Description: Data science, predictive analytics and AI are certainly the buzzwords these days. They are heavily used and developed in underwriting and marketing but for most actuaries, they might/may as well be science fiction. The presenters will demonstrate how a normal actuary can utilize data science techniques in their daily job function without forming an official data science team within a company. It can be done in a gradual fashion so that normal business is not disrupted and improvements are made over time. The usage of data science techniques will include demonstrations of the following areas: - Regression analysis - Economic scenario generator - Experience study - Data quality checks - Inforce compression for explicit hedging

Description: For actuaries interested in term life insurance, add this webcast to your calendar. Our panel of experts will discuss term life insurance from the following perspectives: Pricing Product development Reserving Risk management They will also discuss exploring data visualizations using Tableau.

Description: The SOA, with SCOR as the researcher, completed an experience study of post-level term (PLT) lapse and mortality. In the first part of this session, the presenters will cover the key findings of the traditional analysis regarding the shock lapse, the PLT lapse and mortality experience. The remainder of the session will focus on the results of the predictive modeling, which will provide further insights into policyholder behavior in the post-level term period.

Description: Data is at the core of the actuaries' role within companies, but how can actuaries translate data insights into informed and actionable decision making? The key is communicating data insights into a language that transcends analysis and enables real-time decisions that can provide an advantage to your management team. Learn an approach to choosing a tool or medium for your data insights, questions to ask yourself to better understand the communication necessary, and perspectives on how actuaries can take a larger role in actionable decisions within the industry. Understand availability, quality, and practical application of data sources health actuaries can access easily, how that data looks, approaches to transforming data into decisions, how it enriches administrative and claims data, and applications to pricing and reserving. Finally, discuss implications of ASOP 23, ASOP 41, and ASOP 56 as it relates to this topic. By attending this session, you will be able to: - Create an action plan on how to transform data into messaging for your leaders. - Use real experiences to formulate a strategy to expand the actuaries' role in informing decisions within the industry, including examples, to illustrate how data can benefit financials of health coverages. - Understand the application of ASOP 23 (Data), ASOP 41 (Communication), and ASOP 56 (Modeling) as it relates to turning data into decisions. - Be knowledgeable about new sources of morbidity for medical care and LTC and how to acquire and apply this information in actuarial models.

Description: Open source programming languages have become a common part of the actuarial toolkit over the years. Using tools like R and Python, highly technical actuaries can automate onerous processes, easily work with massive amounts of data, create models and generate reports. Although these tools excel at turbo-charging analysts, it can be difficult for benefits to inure to the rest of the organization given the learning curve (“Can’t you just put this in Excel?”), control risks (“So your entire process could break overnight if some random person in Nebraska changes something?”), and internal IT environments. Listen in on a panel discussion where presenters share their personal stories of working with open source in a production setting. Hear about real world business problems, how open source tools were used, what risks were present, how they were overcome and the final business result.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.