Description: This paper is from the Transactions of Society of Actuaries 1956 Volume 8, Number 20. It was presented at the 1956 Spring Meetings of the Society of Actuaries for discussion. The author presents an outline of how difficulties in the determination of a frequency distribution of the expected costs may be overcome by, in part, applying the concept of using a series of random numbers with a view of making mortality studies for each life in a group of annuitants. The paper also presents the results of some experiments which were designed to discover whether or not the concept could be conducted on an IBM 650 electronic data processing machine.

Description: 2021 Life Meeting - Session 8D: Three Years After Model Conversion: How’s it Going? 9.1.21 Recording

Description: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

Description: Employers, health plans, stop-loss insurers and reinsurers are all affected by the high cost of treating cancer. In recent years, cutting-edge pharmaceuticals, bone marrow transplants and CAR-T cell therapies have driven very high cost trends for cancer, with more emerging therapies on the horizon. In this session, we will review these new treatments, showcase models that aid in understanding the range of likely financial effects of the current, expanded, and likely-to-be approved indications for cell therapies that will treat certain patients affected by lymphoma, leukemia, and multiple myeloma. This session will explain important considerations for modeling Medicare, Medicaid versus commercial populations, key risk concerns caused by the size of enrollee pools, and important differences between the various therapies that affect modeling. We will also discuss averted cost, health outcomes, pricing levels, and risks caused by provider billing practices.

Description: 2021 Life Meeting - Session 8B: Proxy Discrimination in Life Insurance 9.1.21 Recording

Description: In this paper the author proposes a proof for new formulas that derive exposure formulas of the valuation-schedule type on the basis of the Balducci assumption. In addition he provides a short cut for the coefficients of the terms involving deaths. Then he derives a general exposure formula of the valuation-schedule type that may be used for all mortality studies with an observation period covering an integral number of consecutive years. From ACTUARIAL RESEARCH CLEARING HOUSE, 1980 VOL. 2.

Description: This report provides actuaries and other finance professionals with an example of running the Medicaid Unwinding model with results at the national level on projected changes in both acuity and enrollment for Medicaid and Children’s Health Insurance Program.

Description: Historically different approaches have been used by actuaries in modeling mortality improvement. The SOA has recently released a framework that practitioners can use to project mortality improvement, providing methodological consistency across a range of applications.

Description: Regression to the mean (RTTM) is inflating your health care intervention evaluations if you are not carefully managing it's omnipresent and significant impact RTTM is the statistical phenomenon which arises when comparing sequential data points when an initial observation is extreme or an outlier, and states that with no other intervention, future observations will be closer to the average. Members who had high costs last year will, on average, have lower costs this year, even in the absence of any intervention or clinical program, simply by 'regressing' to the mean. This means that if your clinical programs are simply targeting members who had adverse medical experience last year, you’re targeting the wrong people. And if your savings methodologies are simply last year’s costs minus this year’s costs, the savings are inflated. By attending this session, you will be able to identify situations where RTTM will be a significant factor, how to eliminate it's impact when performing retrospective evaluations, how to identify prospects for clinical intervention with RTTM in mind, and more.

Description: David shares his thoughts on using Microsoft's High Performance Computing Server, or HPCS, to cluster computer resources, improve timely performance, and reduce actuarial modeling costs.