four economic variables and the resulting “fan of possibilities” for the values of these variables

Description: Read the August 2020 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

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: Read the August 2019 issue of Predictive Analytics and Futurism published by the Predictive Analytics and Futurism Section. Newsletter provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Career changes typically follow a major milestone—a promotion, a job offer, a new degree, maybe a cross-country move. For Patrick Getzen, exposure to some new perspectives ignited a shift.

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

Description: There is a growing amount of “off-balance sheet” talent in the US and Global workforce. This spans from consultants and contractors, to gig workers and the crowd (both curated crowd and open crowd). One such example is Uber and Lyft workers. Insurance companies and the Actuarial profession have started to experiment with platforms such as Kaggle and this will continue to grow and evolve. This article explores these new sources of talent for companies, the implications to the actuarial function, benefits and challenges of the evolving workforce.

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: Many life and health insurers are beginning to use Predictive Analytics (PA) for a variety of purposes. This article addresses the unique challenges and potential disruption PA brings to the risk manager, as well as the opportunities.

Description: The article explores the possibility of using the emerging technology for life insurers to build a monitor system for better risk and profitability management.

Description: An introduction to creating Shiny web applications, with sample code available to reproduce the interactive application displayed in screenshots in the article.

Description: It is about using actuarial judgement to validate the predictive model.

Description: In this article, we are going to discuss the AR model by making connections to time-dependent ordinary differential equations.

Description: This is the second article in a two-part series examining the potential value to companies of implementing a company profitability and risk dashboard.

Description: The SOA provides information about the actuarial career path in health care and predictive analytics. Throughout 2016, we will share more about actuaries who are tackling some of the industry's biggest challenges and hear from top employers in healthcare analytics.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

Description: The article focuses on the use of predictive analytics in the development of experience studies, pros and cons of using predictive analytics versus traditional techniques, and the implications for setting assumptions and improving profitability.

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.

Description: This is a list of links to research papers on predictive modeling.