Memories of Manual Rating
Karlyn Carnahan | May 05, 2014
Once upon a time, long ago when I was an underwriter, we manually rated risks—yes, on a napkin—and quoted them over the phone. Risk assessment was based almost completely on an underwriter’s individual judgment which for me, as a new underwriter, was relatively uninformed. To become a really good underwriter, you had to have been in the job for a long time, which gave you plenty of opportunity to see lots of risks and hone your judgment.
Today, carriers rely heavily on data and analytics to support the underwriting analysis. Predictive models and multivariate rating plans are used by most personal lines carriers and more and more heavily by commercial lines carriers. Carriers are developing more sophisticated models that tap into massive amounts of data that simply weren’t available even five years ago.
Most carriers, upon launching a model focused on risk assessment and pricing, see significant lifts in their loss ratio, which is why managing data and using analytics in the underwriting process is one of the capabilities most desired by carriers today. Large numbers of carriers have data and analytics as key initiatives and are focusing on building the infrastructure necessary to manage and store huge quantities of data—both structured and unstructured and both internal and external.
But having capabilities for managing data isn’t just about the storage, the schemas, or the reports. Carriers are looking for key people who can analyze this data. Typically these are individuals with masters degrees or PhDs in math or statistics. The best also understand insurance and a sense of curiosity is a requirement.
Some call these individuals data scientists, some call them data masters. Some have even called them “caped superheroes.” Whatever you call them, there don’t seem to be enough. Data enabled risk assessment is here to stay and relying on personal expertise in some lines has faded like my memory of manual rating.
While colleges are producing more students majoring in the STEM areas, it is still a difficult task to find those with the right skill set and experience. Forecasts of the numbers of graduates still pale in comparison to the forecasted need. As a note, 33 percent of all graduates with engineering degrees are foreign born, as are the 27 percent of graduates in computers, math, and statistics, according to U.S. News & World Report. Some are concerned that reported abuses of the H-1B visa system may lead to increased restrictions on hiring foreign-born workers.
What does this mean for carriers? If you’re recruiting from colleges, you may need to hone up on the legal requirements for hiring foreign born workers. You may want to invest in efforts to encourage more students to major in the sciences in order to fill the future pipeline. Many carriers are investing in extensive training for their own employees to build the skill internally.
The good news is that software supporting this kind of detailed analysis is becoming more robust. Carriers can do much more with vended packages today than they could in prior times. There are firms that provide specific services in building and managing predictive models and scores. And some consulting firms are building centers of excellence around data modelling.
With a wide variety of tactics available, it’s time for carriers to begin assessing their strategy for bringing the required capabilities in house.
(Karlyn Carnahan is research director for Celent.)
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