By Chi Wai Lima, Creative Director, Triple-I
As part of celebrating Asian American and Pacific Islander Heritage Month, we have interviewed Jessica Leong, FCAS, lead data scientist at Zurich North America and president of the Casualty Actuarial Society (CAS).
Currently residing in Chicago, Leong shares her insights on how technology and big data are changing the actuarial career path and insurance landscape. She speaks about her team’s work at Zurich and how data science and analysis have helped to improve claims models. In addition, Leong shares the CAS’s initiatives to actively support diversity, equity and inclusion in the insurance industry.
Triple-I CEO Sean Kevelighan currently serves on the CAS board of directors.
You’ve been able to live around the world: Australia, the UK and now the US. What moves in your career did you make for that to happen? What piqued your interest in actuarial studies and the path that led you to data science lead at Zurich?
I decided to become an actuary very early on in my career. I grew up in Australia, and when I was in high school, I knew I was good at math and I was looking at what professions that would lead to. Actuarial naturally sprung up as it does for a lot of people who are good at math, but it looked like a really rewarding career and a rewarding profession.
A lot of Australians like to take a year off university and do backpacking around the world. I took a year off, went to London and got my first actuarial job, working six months at St. Paul. With that money I backpacked around Europe for a year. Then I went back to Australia, finished my degree, and my first job out of school was in London. I just had the itch to go back, and the actuarial profession is a good one if you enjoy traveling.
Then my boyfriend-now-husband got a job in New York, so that’s why I moved to the States. I never actually thought I would live in America, and it’s been more than a decade.
Would you be able to share a project that you’re currently working on at Zurich?
I have a team of data scientists at Zurich, and we build models for three different groups: For underwriting, to help us with risk selection and pricing; for claims, to work on better claims triage and finding claims fraud; and then lastly for our customers to help them better manage and understand their risks.
We have done a lot of work in claims. For example, we have built a claims model that alerts us if a workers’ comp claim is going to become complex, and if it would benefit from having a nurse to review that case and manage it much more proactively. That has really benefited Zurich in terms of outcomes. It has also benefitted our customers and their employees in terms of getting back to work and regaining their health. It’s been a win-win all around.
What are some challenges you’ve experienced in using data in relation to privacy, regulations or bias?
This is a very big topic for not just the insurance industry, but also more broadly, as big data gets bigger and artificial intelligence continues to advance. Something that we do for all of our models is talk to legal, compliance and privacy. They do a thorough review of the models before we actually put them into production, to make sure that from the data and the algorithm viewpoints, we stay true to our principles within Zurich. A few years ago, Zurich released a data commitment to the general public and to our customers about the kind of data we will and will not use so we take that seriously.
Are there any implications that you’re seeing that the pandemic has had on data analysis?
Yes, definitely. A lot of the analysis that’s done in insurance relies on the history being somewhat predictive of the future, and frankly, all data analysis relies on that because data is by definition, historical. So anytime you try to make a prediction from data it is relying on historical fact, and obviously the pandemic really upended that. How do I look at this data and use it to make predictions of the future? It is less clear, and we’ve had to rely much more on judgment, and we’ve had to really think outside the box about the different types of data we should use now to try to make predictions of the future.
Congratulations on your presidency of the CAS. Why did you join CAS and what led you to being elected as president?
When I initially joined the CAS in 2005/2006, I volunteered for the organization. About a third of our members volunteer in some capacity, which is tremendous for any society – that’s a very high rate. I find that the actuarial community is just a great community.
One of the benefits of volunteering for the CAS is having the chance to grow your leadership skills. Before long, I was chair of one of the seminar-organizing committees. That was a really good experience in terms of leadership for me, early in my career.
I was given the suggestion by my boss, about seven/eight years ago now, that I should be on the board of the CAS. It had never crossed my mind, honestly, that I would be even eligible for a job like that. The CAS has a nominating committee, who called me and asked me to run. Then I got a call, maybe two/three years later, asking if I would consider running for president. I’m so honored to have this role.
There’s a three-year plan to create unicorns. Are you seeing any impact so far? Is this resonating a lot within CAS and the industry?
Last November at our annual meeting, we released a new Envisioned Future and a three-year plan. Our new Envisioned Future says “CAS members are sought after globally for their insights and ability to apply analytics to solve insurance and risk management problems.”
Now that might not sound like much, but if you think about what it used to say, something like “the CAS advances the practice and application of actuarial science,” we made the change to be more evergreen and more actionable. We will do whatever analytics needs to be done, and we will do it to solve business problems in insurance, and this will evolve over time.
What this means is that the actuary of the future needs to have three key skill sets. First, they need to be great at analytics, the kind of analytics you need to solve the important insurance problems of today. Second, they need to be great at problem-solving. Actuaries are good at solving the core problems in insurance, pricing, reserving, capital modeling. But more and more with big data, there are new problems you can solve. The example I gave before – is this claim going to become complex, would it benefit from having a nurse? Those are new problems you can now solve with data and analytics that you probably couldn’t have done before. The third area is the domain knowledge in terms of P&C insurance.
That is the unicorn. That is the actuary of the future, having all three key skill sets.
How are you attracting a more diverse body of students to pursue actuarial or related studies? How are you trying to attract different types of people and different ways of thinking to the CAS and to the insurance industry in general?
One of the pillars in our strategy that we released with our Envisioned Future is to diversify our pipeline. We have various initiatives to look to do that. One thing is we are pushing forward in terms of diversity, equity and inclusion, and we recently put out some metrics on our website. Right now, for example, 23% of our members are Asian, under 2% are Black and under 2% are Hispanic. The diversity from the Black and Hispanic point of view is not where we want it to be, and we have a goal of increasing that to about 5% to 8% of our new members in the next five to 10 years. We put a stake in the sand in terms of how we want our racial diversity to improve.
A few years ago, we engaged a consulting firm to figure out what is holding us back in terms of having more diversity. One of the things they identified is just finding out about the profession early in your life is going to be key, because a lot of people in various racial and ethnic groups are not really finding out about the actuarial profession when they need to. So we’ve been doing actuarial high school days, visiting diverse high schools to talk to them about the actuarial profession.
We also have a scholarship program for these underrepresented groups, where we will pay for exams given a few qualifying criteria, because we know that the cost of the exams can also be a hindrance, especially when you’re still in school and you’re not earning any money. To get an internship, you need to have three exams under your belt, but they cost money. It can be tough, so we’re seeing what we can do to help.
What challenges have you had to overcome, as a woman and a person of color in the insurance industry?
I’m very big on self-improvement, and I have tried to develop myself in a way to be successful in this environment.
If I think about my upbringing, it was different as an Asian person growing up in Australia. When I was in high school, I was on the track team and I had wanted to be in the relay. There were only four people in the relay, and I wasn’t picked as one of the four, even though I was probably the third fastest person in the school. I thought that this was just unfair and favoritism. I told my mom, “This is really unfair; you’ve got to do something about this,” and she told me, “Don’t complain; just do what you’re told. Don’t stick out.”
That really jarred with me then and still now, thinking back on it. That highlighted the difference in culture. As I’ve been navigating my way through predominantly Western work culture, I have worked pretty deliberately to think differently and to acquire skills that would help me in this kind of work environment.