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The Challenge of Analytics Integration

How Society Insurance adopted analytics software to improve service for its workers' compensation policyholders.

Prior to its implementation of analytics software, Wisconsin-based workers’ compensation insurer Society Insurance faced challenges in pricing and customer experience for its customer base. To improve processes, it sought to maximize its use of policyholder data through predictive modeling. 

“There’s a tremendous amount of activity in analytics in insurance,” explains Matt Josefowicz, managing director at Novarica. “In terms of usage in small business, that’s definitely a focus area since small business has relatively tight margins.”

The insurance industry has traditionally focused on understanding data so as to deliver the best underwriting and pricing services. Large insurers have been making more investments in analytics, notes Josefowicz. Although smaller carriers have difficulty keeping the same pace, he says, it is easier for them to operationalize their results.

This is precisely what Society discovered through its implementation of predictive modeling software from Valen Analytics, which it began using in 2011. Prior to investing in the Valen solution, Society relied on its underwriters to differentiate risk. It was often difficult to explain processes and pricing details, such as loss control, to its niche base of policyholders, many of whom are small business owners.    

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“We really specialize in trying to get to small customers,” explains Dominic Weber, VP and actuary at Society. The goal, he says, is to look at modeling as a means of identifying policyholders and the key ways to make their businesses more successful. This includes better claims handling, accurate pricing upfront, and better understanding of how to market services and audit.

Prior to the implementation, Weber says, there were challenges in convincing employees that the analytics solution would prove beneficial. Underwriters were concerned about losing their authority and agents had to learn that the solution would ultimately help their customer experience efforts.

“Everyone has to buy into the fact that the model is going to give them good information about the policyholder,” he explains of the cultural shift. “It was a big educational process. The solution was not taking away authority, it was going to give more guidance.”

Employee pushback is a common issue with big data or predictive modeling programs, says Jeff Goldberg, Novarica principal. This is especially true for organizations that frequently have direct interaction with clients and great familiarity with their region. To remedy such situations, he suggests sitting down with the employees who will use the system and demonstrate the added value so as to receive immediate feedback.

Since the Valen system went live, Society has a better understanding of which customers need more attention and which need further explanation of the differences between various kinds of policies. Watching how underwriters and agents use program guidelines helps to increase profitability and allows the insurer to provide lower rates.

“Because of Society’s background as a mutual insurance company, we are invested in our customers for the long term,” says Weber.   

 

Kelly Sheridan is the Staff Editor at Dark Reading, where she focuses on cybersecurity news and analysis. She is a business technology journalist who previously reported for InformationWeek, where she covered Microsoft, and Insurance & Technology, where she covered financial ... View Full Bio

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