I’ve been an actuary for 19 years.
I’ve worked in life, pensions, and P&C in markets around the world, including the Netherlands, Portugal, France, Spain, Italy, Turkey, Malaysia, Hong Kong, Japan, Thailand, and, currently, Australia and New Zealand.
I would be able to breathe underwater, basically be Aquaman.
I think the key to being an innovative actuary is to go beyond the mathematical challenge to ask yourself, ‘what is the real question that needs to be answered here from a business point of view?’ You have to venture into the business side of being an actuary.
In my own experience, it’s the relationship that you’ve got with leadership and the trust they have in you. Once an exec team found out I could answer questions that mattered to them, I could approach them and say, ‘I know you’ve got this question, give me some time and budget and I can solve it’ and they would pretty much always say yes. Gaining trust and support from leadership, and sometimes asking for forgiveness rather than permission, helps you become an innovative actuary.
Within life insurance, I am very convinced that it’s going to be the integration of data science and actuarial science. The reason I say this is the need for those at the exec level to answer important questions about how customers actually behave and what drives their decisions.
Execs want to understand things like what kind of customer actually takes up a benefit reduction offer, what kind of customer actually converts to GMIP options for variable annuities, what kind of customers lapse for what reasons, etc. There are so many questions like these that tremendously influence the value of your portfolio and your sales, and eventually all insurers will have invested in answering these questions.
I think the biggest opportunity now for actuaries is that crossover between data science and actuarial science, what McKinsey calls a ‘translator role’ - which is really the person that has got the subject matter expertise but also understands data science and brings the two together. So really dig into that and don’t apply it to compliance. Even though it has historically been business actuaries following compliance, I think this will shift as the understanding of what really drives financial results and actuarial projections starts to come from the business side of actuarial, not compliance.
Answering the questions with regards to customer behavior and decision making: there’s been investment into making this happen, but it often fails. The fact that it has failed doesn’t mean that it can’t work, the fact that it has failed means we don’t understand how it works.
I believe the solution is more actuaries need to learn to think like data scientists. The mistake we often make is assuming we understand data science because we have a mathematical background. It’s not that simple. In my own experience of working with data scientists, we think fundamentally differently about many things.
Some characteristics of impactful innovation are having a lasting impact and being widely adopted by an organization. What I mean by that is people really believe in and want to use this innovation, they don’t fight it. It has to be contributing significantly to company results. Another key characteristic of impactful innovations is that they encourage or lead to more innovation - it’s a snowball effect. So what we often experience at Montoux is we often are working with people who have been doing the same job for a very long time. Often, they’re concerned with innovation because it does mean a change and they haven’t been exposed to that before. So we notice that, once we’ve gone through the first round of change, they begin to buy into it, and they see the benefit in it. This means the next wave of change or innovation is much easier.
To me, the inclusion of data science is probably the most impactful one. Actuarial modeling doesn’t really need to change that much, as in the mathematical structure of it. It is really about the assumptions that we plug in, which come from solid data science. These provide the insights to drive much better business decisions, which is where I believe the greatest impact will come from.
If I think about the actuarial profession as a whole, rather than specific technical bits, actuaries in general are more tentative to try something and make a mistake, more so than many other professionals. I reckon if actuaries were more open to trying things, and obviously resources are a key issue here too, we would see a lot more actuarial innovation. What is holding us back as a profession is that we have too much to do, and I don’t think we make the time to fix that problem. Automating is not in our blood. As we go through our actuarial careers, we’re taught to create workarounds rather than structural solutions.
If you look at any actuarial process, and you count the number of workarounds, it’ll be in the hundreds or thousands, which is just the nature of the tools we have to work with. But there are better tools available, so it’s really about freeing actuaries up by using tools to automate the low value-added work they have to do. If you do that, actuaries have time to work on things that have a meaningful and positive impact on the business and customers. That change, and a shift in mindset from fearing failure to inviting learning, would make a huge difference towards actuaries being more influential.
I think the first thing that would need to happen is for actuaries to adopt and better understand data science, which happens in GI but not really in life insurance. Instead, data science teams have been formed within life insurance companies separately from actuaries, making it hard for them to work together. I think most high-impact potential for actuaries begins with blending actuarial and data science, which I don’t think we’ve gotten right as an industry and profession.
The other thing is actually getting a wider understanding of the various tools available to them. This is about technology. There are many tools available that can create a more robust, more efficient process to work through, rather than relying on the tools you learned on, many of which are outdated.