Last month I spoke with local Chicagoan, Todd Livergood, to discuss both his career and the intersectionality of Actuarial Science and Data Science. Todd has been a Fellow of the Casualty Actuarial Society since 2013. More recently, he decided to pursue his Masters in Analytics at the University of Chicago, in response to the emerging need for technical & Analytical skills as an Actuary.
Todd has been working on his capstone, one of his final steps towards graduating. Admittedly, he looks forward to completing the program and celebrating his accomplishment on vacation, “very much looking forward to that.” Noting the unique feeling of accomplishing something “you put a lot of work in to. And now, you know, it’s something you can be take a little pride in and feel good about so and you’ll learn something along the way. It’s a very rewarding feeling. And I’m really happy to get through it.”
Q.) So, tell us about yourself, why did you want to be an actuary? How did you get started with actuarial science?
A.) Seems like an eternity ago but when I went to undergrad, college, you know, I’ve always liked math and similar disciplines. I started off as a psychology major for the first couple years and then decided to go back to something I really liked and was good at. So, I switched majors midway through college to become a statistics major. And that’s what I have my bachelor’s in. Late in the game – especially, comparative to now – I heard about the actuarial career. So, I decided to do some research about it and it touched upon a lot of things that I’d like to do.
Q.) What was your personal experience or journey with the exam track?
A.) I ended up taking my first exam in college and was lucky enough to end up passing it. Right after I graduated, I had one exam and I was taking my second one. That kind of sent me down the career path that I’m at right now. I remember it took me a lot longer to pass, I don’t think I got my designation till 2013. I feel like I went up and down, I was passing a couple of exams and then failing a couple of them. I consider it a couple of walls along the way. But it was a once in a lifetime feeling to get my fellowship. It was something I just kept working towards, and eventually got. It’s a rewarding experience to put a lot of work in and you learn a lot of things along the way.
Q.) What do you think the differences of the exam progress/process are now?
A.) Now, it seems like, you know, back when I was going into the career, if you had one exam, I think you were in pretty good shape. I remember being on the recruiting committee, and I know that it seems nowadays that you have to have multiple exams and a high GPA, and I feel like the bar might be raised a little bit from when I took my exams. I was even wondering if I would have even been considered a candidate. And I had just come out of college with one degree and no internship or anything like that!
Q.) So, internships weren’t necessarily required at that time? And what was your first actuarial job?
A.) No, I graduated college in 1998 at Grand Valley State University in Allendale, Michigan, which is close to Grand Rapids. And they didn’t have internship programs at the time. It’s not like there was an actuarial degree or a program at Grand Valley, they happen to have one new course they offered to prepare me for exams, but there was no program to speak of. Exams and my first job were how things started out.
Q.) So, could you tell us about your first actuarial job?
There was a recruiting drive from all states that was close by. But it’s so long ago, I’m trying to think how I even heard of that. I think maybe one of my professors or another student mentioned it. And so, I signed up for that at the last second. From there, I started looking into some places that I might want to work. I kind of had in my mind on working in the big city, so Chicago and Indianapolis were the cities that sort of attracted me. Allstate was in Northbrook, Illinois, a little bit north of Chicago. I ended up interviewing with them. And got it as my first position outside of college.
I worked at Allstate for two years and lived out in the suburbs before I moved to the city. But the commute was sort of a nightmare from the city to Northbrook. So, a couple of folks who I previously worked with at Allstate referred me to CNA. And CNA is where I proceeded to spend most of my career.
Q.) This brings me to my next question. And that is, what made you decide to join the CAS over the SOA?
A.) I made the decision while taking exams and based on where my experience came from, working at Allstate. I definitely wanted to get my designation ‘yesterday’ and pursue the profession.
You know, I like learning things and for me, it was just a learning experience. But it’s just it’s so nice to actually get past that final hurdle and get in and get your fellowship – just something I never thought about until I got there. At times, it felt like a fire being put out by an ambulance, as I gained more experience for passing or failing the exams, until the situation was handled.
Q.) What made you interested in data science or pursuing your degree in analytics?
A.) My career path took flight into the people managing side. So, over time, I sort of got away from a lot of analytics and things like that. I remember working on a project at my prior employer, and there was a lot of heavy modeling, predictive analytics, things like that. I had the feeling that ‘gosh, I must be the dumbest person in the room. I don’t know any of this stuff.’ This opened my eyes. I came to realize I was living a little bit limited in some of the things that I could do. And at the same time, it’s difficult to find the time to invest in doing some of that stuff. Finally, there was a point where I said; ‘You know what, I’m going to take the time and do something about it.’
So, I actually did a certificate program at the University of Chicago, and I figured that was a good way to put my toes in the water and see if this is something I truly enjoy- ‘Do I like this stuff? Is it something that I feel is interesting?’ I went through it for a summer, maybe it was like six or nine months, I can’t remember exactly how long it took.
In some of my classes we touched upon some of the programming, like machine learning, building a machine learning algorithm, and how you present your best practices for telling the story. You’re taking something that’s complicated, a machine learning algorithm and explaining the way that this is and how it’s going to benefit someone. And that really spoke to me, and I came out of that, ‘like, wow, this is really cool.’ I really liked it, I liked going into it, I didn’t know a lick about Python, R. And then it got me exposure enough where it felt like it was a lot of fun. Finally, I made the decision that I’m just going to go ‘full bore’ into this.
The other thing is, I was thinking from a career standpoint. It seems like the actuarial career is going in that direction. And I feel this will open the door to other things that I might like to do in the future. And that’s how I made the decision just to go all in. As I mentioned before, I love the whole learning process, and there was a time period where I felt like I wasn’t learning anymore and stagnant per se. I was still learning things from a business standpoint but from an analytics standpoint, I was at a bit of a standstill.
Q.) What made you choose University of Chicago?
I know University of Chicago is a strong school and while we were cooped up inside in the pandemic it felt like the right time to do something. So, time and the strong program led me to go the direction of getting my masters at U of C.
Q.) Seems like good timing too that you’re finishing up, as the world’s getting more hectic again. That’s great. I did notice that you had taken on different types of boot camps, including courses that you did through the University of Chicago, did you take other courses?
A.) Yeah. Online, there was a dummy class that I took, trying to learn Python that someone had suggested. This was mostly pure Python learning and wasn’t necessarily engaging. I also took a few classes with Coursera, they had a few classes, including the data science toolbox. I took those early on just, again, to get a little bit exposure to some of the programming and analytical perspective.
With Coursera, I think they have a whole series, maybe 10 courses. I only took the first couple before deciding to do a Masters. If I hadn’t, that would have been another avenue I could have pursued – if I wanted to do something a little bit less. It’s amazing how many online courses and company resources there are now. In fact, I am sure I will take more after finishing my masters here, there’s just so much to learn! To me, almost never ending.
There’s so many different avenues and branches that you can go down with data science. So, I’m sure I’ll be revisiting some of those and can become more specialized in neural networks or other similar techniques. All the courses I have taken were really helpful.
Q.) To expand more on your other response, what is your experience with communicating your findings to a non-technical audience? Do you think it was well addressed within some of these courses?
A.) That’s actually a big point of emphasis with the program. In fact, I think they have more specific programs that you can take electives for, as well. There is even one that actually deals specifically with the communication piece.
The great things about each one of these classes is there’s always a project that’s involved, group project, work every step along the way, so you are always explaining things.
For instance, with the Capstone, you have to explain the results. So that is something that I’m dealing with right now. I’m not sure how other programs work but at University of Chicago, that’s the point of emphasis. Because in the real world, that’s what you’re going to have to do.
The focus is all data science and business skills at the same time. It’s hard to do the one without the other. If you can’t explain things, you could build the best model of the world, and it’s not going to do any good. If you can’t explain how it’s going to benefit someone or how it’s going to help improve something, what’s the point? And if it’s not technical, you’re dealing with non-technical people all the time, so you have to have those skills.
Q.) That’s amazing that it is integrated and especially since it is a necessary aspect. What do you think the biggest difference is between actuarial science versus data science? Or how do you see them merging?
A.) So that yeah, that’s a really good question. It seems like with actuarial work, there’s a lot of set processes that are involved. So, you know, reserving there’s looking at getting the data around in a certain way, by accident or by policy, or you have to incorporate specific methods like the chain ladder method or you have to make selections. If you’re doing a pricing review, you have to pull in the expenses, have to consider other factors and maybe you have to go talk to someone from claims. With actuarial work, people maybe have to understand things a little bit more.
To me, the data scientist can almost help strengthen a lot of things for actuarial, pulling from more real world evidence. I’d be interested to hear what other people have to say but Data Science enhances the things that you can do from a natural standpoint as far as the accuracy, the speed, and maybe unlock some findings that you may not have found before from a pricing standpoint.
From a strategy standpoint, I feel like it just unlocked a lot of doors within the actual world, versus if you didn’t maybe have some of these skills.
Q.) Definitely. So, you would agree, it’s not necessarily one versus the other? They work together?
A.) Yeah, absolutely. I think there’s some similar characteristics or aspects and one of the biggest parts of a data science project isn’t gathering the data. That is just something you are able to play a part in- when you’re doing everything, you have to get the data, you probably have to do some manipulation, and you have to do some checking. At the end of it, you’re going to have to present some results.
A lot of standard practices in actuarial are similar or come from Data Science, but there’s some extra things that you can add onto and provide some extra insight. To me, they can enhance each other, I think they synergize really well, and I don’t think of it as a one versus the other.
As a Data Scientist, you might not know anything about insurance, and this could be a difficulty. From an actuarial standpoint you may take the harder route with an extra technique like clustering analysis that could provide a finding that points to a strategy, to target a certain segment that might be performing well. I could see there being some techniques that you could use from data science perspective that would help easily find those answers in a better way. So yes, I think they definitely work hand in hand.
Q.) Which brings me to- how would you define a data scientist in your own words?
A.) Gosh, that’s a really good question. Very hard question. Some years, it can be someone who takes the data, and has the ability to unlock findings of the data that you wouldn’t be able to find otherwise. And you can do that through some statistical techniques that you’re taught along the way. And it’s someone who can communicate those findings in a way that provides an insight, or maybe it drives that strategy, or leads to a better process.
A DS is someone who has the tools to be able to see that through. Whether it’s doing a linear regression, clustering analysis, or maybe even something more advanced – do they have the tools in their toolkit to be able to unlock those findings? Someone who’s not limited in what they’re what they’re able to do. Someone who is confident their best answer is the best answer.
But what is the threshold? What kind of specific skills apply? If you can build a linear regression model, does that make you a data scientist? I’m not sure if there’s like a fine cut off line. I feel like it’s just like an amalgamation of a whole bunch of different skills.
Q.) We’ve touched on it overall in our conversation, but to ask directly – how have you seen the industry evolve, that being actuarial and data science?
A.) Good question, it seems that companies are trying to acquire insurance companies. They are trying to incorporate more data science strategies to the world, and it can feel like they are doing it for the purpose of saying they are.
From a pricing standpoint they are pursuing building more predictive models. There are probably companies that have been doing it for a while. Like at Allstate, I know a very large part of their organization is devoted to this mode of development.
There is probably a good product line now that may not have a ton of data, but still tries to use these techniques. Using them from a pricing perspective, or from a strategy perspective, to enhance the decision making to improve the whole process. For business analytics, it seems there’s more focus on communicating the data through visualization, whether it’s through Tableau or Power BI. I feel that is a whole other area – using those communication tools and trying to leverage them as much as possible.
Todd encourages both new and seasoned actuaries to deepen their technical & analytic skills and thinks the best actuaries continue to challenge themselves in any way they can. “It’s good to know that there are so many online resources that I could turn to, to sharpen my skills even more.” I think they were really helpful.
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