By Chi Wai Lima, Creative Director, Triple-I
As part of celebrating Asian American and Pacific Islander Heritage Month, we interviewed Jessica LeongFCAS, principal data scientist at Zurich North America and president of Accident actuarial society (CASE).
Currently residing in Chicago, Leong shares his insights on how technology and big data are changing the career path of actuaries and the insurance landscape. She talks about her team’s work in Zurich and how data science and analysis has helped improve claims models. Additionally, Leong shares CAS’s initiatives to actively support diversity, equity and inclusion in the insurance industry.
Sean Kevelighan, CEO of Triple-I, currently serves on the CAS Board of Directors.
You have been able to live all over the world: Australia, the United Kingdom and now the United States. What changes did you make in your career to make this happen? What piqued your interest in actuarial studies and the path that led you to become Head of Data Science in Zurich?
I decided to become an actuary very early in my career. I grew up in Australia and when I was in high school I knew I was good at maths and I was looking into what careers that would lead to. Actuarial science came naturally, as it does for many people good at math, but it looked like a very rewarding career and profession.
Many Australians enjoy taking a year away from university and backpacking around the world. I took a year off, went to London and got my first job as an actuary, working six months in St. Paul. With this money, I traveled Europe for a year. Then I returned to Australia, graduated and my first job out of school was in London. I just wanted to go back, and the actuarial profession is a good profession if you like to travel.
Then my boyfriend, now husband, got a job in New York, which is why I moved to the United States. I never thought I’d live in America, and it’s been over a decade.
Would you be able to share a project you are currently working on in Zurich?
I have a team of data scientists in Zurich and we build models for three different groups: for underwriting, to help us with risk selection and pricing; for claims, work to better sort claims and detect fraud; and finally for our clients to help them better manage and understand their risks.
We’ve done a lot of work on claims. For example, we’ve built a claims model that alerts us if a workers’ compensation claim is going to become complex and if it would be beneficial to have a nurse review that case and handle it much more proactively. This really benefited Zurich in terms of results. This has also benefited our clients and their employees in terms of returning to work and restoring health. It’s a win-win situation all around.
What challenges have you encountered in using data regarding privacy, regulation, or bias?
This is a very important topic not only for the insurance industry, but also more broadly, as big data gains momentum and artificial intelligence continues to advance. What we do for all of our models is talk about legal, compliance and privacy aspects. They perform a thorough review of the models before we put them into production, to ensure that, from a data and algorithm perspective, we stay true to our principles within Zurich. A few years ago, Zurich published a data commitment to the general public and our customers regarding the type of data we will and will not use, so we take it seriously.
Do you think the pandemic has had any implications for data analysis?
Yes definitely. Much of the analysis done in insurance relies on the fact that history is somehow predictive of the future and, frankly, all data analysis relies on that because data is by definition historical . So any time you try to make a prediction from data, you’re relying on historical facts, and obviously the pandemic has really shaken that up. How can I examine this data and use it to make predictions about the future? It’s less clear, and we’ve had to rely a lot more on judgment, and we’ve had to really think outside the box about what different types of data we should be using now to try to make predictions about the future.
Congratulations on your CAS presidency. Why did you join CAS and what led you to be elected president?
When I joined the SCA in 2005-2006, I volunteered for the organization. About a third of our members volunteer in some capacity, which is great for any company – it’s a very high rate. I find the actuarial community to be simply a wonderful community.
One of the benefits of volunteering for the SCA is the opportunity to develop your leadership skills. Shortly after, I served as chairman of one of the seminar’s organizing committees. It was a very good leadership experience for me at the start of my career.
My boss suggested to me, about seven or eight years ago now, that I should serve on the CAS board. Honestly, it never occurred to me that I would be eligible for a job like that. The SCA has a nominating committee that called me and asked me to run. Then I got a call, maybe two or three years later, asking if I was considering running for president. I am so honored to have this role.
There is a three-year plan to create unicorns. Do you see an impact so far? Does this resonate a lot within CAS and the industry?
Last November, at our annual meeting, we released a new envisioned future and three-year plan. Our new Envisioned Future states that “CAS members are sought after globally for their knowledge and ability to apply analytics to solve insurance and risk management problems.”
This may not seem like much, but if you think about what used to be said, something like “CAS advances the practice and application of actuarial science”, we made this change to be more scalable and practical . We will do all the necessary analysis, and we will do it to solve the business problems of the insurance industry, and it will evolve over time.
This means that the actuary of the future must possess three key skill sets. First, they need to be good at analysis, the type of analysis you need to solve today’s important insurance problems. Second, they must be good at solving problems. Actuaries are skilled at solving fundamental problems of insurance, pricing, reserves and capital modeling. But Big Data increasingly makes it possible to solve new problems. The example I gave earlier – is this claim going to get complex, would it be beneficial to have a nurse? These are new problems that you can now solve with data and analytics that you probably couldn’t have done before. The third area is domain knowledge in terms of property and casualty insurance.
It’s the unicorn. He is the actuary of the future, possessing the three key skills.
How do you attract a more diverse student body to pursue an actuarial science or related field? How do you try to attract different types of people and different ways of thinking to CAS and to the insurance industry in general?
One of the pillars of our strategy that we published with our Envisioned Future is the diversification of our pipeline. We have various initiatives to achieve this. One thing is that we are making progress in terms of diversity, equity and inclusion, and we recently published some indicators on our website. Currently, for example, 23% of our members are Asian, less than 2% are Black, and less than 2% are Hispanic. The diversity of Black and Hispanic perspectives is not where we want it to be, and we aim to increase it to approximately 5-8% of our new members over the next five to 10 years. We are putting a stake in the sand for how we want our racial diversity to improve.
A few years ago, we hired a consulting firm to figure out what’s stopping us from having more diversity. One of the things they identified is that it’s critical to learn about the profession early in life because many people from various racial and ethnic groups don’t really learn about the actuarial profession when they first get into it. need. We therefore organized secondary actuarial science days and visited various secondary schools to talk to them about the actuarial profession.
We also have a scholarship program for these underrepresented groups, where we pay for exams based on a few eligibility criteria, because we know that the cost of exams can also be a barrier, especially when you are still in school and that you are not making money. To get an internship, you need to have three exams under your belt, but they cost money. This can be difficult, so we see 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 am very committed to personal development and have tried to develop myself in a way that I can succeed 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 wanted to be part of the relay. There were only four people in the relay, and I wasn’t chosen among the four, even though I was probably the third fastest person in the school. I thought it was just unfair and favoritism. I said to my mother, “It’s really unfair; you have to do something about it,” and she said to me, “Don’t complain; just do what you are told. Don’t stand out.
It really upset me at the time and still does today, thinking about it. This highlighted the difference in culture. As I navigated my way through a predominantly Western work culture, I worked quite deliberately to think differently and learn skills that would help me in this type of work environment.