How we used data technology to save 22% on underwriting costs.

The ask

After building and launching a new digital term insurance product, our carrier partner wanted to further leverage our data technology to enhance cost efficiency. In short, could we employ our marketing data technology stack to lower both underwriting and per-submitted-application costs?

‍

The solution

Using what’s called “narrow AI,” we built a sophisticated data model designed to determine one key variable about incoming traffic: How likely is a given customer to be approved for a product? This data model became part of a product we call Recommendation Engine, which took just about one month to implement for our partner. Here are some of the benefits:

True “real-time” data

Previously, carriers would have to process data in daily or weekly batches. That meant not knowing who was on their website or interacting with their channels and products until days or weeks later. Bestow’s Recommendation Engine can process data and serve results in milliseconds — both for automated outcomes like recommending a product or for operational outcomes like helping marketers target the right customers.
‍
In real-time, Recommendation Engine will serve an applicant a data-backed, tailored experience. For example, based on information gathered at the quote stage, a customer may be ushered through an instant underwriting flow, an in-house agent flow, or off-boarded to a third party partner to explore product options.

‍

Guiding customers to the right products

For this carrier, Recommendation Engine means instant pipelining to help guide the right customers to the right products. With only five customer-provided data points, we can predict with up to 83% accuracy how likely a visitor is to be approved and ultimately to make a purchase — and because the data model is self teaching, it will become even more accurate over time.

Turning cost into revenue

These robust data models, along with strategic third-party partnerships, allow this carrier to instantly and automatically direct customers toward products that better suit their needs. Translation, potential costly declines can become revenue in the form of lead generation fees — all while providing better outcomes and experiences for customers. 

The results

The layering of Recommendation Engine over the carrier’s new digital D2C product has had a tremendous impact on the bottom line. By analyzing traffic in real time to determine product fit and propensity to be approved, they’ve seen a 22% reduction in per policy underwriting costs and a 22% decrease in costs per submitted application. Perhaps just as impressive as these cost efficiencies, Recommendation Engine is also generating a 5% lift in approval rate while only being served to 20% of total traffic.