I became an actuary in January of 2007 so just over 15 years.
I have worked in just the US as far as the insurance market but I worked in medicare supplement for a short stint and then the rest of that time has been in long term care insurance. I’ve spent about 13 years in long term care insurance.
The ability to remember everything, that would be fantastic. Especially in my job, it would be great if I could just remember everything, it would have made my exams a lot easier.
I think one of the most crucial characteristics of an innovative actuary is the ability to change, whether that means changing the way they think about things, changing their opinion or their stance. We are constantly barraged with new information and things are constantly evolving. The ability to not be stuck in one way of thinking, one way of approaching problems, I think being able to change and do that seamlessly is probably, in my mind, the key characteristic of an actuary who can really pursue innovation.
To me, communication is the key to really being impactful within your company as an actuary. It’s not just “can you read a presentation in front of a group of people” or “can you lead a meeting”, it’s really about whether you can take the complex ideas that you deal with on a day to day basis and translate them into concepts that multiple different areas of the company can understand and they can take away the key insights and points of what you’re trying to say. So, can you take an idea and make others understand that idea? That is probably the most crucial piece to being impactful in your company because most actuaries are pretty smart and they know their math, but it’s that next step of “how do I take what’s in my brain and make sure that you can understand it”.
I do think it’s important that companies take an active role in trying to develop their actuaries and emphasizing that growth but ultimately, it’s going to be the actuary that needs to take ownership of their own development. It’s establishing mentorship with other, more tenured actuaries, but even more importantly, not just actuaries. Some of the most robust feedback I’ve gotten over my career and the most guidance I’ve gotten was from people who were not actuaries at all, but see the world in a completely different light than I do. That will really help you to understand other perspectives and be able to craft your own form of communication around that. While it’s great that companies can do stuff for their employees and I think they should, it really falls on the actuary to take their own development seriously.
For me, there’s been more and more of an emphasis over the years on actuaries understanding how to interpret data, understand data and have more of an expertise in data science. We’ve kind of seen the evolution of actuaries where it started as working with life tables, contingency tables, calculating reserves and doing pricing, but now technology has come far enough that computers do a lot of that for us. I think the role of the actuary these days is to understand some of the more complex analytical techniques, be able to digest them and use them to drive business strategies. The role of the actuaries is evolving from the person who’s in the back room doing all the calculations to now, someone who has to be front and center and translate all of the calculations the computer is doing and explaining to business leaders how you can actually use that output. They are more reliant on understanding what goes into our models and getting a better sense of communicating it externally to business leaders.
That one is a tough one because I do think there is a lot of overlap. Actuaries, with the exams focusing a bit more on data science and predictive analytics, as a profession are now starting to encroach on those same areas that data scientists fulfill. I think that when you think about approaching a problem, there are multiple different facets in which you need expertise. Data scientists can very deeply think about the processes, the models, the path that we follow to answering questions. They look at “Am I using the right data in the right way?”, “Am I incorporating it into my model properly?”, “Am I thinking about all of the variables that go in my model?”. Data scientists can do a good job of what I call “deep thinking” whereas actuaries have that “broad thinking” mentality where they are not just thinking about “How do I solve a question with data and my model?”, but “How am I thinking about everything else in my organization, my processes, my practical applications - how policyholders behave?”. I think that intersection between broad thinking that actuaries bring, the deep thinking that data scientists can bring and being sure that each of them can do a little bit of both, gets you a better overall outcome.
Just because something’s innovative, doesn’t make it impactful and you don’t necessarily have to have something that is innovative to make an impact. I think that when those things can cross over, when you have an innovative solution and something that is impactful, that’s generally those big headline things that you see. These are the breakthroughs and I think to have an innovation that’s impactful, the entire time you are thinking through what your solution is or what you’re trying to develop, you have to keep in mind “What’s the goal?”, “Where will this be used?” and “How is this going to change the direction of what I’m doing?”. So if my company says we need something that’s impactful, it has to align with what they are trying to do strategically, and then I can start to innovate. But, if you just innovate without any goal in mind, then you’re generally going to find really interesting things that don’t necessarily lead you anywhere.
I have and it’s weird because when you say something is innovative today, it may seem not all that innovative but it was 5 or 10 years ago because of how quickly things evolve. One of the things we take for granted, speaking from the long term care insurance perspective because we have an incredibly complex product, is the advent of doing multi-state modeling and the ability to see all of the different interactions with different assumptions from an actuarial perspective on long term care insurance. To actually see that granularity in models and individually be able to tweak my incidence up, tweak my claim termination rates, my utilization, to look at morbidity improvement and actually see how that’s changing over time. It used to be that we just had models that had a claim cost , it was just one singular number and then you could maybe play with the termination rate. It was really simplistic modeling. The advent of being able to use cloud computing and generally just having more resources technology wise led to multi-state modeling and it’s given us a deeper understanding of our product and the way it behaves over a long period of time. Being more complex doesn’t always make it easier to understand or make you more right, but it’s driven us to try and understand the complexities of modeling much more easily. There’s an appreciation for that. Being able to get that granularity in our modeling and understand things more fully has been a huge innovation and it’s driven us a long way to understanding our product and our policyholders that we serve better.
I do, and I briefly mentioned cloud computing, that’s becoming more of the norm so the constraints that we had before like server size and storage size are really kind of going away. Now we can start getting more into predictive models, doing monte carlo simulations on large amounts of data, things that before, when we had constraints, we couldn’t do. I do see that the industry is going more that way, and it’s not just long term care insurance, all kinds of insurers are doing more and more of these things. I do see that going further and further and then we can get more and more in the way of doing simulation and predictive modeling.
I honestly think it is the way that you think about things, being adaptive. Just in the 15 years that I’ve been an actuary, which in the grand scheme of things isn’t that long, the way that the actuary is used within the industry has changed dramatically. I kind of sit in between two generations, I have a lot of coworkers who are from that earlier cohort of actuaries that got their start doing life contingency tables and valuations. The new actuaries that are coming up are very fluent in R programming and understand predictive analytics very well. In that short time period the use of actuaries by companies has changed dramatically. If you don’t change with the need, that’s where you can get left behind a bit or you’re struggling to find solutions in a world where your tool kit that you had before is not necessarily relevant. I’d say being adaptive, constantly getting out there trying to learn new things, trying to think about things differently and not being married to what you’ve known historically are key. Even though it’s gotten you to where you are today, to get to tomorrow you’re going to have to learn new things.
I would tell them to first, get a feel for what actuaries do. Reach out to an actuary or a couple of actuaries, that work in the profession and get a sense of what they actually do on a day to day. When I became an actuary it was because I like math and science, I got a math degree and a computer science minor and I thought “well what do I do now?”. I just kind of started poking around out there, reading the articles about actuary being a good profession and I really kind of fell into it. I’ve heard that from a number of actuaries and a lot of time it works out, but I also wouldn’t say it’s the best approach to becoming an actuary. So, I’d say first, get a sense of what they actually do and try and get a sense if that’s something you’d like doing for a long period of time. Once you start getting into a career, you look back and think 15 years has gone by in the flash of an eye and you want to make sure that what you are doing is making you happy and it’s stimulating you.