I will be a senior at Indiana University (IU) next year. I am double majoring in accounting and finance, with minors in economics and mathematics. I will also serve as the vice president of IU’s Actuarial Club next year, and I currently serve on the Kelley School of Business Equity and Inclusion Committee. The COVID-19 pandemic had a substantial impact on my summer plans, as I am sure it did for every single person around the world. What stood out about the entire situation was how quickly everything moved. There was seemingly new information every single day, and the majority of it wasn’t good. Everyone at IU went on spring break, and two days later we were notified that classes for the remainder of the semester would be conducted online. 

Last year I wrote about a concept called the Modeling Lifecycle (Modeling Lifecycle). In that blog, I spent time addressing the many steps that are necessary for a predictive modeling project to be a success. Obviously, one of those critical steps is actual implementation of the model itself. Without that, you only have a fancy formula that doesn’t do much of anything for you. While insurers continue to devote more and more time and resources to predictive analytics, it would also benefit them to make sure they are devoting sufficient attention to model implementation.

On the May 2020 Pinnacle APEX Webinar, “The Importance of Data Management and Data Quality,” we addressed a critical business issue:  the costs of poor data, reasons for data management, and ways to improve how data is managed in an organization. The webinar also took a deep dive into methods to address the major challenge of missing data.

In 2020, the Minority High School Scholars Analytics Academy, or MHSSAA, will mark its 25th year. The time is right once again to pause and honor this program and all that it represents to the insurance industry, the actuarial profession and the students it serves. Through the MHSSAA, participating students from around the country learn about how insurance works, the requirements of the actuarial profession and the role of analytical skills in solving real-world business problems.  

While predictive analytics can provide significant benefits to insurance companies and customers, the rapid pace at which analytics is evolving and the relative complexity of some of the models used poses a significant challenge to state regulators who are charged with reviewing and approving such models. The National Association of Insurance Commissioners (NAIC) recognized this emerging issue and created the Casualty Actuarial and Statistical Task Force (CASTF), which has been charged with identifying best practices to guide state insurance departments in their review of predictive models for underlying rating plans. Over the course of the last year, the CASTF has released multiple drafts of the white paper “Regulatory Review of Predictive Models” for public comment. And comment the public has! Numerous letters have been submitted from trade associations, actuarial organizations, credit agencies, consumer groups and even insurance departments to provide their input on the lengthy white paper.