Description: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.

Description: An overview of the Predictive Analytics World (PAW) conference

Description: This is the second part of a 2 part series on Real options analysis (ROA). This article looks into the limitations of using financial option theory when doing real options analysis with projects that are long-dated and uncertain, such as: 1. First-degree homogeneity, 2. Uncertainty must derive from Brownian motion, and 3. Risk neutrality.

Description: This article is part of a 2 part series on real options analysis (ROA). Before you can understand the limitations of ROA, you must first understand the difference between risk and uncertainty. This article goes into detail explaining the vast differences between the two phenomena.

Description: This article describes how to model variable annuities using a GPU along with some of the stumbling blocks.

Description: This article explains how to create more maintainable models using abstractions by relating it to option, machine learning and machine intelligence models.

Description: This article is a basic introduction to information theory with the goal of explaining the maximal information coefficient.

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: This article describes how different types of regression models used for fund modeling can affect the variable annuity's risk exposure report and attribution report.

Description: The article is about how current advancements in AI can both help and potentially replace actuaries. We focus on three types of AI as defined by Numenta.org: Classic AI, Machine Learning, and Machine Intelligence. We present how Machine Learning and Machine Intelligence can affect actuarial analysts and actuaries alike. We then show how Machine Intelligence can be used to help regulators possibly construct a system or model that dynamically learns the behavior of insurance companies to predict failing insurance companies.