Your career followed a “textbook” path. You started as a trainee at a large life company, gradually took on larger and more complex cases, transferred to an “impaired” risk unit and then moved to a reinsurer where you underwrote facultative submissions. When did your career diverge from this traditional underwriting path?
About eight years ago I took a lateral transfer to work in underwriting research. I’d been working as a line underwriter for 15 years, and this new opportunity intrigued me. I went from underwriting individual risk to analyzing blocks of business and then to protective value studies on new data sources such as clinical lab data. It’s a totally different experience.
There’s a misconception that production underwriters can readily transfer from line underwriting to R&D. There was a steep learning curve for me in the beginning. I needed to have a strong foundational knowledge of mortality tables, data analytics and predictive models.
My role changed from working independently to having significant collaboration with others including actuaries and data scientists. Having said that, knowledge of traditional underwriting tools and requirements is an essential asset to bring to an R&D team that’s exploring the value of traditional underwriting evidence. We must understand where we’ve been to understand where we need to go.
What were your first underwriting R&D projects?
When I first joined the R&D area, we were working on a model to predict preferred risk class distribution given a set of preferred guidelines. The team was analyzing and manipulating blocks of business to better understand what was driving the mortality. Mortality experience can vary a great deal across companies that have similar products and underwriting guidelines. This was an important realization, and in some ways, it helped to open our minds to new ways of selecting and classifying risk. The old ways were not necessarily always right.
Serious investment in alternative underwriting initiatives took hold a few years ago. What were the main causes for such an industry-wide trend?
“Faster, simpler, better” became the mantra for all functional areas from products and distribution to underwriting and administration. But the mantra really took off in underwriting, because many in the industry had come to see traditional underwriting as the biggest obstacle to new sales. And with advances in technology and the phenomenal growth of digital information on individuals from third party data sources, underwriting was ripe for a change.
What is the difference between automated and accelerated underwriting or are they one and the same?
There is considerable confusion in the industry regarding these terms. Although they often are used interchangeably, they fundamentally mean different things. In the simplest terms, automated means no human underwriter involvement is needed. For example, SCOR’s Velogica is an automated underwriting engine.
Accelerated underwriting may or may not include automation. It involves a “subset” of fully underwritten business where fluids are waived, based on rules or a predictive model. This “subset” of low risk applicants is “accelerated” through the underwriting process without fluids (blood, urine, vitals), because they are determined to have no significant medical conditions that would prompt the need for requirements such as fluids. And, it’s important to note, the premium is the same as those that are fully underwritten.