I have experienced actuarial education both before and after the emergence of artificial intelligence (AI). I completed my undergraduate education and spent a few years working in an actuarial role before AI became widely available. I recently decided to return to graduate school to pursue a master’s degree in quantitative risk analysis (hoping to deepen my knowledge of asset liability management), and I quickly realized that the learning experience had changed fundamentally.
Experiencing both environments has given me a unique perspective on how AI is reshaping the learning process. If AI is changing how students learn, how does that translate into the skills employers expect from entry-level actuaries? The observations that follow are based on my own experience, as well as discussion with students, professors and industry actuaries.
How AI Is Changing the Way Students Learn
One big difference I have noticed is that AI changes where the thinking occurs during the learning process. Traditionally, learning followed a fairly linear path. We searched for information, read through resources, built an understanding and eventually arrived at a conclusion. Today, AI can complete much of the information-gathering process within seconds. Students can now build a baseline understanding much faster than before. As a result, the emphasis shifts from gathering information to further developing and connecting ideas and validating results. Critical thinking and judgment become much more important.
For example, asking AI, “How are insurance premiums calculated?” will generate a quick baseline explanation, such as “Premiums are calculated based on how likely a claim is to occur and how much it is expected to cost.” Going one step further, a more specific prompt such as, “How are insurance premiums determined from an actuarial perspective using mortality, morbidity, lapse assumptions and regulatory frameworks? Explain the dynamics” will lead to a much richer discussion. It further explains how claims are quantified using underlying actuarial assumptions, how mathematically those assumptions impact premium setting and how external factors influence pricing under different regulatory frameworks. This deeper exploration is where students should spend more of their time thinking and learning when using AI. The difference is the level of thinking behind that question.
Unlike many who assume that everyone will become equally knowledgeable as AI becomes more accessible, I believe the knowledge gap between learners is going to become even wider. Basic information is becoming a commodity; it can be obtained easily. What will distinguish students’ performance is how and what they each build on that foundation. Those who actively explore topics, ask thought-provoking questions and take discussions to the next level will develop a much deeper understanding than those who stop at AI’s first response.
What Employers Still Expect from Entry-Level Actuaries
With AI changing how students learn, has it changed what employers expect from entry-level actuaries? From my conversations with hiring managers and experienced actuaries, the answer is mostly no. The core expectations remain largely unchanged. Employers still look for candidates who are detail-oriented, think analytically and are eager to learn and grow. Just as importantly, they value the ability to solve problems where there is no single correct answer.
For students who have become accustomed to incorporating AI into their learning process, this can be an important adjustment. In school, it is often possible to receive an immediate explanation or solution. In the workplace, however, the challenge is navigating the path to arrive at a solution. Developing this mindset takes training. It requires critical thinking, a deeper understanding of the problem and, especially, the resilience to work through uncertainty.
Practical Recommendations for Students
First, pause and think. Take time to develop your own prediction. Then use AI to test your hypothesis. This will allow a deeper conversation with AI to help affirm your ideas and/or identify elements you may have missed. The goal is to become comfortable with the journey toward a solution. When you take ownership of that process, the final insight feels more original, and you can speak with greater confidence.
Second, develop a “one step further” mindset. Whether you’re studying, interacting with AI or working on a project, don’t stop once you’ve met the minimum requirements. Ask one more question. Challenge an idea. Explore an alternative approach. Look for one additional insight or improvement. Intentionally allocate ample time in the later stages of the learning process.
Last, consider using AI in areas where you already have some understanding. This may seem counterintuitive, since many people turn to AI when they’re stuck. However, having a foundation allows you to recognize when AI’s response is incomplete, ask better follow-up questions and refine its output.
Conclusion
As actuaries, we are trained to critically evaluate our analyses, validate our results and continually ask whether our conclusions are actuarially sound. The actuarial profession is built on this foundation, and AI has not changed that. What has changed is where we spend our time and where we create value. Students who use AI to strengthen their judgment, challenge their thinking and continuously build on their understanding will be well prepared for the next generation of actuarial work.
This article is provided for informational and educational purposes only. Neither the Society of Actuaries nor the respective authors’ employers make any endorsement, representation or guarantee with regard to any content, and disclaim any liability in connection with the use or misuse of any information provided herein. This article should not be construed as professional or financial advice. Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.
Faith (HeeJeong) Kim, ASA, is a graduate research assistant at Georgia State University–J. Mack Robinson College of Business. Faith can be contacted at hkim236@gsu.edu.