Description: Machine learning algorithms fit models based on patterns identified in data and can be very complex. In this report, we describe and illustrate a range of methods for interpreting machine learning models from the growing field of Interpretable Machine Learning (IML).

Description: This primer paper seeks to introduce and update the actuarial profession to the topic of insurance policies and genetics and study the impacts on insurance and potential responses.

Description: An overview of how the actuary's role will evolve as technology improves.

Description: The future holds many changes. We've already dealt with major changes over the last few decades. This article goes through tips to embrace change effectively.

Description: This article addresses the risks associated with poor data quality and the importance of establishing effective data governance.

Description: This article discusses the emergence of multi-processor computers and the importance of acquiring parallel computing skills.

Description: Discusses the benefits of using the Web-based version of the newsletter instead of the paper version.

Description: Discusses useful resources for actuaries available on the Web.

Description: Discusses how to use the Dependent Multivariate Fractional Brownian Motion, or DMFBM, function in the computer language R. The function can be used for, among other things, calculating quantities such as reserve requirements for a block of variable annuities with living benefit riders.

Description: Opinion piece about when, how, and why actuaries need approximations.