((Downtown Newark, location of Prudential Financial. Photo credit: Fody.)
As consumers have grown habituated to rapid digital transactions, it has become increasingly urgent for life insurers to sell their products much more quickly and easier for applicants and producers. Prudential’s (Newark, N.J.) data scientists set about a way of doing so in 2016, and in close collaboration with business leadership have delivered PruFast Track. Powered by Prudential’s proprietary Risk Assessment Mortality Model (RAMM), PruFast Track is able to accelerate up to a third of life insurance applications, with 80 percent of those being fully underwritten within 48 hours—as opposed to over 30 days.
Since its debut with the company’s captive sales force, Prudential Advisors, in the third quarter of 2016, PruFast Track has since been rolled out to a number of the company’s external distribution partners. To date, the analytics-driven underwriting capability has accelerated more than 10,000 cases without examinations and invasive tests. Prudential’s Individual Life Insurance division uses PruFast Track for its entire Single Life portfolio.
Integrated into the Existing Process
“We’d been looking for ways to make customer experience better by giving applicants a quicker decision, and we set about it quietly, without much fanfare, starting with Prudential Advisors and branching out from there,” comments David Goldberg, VP of Data Analytics in Prudential’s Customer Office. “We’re not asking them to do something different—PruFast Track has been integrated into the existing process.”
The reason that was possible is that Prudential’s data scientists leveraged the power of the company’s existing life underwriting expertise and combined it with more powerful technology. RAMM was built in the first half of 2016, using machine learning and massive distributed computing power.
RAMM emerged through a working group, led by Goldberg’s team, which initially built what he characterizes as a very rough preliminary model. After the team built the conceptual model, the Life business built an execution plan. It all came together fairly quickly,” he says. “As a data scientist, there’s nothing you like more than a business partner ready and willing to implement.”
New Insights From ‘Vast Experience’
“The perhaps under-appreciated magic here is the vast experience Prudential has in writing life insurance policies,” Goldberg remarks. “We’re able to take a volume of data, apply greater statistical analysis and computer power and draw another level of insight out of it.”
The existing level of insight is by no means an end point. Goldberg stresses the iterative nature of the initiative, starting with an update schedule. After PruFast Track’s initial rollout with Prudential Advisors, Goldberg’s team worked on the kinds of fixes and minor improvements expected following a pilot. Prudential plans to do updates to the underlying model about once annually.
PruFast Track has been very well received by producers and customers, according to Goldberg. “There’s been a lot of happiness in a lot of places,” he says. “Customers are happier because they have a better experience and a quicker decision; our Advisors are happy because they get quicker results—and they get paid quicker.”
The reception has been very positive from distribution partners as well for the same reasons and in part because the digital aspects of the process are part of a larger move away from paper-based processes. “When the outside advisor gets the same quality of experience—faster, with less effort on their part and a quicker payday, they’re happier,” Goldberg comments.
Avenues for Future Improvement
Goldberg sees further opportunities for improvement with the continued advancement of the digitization of the process and the provision of new tools to advisors. The underlying analytic model itself also presents avenues for continued improvement, he emphasizes. There are ways to improve the model and to driver higher numbers of accelerated cases,” he elaborates. “There will be ways to broaden the application, obtain data points through different methods and sources, and also ways to streamline the process and continue to improve the customer experience.”