Description: Actuaries are leveraging predictive modeling and machine learning more and more as they strive to provide greater value to their stakeholders. Actuarial standards of practice provide guidance around modeling and data. The world has changed significantly over the last few years, so it is important these models be tested for fairness as well as any statistical biases. This article addresses some of the key issues actuaries need to be aware of as Corporations and society work through the responsible us of this technology and underlying data.

Description: Overview about the information covered in the Predictive Analytics Certificate and how that is useful for actuaries.

Description: This article helps actuaries who may have excellent business ideas to take the first step of starting a business

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.

Description: Increasing technology is available to create and disseminate educational resources worldwide. In response, this article advocates creation of open educational resources (OERs) that will benefit the actuarial profession with both recruitment and retention of members. Specifically, we describe OER contributions made at the University of Wisconsin-Madison and more recently through the ASTIN Academy, a working group of ASTIN, the non-life section of the International Actuarial Association. We advocate pushing boundaries and creating new ways of teaching effectively for the future. The innovative use of technology and open-source materials is a way to foster diversity, equity, and inclusion in the actuarial profession.

Description: While many know that machine learning is a big deal and even have a general idea of what it is, many do not know what machine learning can do for them. As an alternative to citing achievements or explaining algorithms, this article outlines the challenges that occur in statistics and the solutions that machine learning offers. Some of those challenges include correlated predictors, missing data, data interaction, and prediction intervals. Corresponding solutions include regularization technics, missing value imputation with KNN, tree based models, and cluster analysis for backtesting.

Description: A three-minute neuroscience-based image exercise is able to accurately unlock the emotional state of individual employees, permitting prediction of a host of specific needs. Actuaries can use this quantitative information at scale to target resources, benefits, and supports to increase the objective wellness and subjective well-being of workforces.

Description: An introductory article describing how Bayesian statistics has evolved over the past 20 years and why it offers the actuarial discipline powerful tools to better understand and manage risk. Compared with traditional (frequentist) and black-box (machine learning) approaches, Bayesian analysis provides explainable models focused on distributions of estimates instad of simply point estimates.