Actuarial science is all about making sense of uncertainty—whether it’s projecting future claims, pricing insurance products, or modeling complex risks. Traditionally, actuaries have relied on tables, spreadsheets, and static charts to communicate their findings. But as datasets grow larger and models become more intricate, there’s a growing need for clearer, more intuitive ways to present this information. That’s where animation in data visualization comes in. By bringing data to life, animation can help actuaries—and their stakeholders—see patterns, trends, and uncertainties that might otherwise be missed in a sea of numbers. In this article, I’ll walk you through why animation matters in actuarial visualization, how to use it effectively, and practical steps you can take to start animating your own data today.

Let’s start with the basics. Data visualization isn’t just about making pretty charts; it’s about uncovering insights and telling stories with data. When you’re dealing with loss development triangles that stretch to 60x60 cells or larger, trying to spot trends in a spreadsheet is like looking for a needle in a haystack[1]. Static graphs help, but animation takes it a step further by showing how data evolves over time or across scenarios. Think about how a simple slider animation can reveal how claim patterns develop quarter by quarter—suddenly, you’re not just looking at a snapshot, you’re watching the story unfold[2]. This kind of dynamic visualization is especially powerful for communicating uncertainty, a core concept in actuarial work. Animation can make the range of possible outcomes tangible, helping everyone from junior analysts to senior executives grasp the nuances of your models.

One of the biggest advantages of animation is its ability to capture attention. Our brains are wired to notice movement, so animated charts naturally draw the eye and make it easier to spot changes or outliers[2]. For example, if you’re simulating thousands of scenarios to estimate reserve variability, a static histogram might show the final distribution, but an animated version could reveal how that distribution shifts as more data comes in. This not only makes your analysis more engaging but also helps you and your audience develop a deeper, more intuitive understanding of the underlying risks. And the best part? You don’t need fancy software to get started. Tools like Microsoft Excel, which most actuaries already use, can handle basic animations with a bit of VBA scripting[1]. More advanced users might turn to libraries like D3.js or Observable for interactive, web-based visualizations[3].

Let’s look at a concrete example. Suppose you’re analyzing a portfolio of auto insurance claims. A static line graph might show you how claims develop over time, but an animated version could let you “play” through each quarter, watching as claims emerge, reserves are adjusted, and ultimate losses become clearer. This kind of visualization can highlight anomalies—like a sudden spike in claims in a particular quarter—that might otherwise blend into the background. It can also help you explain complex concepts, like the “chain ladder” method, to non-actuarial colleagues. By showing the process in motion, you make the abstract concrete, and that’s where real understanding happens.

But animation isn’t just about showing change over time. It’s also a powerful tool for exploring uncertainty. In actuarial modeling, we often work with ranges and probabilities—think confidence intervals around reserve estimates or stochastic simulations of future cash flows. Static charts can show these ranges, but animated ones can bring them to life. For instance, you could animate a “fan chart” that shows how the range of possible outcomes widens or narrows as new data arrives. This not only makes the uncertainty visible but also helps stakeholders appreciate the limits of your projections. In a world where decision-makers are often pressured to treat actuarial estimates as certainties, this kind of visualization can be a gentle reminder that the future is always uncertain—and that’s okay.

Another area where animation shines is in scenario analysis. Actuaries often need to compare different assumptions or strategies, like the impact of changing lapse rates or investment returns. Instead of presenting a series of static charts, why not animate the transition between scenarios? This allows your audience to see not just the end results but how one outcome flows into another. It’s a bit like flipping through a flipbook—each frame builds on the last, creating a seamless story. This approach is especially useful in boardrooms or client meetings, where you want to keep your audience engaged and make complex comparisons easy to follow.

Of course, animation isn’t a magic bullet. Like any tool, it can be overused or misused. The key is to use animation purposefully—to highlight changes, reveal patterns, or illustrate uncertainty—not just to add visual flair. Too much motion can be distracting, and poorly designed animations can obscure the data rather than clarify it. That’s why it’s important to start with a clear goal: What do you want your audience to see or understand? Once you know that, you can choose the right type of animation and design it to support your message.

So, how can you start incorporating animation into your actuarial visualizations? Here’s a step-by-step guide based on my own experience and best practices from the field. First, identify the data or process that would benefit most from animation. Are you tracking the development of claims over time? Simulating the impact of different economic scenarios? Exploring the sensitivity of your results to key assumptions? Once you’ve picked your focus, sketch out how the animation might look. You don’t need to be an artist—a simple storyboard or wireframe will do. Next, choose your tools. For most actuaries, Excel is the easiest place to start. With a bit of VBA, you can create sliders, step-through animations, or even simple simulations[1]. If you’re comfortable with coding, libraries like D3.js offer more flexibility and interactivity[6]. There are also online platforms like Observable that let you build and share animated visualizations without installing anything[3].

When designing your animation, keep it simple. Focus on one or two key messages, and avoid cluttering the display with too much information. Use color, size, and motion intentionally—for example, use color to highlight outliers, size to show volume, and motion to indicate change over time. Make sure your animation has clear controls, like play/pause buttons or sliders, so viewers can explore at their own pace. And don’t forget to test your animation with colleagues or stakeholders. Sometimes, what makes sense to you might confuse others, so feedback is essential.

Let me share a personal example. A few years ago, I was working on a project to explain reserve variability to a group of underwriters. The traditional approach—a table of percentiles—wasn’t getting through. So, I built a simple animated histogram in Excel that showed how the distribution of reserve estimates changed as we added more simulation runs. Watching the bars shift and settle made the concept click for everyone in the room. That “aha” moment is what makes animation so valuable—it turns abstract numbers into something you can see and feel.

If you’re looking for inspiration, check out projects like Gapminder, which uses animated bubble charts to show how health and wealth have changed around the world over the past two centuries[5]. While Gapminder focuses on global development, the same principles apply to actuarial data. You could animate loss ratios by region, claim frequencies by age group, or any other metric that changes over time or across groups. The key is to let the data tell its story, with animation as your narrator.

As you get more comfortable with animated visualizations, you can start to explore more advanced techniques. For example, you might use motion paths to show how individual policyholders move through different states (e.g., active, lapsed, claimed) over time[6]. Or you could build interactive dashboards that let users adjust assumptions and see the results update in real time. The possibilities are limited only by your imagination—and your data.

One thing to keep in mind is that not all data needs to be animated. For simple comparisons or one-time analyses, a static chart is often sufficient. But for processes that unfold over time, or for exploring the impact of uncertainty, animation can be a game-changer. It’s also worth noting that animation can be a powerful teaching tool, helping new actuaries visualize complex concepts and build intuition faster.

Finally, let’s talk about some common pitfalls and how to avoid them. First, don’t assume that animation alone will make your data understandable. Clear labeling, thoughtful design, and a well-structured narrative are just as important[4]. Second, be mindful of your audience’s attention span. Long, looping animations can lose viewers, so consider adding controls that let them pause, rewind, or jump to specific points. Third, make sure your animations are accessible. Not everyone can perceive motion easily, so provide alternative ways to access the information, such as static summaries or data tables.

In terms of statistics, there’s not a lot of formal research on the impact of animation in actuarial visualization specifically, but the broader data visualization literature consistently finds that well-designed animations can improve comprehension, engagement, and retention of information. For example, studies in education and cognitive science show that dynamic visualizations help learners build mental models of complex processes more effectively than static ones. While actuaries may not always have the luxury of conducting formal usability tests, the anecdotal evidence—and the growing popularity of tools like Gapminder and D3.js—suggests that animation is here to stay.

To wrap up, animation is a powerful but underutilized tool in actuarial data visualization. It can help you uncover insights, communicate uncertainty, and engage your audience in ways that static charts simply can’t match. Whether you’re working with Excel, coding in D3.js, or experimenting with online platforms, the barrier to entry is lower than you might think. Start small, focus on clarity, and don’t be afraid to experiment. The next time you’re staring at a massive loss triangle or a complex simulation output, ask yourself: Could animation help me—and others—see what’s really going on? Chances are, the answer is yes.