Q&A with David Moore, FSA, MAAA and Senior Technical Director of Life Insurance Analytics and Predictive Modeling at Nationwide Financial
How did you get started as an actuary and how did you begin working with predictive analytics?
I had a pretty traditional beginning to my career. I went to the University of Waterloo to pursue a math degree and became interested in actuarial science. After I got my FSA, I started my first job as an actuary where I continued on the traditional path. At the same time, I was able to dip my toe into some consulting work and was introduced to predictive modeling for life insurance.
How is predictive analytics changing the life insurance industry?
Fifteen to 20 years ago, when I was starting my career, being an actuary at an insurance company wasn’t seen as innovative. It was viewed as mathematically intensive, statistically oriented and business-focused. Insurance companies were big, old institutions, and what was most important was having a safe capital base and being able to pay claims. There wasn’t a focus on actively seeking better ways to engage with your customers. Just like most industries today, old industries – like insurance – have been disrupted; today we understand the power of the different elements of data and what we can do with it.
How has predictive analytics changed life insurance for customers?
While it's still not easy to buy a policy because there are a lot of steps for customers to take, insurance companies are trying to ease that process. At Nationwide, we have been very focused on improving the underwriting process and the customer experience using data and predictive modeling, and we've made great strides. For example, we’re now able to issue a percentage of policies without labs, which creates a better customer experience. That said, we still have a long way to go in enhancing that experience to make life insurance easier to navigate for customers – ultimately, we want the customer experience to look and feel more like the everyday technology someone is used to working with, like a smartphone app. We're not there yet, but that's where we need to be.
Do you still use traditional actuarial skills in your work?
Traditional actuarial skills continue to have an important role in my work today. One recent example was trying to understand how to use an existing model to augment traditional underwriting methods. To do this, we looked at thousands of auto policies with their risk scores and used the data to perform a mortality study, just as a traditional life insurance actuary would do when setting pricing or valuation assumptions. Understanding that aspect of the process is still important. Actuaries understand mortality risk and the elements of it, as well as the way to do a mortality study, and that’s something you're not going to get from a data scientist who has a pure statistical background.
What other skills or information do you think are important as an actuary working in predictive analytics?
It's very important to understand the business context of the data and the industry. As actuaries, we don't want to just build a model for the sake of building a neat, shiny tool. It's really important to understand the problem you're trying to solve so that you can choose the right method to solve it.
Communication and interpretation skills are also necessary skills for actuarial and non-actuarial jobs. Whether you're looking at an underwriting model or other models around how you structure your business and making data-driven decisions versus a current methodology, there needs to be a plan for how you communicate that.
More than ever, people want to feel like they’re doing good work and that their work is valued. What about your work is exciting and what makes you feel valued in your organization?
The simple answer is that predictive analytics is innovative, and it’s exciting to work with such an innovative process and set of tools. I get a chance to do something new and different every day. While that is really exciting, it’s also a little scary.
It's also fulfilling to work on something that has a major impact on an organization of 30,000 people. I'm fortunate to work at a company that values innovation, customer engagement and employee engagement. My employer wants its employees to be doing their best work and have an impact on our customers and the industry.
What would you recommend to a prospective actuarial student or someone looking to get into predictive analytics?
There are many resources to take advantage of that are little to no cost. There is a wealth of information on the SOA's website, and the SOA is now offering a predictive analytics certificate program to give students and working actuaries hands-on experience with some of the key tools in the industry. That’s going to be very, very powerful.
What advice would you give to someone interested in predictive analytics who works for an organization that isn’t currently doing this kind of work?
The first step to introduce predictive analytics is that you need support from the top down. Innovation can start at the bottom, but you need someone in an executive role championing that from above. If you don't already have that kind of support, then I would advise you to talk to your leadership, talk to your boss and make some noise about what other companies are doing with predictive analytics.
To me, predictive analytics is about trying to make data-driven decisions throughout your organization—not just in one aspect. To yield the best, most effective results, it’s important to make sure the entire organization is aligned strategically.