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

Description: "Visual storytelling is frequently used to effectively convey complex actuarial concepts to our clients and other audiences. When talking about data visualization, we often focus on the explanatory aspect. But data visualization is also a crucial tool in developing insights from large and complex data sets at every stage of data analytics spectrum: • descriptive (what happened?), • diagnostic (why it happened?), • predictive (what will happen?), • and prescriptive (how do I influence what will happen?). In exploratory and modeling stages of data analytics’ project, we let the data tell the story or explore our intuition and hypothesis through data pictures. We demonstrate the power of visual display in healthcare data analytics through vivid examples from the healthcare case studies. These illustrations of data visualization exploratory power range from assessing the quality of data or examining outliers, to exploring variables’ relationships and trends, to hypothesis testing, to guiding in feature selection and models’ comparison and evaluation, to model validation. Throughout these illustrations, we will discuss principles of graphical effectiveness, excellence and integrity."

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: Presentation from SOA Annual Meeting.

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.