Description: In this article, we are going to discuss the AR model by making connections to time-dependent ordinary differential equations.

Description: Reflection on three years on the PAF section council and where we're headed next.

Description: Data analytics is changing insurance industry and will fundamentally change how we run insurance business. Actuaries could and should be on top of it and lead the change. This practical session would provide a technical overview about GLM and describe how GLM can be applied as a business tool in life insurance. The session would also focus on case-studies across different functional areas like pricing, fraud detection, underwriting and product development.

Description: A discussion on using the Cox Proportional Hazards Model in developing a predictive underwriting model that produces a mortality multiplier for each individual.

Description: We compare two predictive analytics methods - GLM and GBM in terms of predictiveness vs. interpretability.

Description: This session will cover clustering techniques generally (k-means, hierarchical), and how k-means have been used to segment policyholders using third party features such as credit scores to assess customer profitability.

Description: Possible usage of tools that can automate time consuming processes, and lead to a different team structure and organization for predictive analytics team.

Description: Why Actuaries Should Welcome Automation

Description: This article gives a brief overview of ways to extend clustering analysis beyond basic methods such a k-means

Description: Feature article providing a brief introduction to Cython.