After several years as an operations leader for GEICO in Virginia, Phil Offutt was considering an MBA. But when he learned about the M.P.S. in Insurance Management program at Columbia University School of Professional Studies (SPS), the choice was clear. Offutt graduated from the program in 2024, and today, works for Virginia’s Department of Insurance (DOI) where he works to protect consumers and evaluate predictive models. We recently reached out to Offutt to get a better understanding of the issues facing the insurance industry and how students can prepare to respond. Here, Offutt shares his insight.
Emerging Challenges in Insurance Regulation
By Phil Offutt (‘24SPS, Insurance Management)
A key challenge for insurance regulators is making sure premiums are fair and competitive within their states. In property and casualty insurance (home, auto, commercial, etc.) this challenge is amplified by weather factors, record litigation awards, and other environmental pressures that increase claims costs. Traditionally, insurers have responded by raising rates or by cutting operational expenses if rates increases weren’t approved quickly enough. Now, across the country, many insurers are turning to advanced predictive models to refine their pricing algorithms to offer better segmented premiums.
Predictive models help insurers quickly analyze correlations between traditional rating variables, such as building age, materials, or the presence of fire alarms, and non-traditional variables like building tenant credit scores to spot trends in claims losses. When these trends have been validated properly, two things can happen: 1. Non-traditional variables can become new pricing factors, or 2. The total number of rating variables is reduced due to the predictive power of other key variables. Regulators are responding positively—as seen in California’s approval of extensive wildfire catastrophe modeling.
Despite some positive state reception, and even federal enthusiasm expressed in AI adoption through “America’s AI Action Plan”, regulators face the challenge of making sure that price segmentation strategies are based on solid actuarial principles. For example, an underwriting model may show a strong correlation between certain new factors and claims losses. However, correlation doesn’t mean causation; carriers may still need to do the manual work to confirm the relationship. This is especially the case if they are receiving data from third parties.
To address this environment, I recommend that students learn the basic concepts of model validation testing (e.g. lift chart analysis, gradient boosting, generalized linear models, etc.) and the core principles of insurance pricing. Two courses in the Insurance Management curriculum provide this foundation: Technology and Data Analytics, and Product, Pricing, and Distribution. Whether you’re a regulator, business leader, or in another technical role, this foundational material will help you frame your problem-solving related to these emerging issues.
About the Program
The Master of Professional Studies in Insurance Management is for career professionals who want to accelerate their advancement to leadership positions or broaden their expertise in the industry. It accommodates both professionals already working in insurance and those looking to make a career change. The program is part-time, online, and instruction is asynchronous to accommodate working professionals.
Applications are reviewed and candidates are accepted on a rolling basis for the M.P.S. in Insurance Management program. Learn more about the program here.
