Description: Discussion of how to apply FinTech and IT processes and technologies to building an in house actuarial models.

Description: Commentary on practical observations resulting from constructing and running Economic Capital models. Possible standardization of assumptions and result interpretation are morphing even though use of E.C. models has now been around for years.

Description: Presentation from SOA Annual Meeting.

Description: This article walks through the history of model efficiency in the U.S. life insurance industry, examines its current application, and looks ahead to what we might expect to see in the future.

Description: Comparison of open code and closed code actuarial modeling systems, focusing on advantages and disadvantages of each group.

Description: This article demonstrates how to implement DevOps for data using Moody's Axis. This was described as Model as Data (MAD). It described how extract turn-around documents from the model so they could be loaded back into the model. This feature allows continuous integration, continuous deployment and continuous testing of the model, which are important concepts in DevOps. This will allow the actuaries to speed up the model development cycle.

Description: In this, the second step, Sibson engaged the vendor and actuarial communities to solicit input on the actuarial model for determining the impact of various environmental, lifestyle and behavioral risk factors. The goal was to understand the extent to which actuaries currently use models around wellness, refine the above conceptual model, and better understand the potential uses of, and value to, the actuarial and vendor communities that would use such a model. We sought to understand the potential applications and sources of data for the actuarial model. We gathered input on how the model would best suit the professional needs and help shape the future role of the actuary. Lastly, we gathered feedback on the above conceptual model.

Description: This paper demonstrates an approach of modeling experience, and then interpreting the model and residual errors.