Description: The U.S. health care industry has gone through a dramatic change during the past several years. The business model has moved from a risk selection model to a risk management model. People can shop online for their health care coverage. Everyone is welcome! Annual and lifetime dollar limits, underwriting and the pre-existing conditions exclusion are all in the history book now. Facing the challenges of the post-reform era, many health insurance carriers began using big data analytics on all aspects of their decision-making processes. The goal is to be able to compete on analytics in this quickly changing environment. However, becoming an analytics competitor is not easy. It requires the belief from the leadership and a group of dedicated analytical professionals to solve emergent business problems that existing strategies cannot address in an evolving market. These problems include marketing for acquisition and retention, coding accuracy, revenue growth, health care disparity, payment integrity, quality of care, health care cost reduction and, ultimately, personalized care management. In this section, audience will learn a basic understanding of the role of predictive analytics in US Healthcare marketplace.

Description: The SOA's Committee on Finance Research is please to make available the results of this research project, jointly sponsored by the SOA and CAS, that explores economic series and provides and Excel based tool for generating future economic scenarios. This is an Excel Spreadsheet for Appendix C showing Simulated Financial Scenario Data of inflation rates and interest rates from this research project. Please see the SOA website for the full report and other related spreadsheets.

Description: Mandatory implementation of life principle-based reserves (PBR) is just around the corner, and there is no shortage of work to do, as most products have yet to be moved to PBR. Oliver Wyman recently completed its 2019 PBR survey, with more than 40 participants covering 85 percent of the individual life market, including 23 of the top 25 life writers and five reinsurers. This article will expand on the key survey findings including implementation status and model simplifications.

Description: The author discusses the two-part presentation on complexity science tools at the SOA Annual Meeting October 2010. He explains that presentations went back to basics with explanations about the difference between modeling tools used to employ deterministic methods for classical actuarial models.

Description: Review of a report by Alan Mills introducing Complexity Science, a discipline which could help actuaries to model and manage complex systems, such as the health care system.

Description: According to the Actuarial Standards Board’s Actuarial Standard of Practice No. 49 (ASOP 49), actuarially sound Medicaid managed care capitation rates will include a provision for underwriting gain in order to provide compensation for the risks assumed by an MCO. However, there is no generally accepted method for quantifying underwriting gain in managed Medicaid capitation rates. This article discusses a new model developed to address this void and calculate underwriting gain. The model was developed by researching existing financial literature and performing a statistical analysis of industry data to build out a framework that can be leveraged to estimate underwriting gain.

Description: This article describes the financial modelers’ manifesto. Focus is on how difficult it is to model financial instruments when there is a human element involved.

Description: This article discusses the reliance the banking industry placed on sophisticated mathematical models.

Description: This article discusses how complex models can provide a false sense of security, hiding the evidence that the entire range of indications may hinge on one or two key assumptions.

Description: The article discusses the investment models of The Mutual Pension Insurance Company Ilmarinen, one of the companies managing the statutory employment pension in Finland. The parts are: financial time series data management, time series analysis programs, investment simulation models and portfolio optimization models.