Description: Certain valuation assumptions are difficult to set with a high degree of accuracy, whether because the underlying experience is in different economic or interest rate environments, in addition to other reasons. Examples include assumptions for long-term care insurance, annuity options, lapsation and premium persistency on ULSG. This session will share how the actuary can use predictive analytics to augment the assumption-setting process. Some of the benefits to the actuary (along with the company and the regulator) include more accurate assumptions with lower margins for adverse deviations. Of possible interest as well is that applications go beyond valuation and include acquisitions, reinsurance and pricing.

Description: Mindful data visualizations show the end users the end way in how to analyze and interpret your data. More importantly, they can then use this data they see to drive decisions and modeling going forward.

Description: This is the third and final article in a series on the Power BI suite of tools available from Microsoft. A high level look at its analytical capabilities.

Description: Explores the open vs. closed systems debate with regards to actuarial modeling and exposes the over-simplification. Valuable for anyone who uses models.

Description: In this paper, we introduce a family of leptokurtic symmetric distributions represented by the difference of two gamma variates. Properties of this family are discussed. The Laplace, sums of Laplace and normal distributions all arise as special cases of this family. We propose a two-step method for fitting data to this family. We develop a statistical test for goodness-of-fit and introduce a test of normality of the data. A simulation study is provided to illustrate the theory.

Description: This article is about why actuaries should have integral roles within nearly every IT and business intelligence company serving the insurance industry.

Description: As commodity markets evolve in tandem with advents in communications technology, the criticality of data management becomes a lynchpin of corporate success or failure. Advanced analysis on market movement can lead to substantive market gains, while an inability to assess risk in a timely fashion can hinder corporate responsiveness, stagnate growth or result in unmitigated risk. In essence, the old paradigm of data flow and management was severely limited by 1 the amount of readily available data, 2 the transmission capabilities for data, and 3 the costs of data storage and maintenance. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: This article summarizes IT trends that impact life, health and P&C insurance providers as revealed by Gartner's research.

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: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.