Description: The SOA Research Institute Aging and Retirement Strategic Research Program and the Long-Term Care Insurance Section are pleased to make available a research report analyzing the need for informal caregiving and its impacts on employees, employers, and other stakeholders.

Description: ASM-SOA Joint Seminar

Description: The CIA, CAS Institute (iCAS) and the Society of Actuaries (SOA) are joining forces once again to launch the Predictive Analytics Hack-A-Thon!

Description: The SOA is seeking actuaries to write questions for the Enterprise Risk Management (ERM) Exam that is offered twice each year.

Description: SOA members, as well as other business professionals, are welcome to join one or more sections/community.

Description: Comprehensive actuarial guides.

Description: Enterprise Risk Management (ERM) provides practitioners all of the tools necessary for assessing, quantifying, and managing risk on an aggregated, holistic basis. However, in practice it is often found that there is a tendency for company managements and even seasoned risk practitioners at times to navigate their risk exposures one at a time. This habitual approach produces sub-optimal results due to the oversimplification of risk events and understating the complexity of the world in which decision makers must navigate. At the heart of this sub-optimal behavior is the human predilection towards mental accounting. As with financial accounting, mental accounting employs a ledger function that mentally registers debts and balances. However, unlike a traditional ledger, the mental ledger is not precise, does not always sum debits to equal credits, and creates arbitrary categories for resources that are treated as independent, unrelated pools. These differences in function profoundly impact financial judgments and decision-making. Learn more about the long history of mental accounting, including examples from daily life to illustrate the principles involved. Discuss how mental accounting can arise during the ERM process cycle, with a special emphasis on its impacts on effectively quantifying risk and how this results in sub-optimal decision making. Discover behavioral tools specifically designed to mitigate the risk of mental accounting creeping into a company's ERM activities.

Description: Over the last several years, the life and annuity industry has seen significant reforms to the Valuation Manual and RBC Instructions, but one outstanding item not yet addressed with recent 'VA Reform' was the economic scenario generator (ESG). The NAIC has engaged Conning, Inc. to replace the Academy's generator (AIRG) that is currently benchmarked for VM-20 and VM-21 calculations. Learn about a broad overview of the current statutory ESG reform, and possible considerations companies need to account for in the reserve and capital calculations dependent upon it. You'll explore a brief regulatory history of the ESG efforts, as well as key mechanical differences between the proposed Conning 'GEMS' model and the AIRG. We will also examine key statistical differences between the model outputs, potential implications for company risk management, and pending regulatory changes to consider before official implementation of the new ESG. With industry field testing recently completed, review of this new scenario set is still ongoing. This initiative is extremely important to the industry because it could ultimately have significant implications on companies' regulatory requirements and risk management strategies.

Description: Models are meant to represent reality, and they often do so quite well. However, they cannot make decisions that contradict the modeling algorithm or underlying assumptions and data. Neither do they possess emotional intelligence or the ability to adjust for social equality. As complexity of models evolved to predictive analytics and machine learning, reliance on model results has increased as well. Over the last decade, as actuaries, modelers and data scientists looked closer into their models, they often identified certain social biases in model assumptions and algorithms. From the use of indicators like credit scores to geo-social segmentation, insurance models have often skewed results in favor of certain socio-economic groups. Underlying this segmentation, one can identify bias around access to health care and education, socioeconomic groupings and financial literacy.

Description: Predictive analytics projects in the life insurance industry are oftentimes set up as research ideas leading to intangible or unusable results for the practicing actuary. What if you could change that? What if the research turned into development and practical use through leveraging valuable resources properly? Learn how to structure predictive analytics projects, and about the project team, so there will be actionable takeaways for the business unit. Specific areas of value for you include underwriting, experience studies, assumption-setting, and product development. Watch as presenters talk through real case studies on turning a predictive analytics project into workable solutions rather than a resource drain, an unclear goal, or a misunderstood business need, which are some of the pitfalls of a poorly planned project.