OneBeacon loss-ratio improvement aided by SAS® Analytics
OneBeacon Insurance Group improved its loss ratio by two to four points using SAS Analytics to price insurance products accurately in several of its specialty segments. Analytic know-how gives the company the confidence to continue expanding in the sometimes difficult-to-price specialty insurance market.
OneBeacon, based in Minnetonka, MN, offers a range of specialty insurance products sold through independent agents, regional and national brokers, and wholesalers. The company's specialty businesses include insurance for professionals, collector car and boat owners, third-party general liability policies, marine and specialty property policies.
Project nets 10 times ROI
"This project netted 10 times the return on investment. It was an important early win for predictive modeling at OneBeacon. Projects like this, using SAS, typically return four to 10 times the return on investment,'' Lehmann says.
SAS® Enterprise Miner™ speeds the process
It also shaves weeks off the time it took to build models. "We are actively working to shift model building and predictive modeling into our day-to-day work,'' says Lehmann.
The company had other choices to enhance its analytical capabilities but felt that SAS provided greater flexibility, functionality and ease of use.
Pricing specialty insurance
Compared to single-family homes, items like yachts, collector cars and cargo ships are rare. So much less data exists on claims. And OneBeacon's subsidiaries and units capture data in unique ways using different formats.
The specialty lines also need different types of third-party data – whether it’s commercial credit scores, ZIP code information or data on where a doctor or other professional attended graduate school.
"We get a sense from the business experts what might be useful. To supplement our data, we've used US Census data and credit agency reports from companies like Dun & Bradstreet and Experian,'' Lehmann says.
Predictive models are relatively new tools used for underwriting specialty insurance – a practice that tends to require specific expertise and knowledge about the risks as well as the constantly evolving insurance marketplace.
"Many business users have embraced the models," he says. "I hear from model users that they're almost making a leap of faith to buy in to the results. They don't fully understand the statistical methods involved. SAS can help us more quickly answer questions about a model and build confidence in that model. We don't require internal users to adopt the models built by our research team, but so far we've gone forward 100 percent of the time.''
Next up: more effort to analyze claims
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.
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An early project netted 10 times ROI; loss ratio improved by two to four points; weeks shaved off time spent building models
“The models that we use and build with SAS give us a competitive advantage.”
Assistant Vice President for Corporate Research, OneBeacon