Description: The speakers will share the international experience in the annuity market, discuss the annuity puzzle and the influencing factors of annuity supply and demand, and express their views on the annuity market trend under the aging and longevity trend of China's population and longevity risk management of annuity products.

Description: Machine learning models have many practical applications in the world of insurance. These applications are well documented and most actuaries, at least at a high level, are familiar with these concepts. The presenters will take this one step further and provide an introduction to the theoretical formulas and real-world considerations that make machine learning models possible.

Description: The presenters will provide insights into fraud and abuse risk in the health insurance industry, including an examination of traditional and predictive modeling techniques for both prevention and detection. Emphasis will be placed on current and emerging risks. Canadian examples and perspectives will be offered, but the applications for attendees to use in practice are non-nation specific.

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: Table of Contents for the 1999 annual meeting of the Society of Actuaries, held in San Francisco, October 1999

Description: Many quantities arising in non-life insurance depend on claim severity distributions, which are usually modeled assuming a parametric form. Obtaining good estimates of the quantities, therefore, reduces to having good estimates of the model parameters. However, the notion of 'good estimate' depends on the problem at hand. For example, the maximum likelihood estimators [MLE] are efficient, but they generally lack robustness. Since outliers are common in insurance loss data, it is therefore important to have a method that allows one to balance between efficiency and robustness. This presentation suggests a general estimation method that we call the method of trimmed moments [MTM]. This method is appropriate for various model fitting situations including those for which a close fit in one or both tails of the distribution is not required. The MTM estimators can achieve various degrees of robustness, and they also allow the decision maker to easily see the actions of the estimators on the data, which makes them particularly appealing.

Description: This article addresses the difficulties of risk modeling in a poor economic environment. The problems include imperfect vendors of data, facing events that have never occurred before, unpredictable consumer behavior, conflicting data sources, and AAA credit doesn't always mean AAA.

Description: Changes introduced by various Medicare Alternative Payment Models and the Medicare Access and CHIP Reauthorization Act (MACRA) have accelerated an existing market trend of shifting some of what has traditionally been considered “insurance risk” to providers. The Provider Risk Sharing Boot Camp will give attendees deeper insights into methods, current thinking, and alternative solutions that actuaries can use to help providers understand and manage the financial and actuarial risks that they are taking, many for the first time.

Description: In this session, we will discuss the advantages of using stochastic simulation of decrements in actuarial projections. We will also discuss practical approaches for parameterizing assumption probability distributions for simulation using predictive models as well as challenges the practitioner may face in implementation.

Description: Neural Networks are no longer limited to classification and regression solutions to numeric problems. CNNs add the ability to classify images, and GANs can create new images. Learn how these capabilities can provide competitive advantages to early adopters, how they work, and how to implement them.