Analytics Emerge All around the Enterprise
Insurance Networking News, August 1, 2009
While the biblical injunction against envy is unequivocal, inside the walls of an insurance company a little envy may be a good thing, especially when a successful implementation of a technology in one department spurs its deployment elsewhere in the enterprise.
This case for coveting seems particularly strong in the instance of business analytics, which often enters an insurance company in one department only to spread elsewhere. While relatively established in claims and underwriting, analytics is also finding fertile ground in areas such as marketing and agency management. Insurers, it seems, have analytical needs across the enterprise.
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Such was the case in Fond du Lac, Wisconsin, where Society Insurance purchased an analytics solution to help with underwriting, but is now beginning to deploy it in its claims and marketing departments, says Rick Parks, the company's SVP and COO. Parks relates that the company selected a solution from Denver-based Valen Technologies with this type of flexibility firmly in mind. "We were interested in having a solution that could transition into other roles in the company," he says.
Dax Craig, president and CEO of Valen, says he sees analytics spreading from traditional uses such as fraud detection and underwriting into areas such as premium audit.
However, Craig cautions carriers that just because an analytics solution worked well in one part of an enterprise, they shouldn't expect instant results elsewhere, especially if relevant data is wanting. "If you don't have the data, you don't have a model," he says. "We can only help solve a problem if we can get a statistically and actuarially defensible amount of data."
Parks also cautions that companies looking to spread analytics throughout the enterprise are in for some work. "We realized that this was going to be quite an investment in time, resources and funds," he says.
A NEW DEPARTMENT
Neeraj Arora
Few people realize the effort necessary to spread analytics across the enterprise more than Neeraj Arora, director of Personal Lines Insight & Innovations, at Los Angeles-based Farmers Insurance Group. The department was founded two-and-one-half years ago to drive value to other parts of the organization. "At a very tactical level we are trying to spread analytics around the organization department by department, or even project by project," he says.
In an organization as big as Farmers, such an effort entails gathering, collating and make sense of billions of bits of data in order to create predictive models. One area Farmers is seeing results is in marketing, where it uses response rates from previous marketing efforts to populate a predictive model that calculates the likelihood a consumer will buy a policy based on a certain piece of mail sent to them. Thus, the models helps narrow down the mailing list, Arora says.
Carriers can further zero in on a target demographic by including other common predictive variables such as age, credit information and marital status, says Stuart Rose, marketing manager, Global Insurance, for Cary N.C.-based SAS Institute Inc. "Analytics can realize what those traits are in people who will need more insurance and tailor marketing efforts to them," he says.
Arora says the departments can now apply scientific rigor to decisions previously made on tradition or gut instinct. "Where statistical analysis can help you is by telling you if you're gut instincts are really true," he says.
Such insights may seem counterintuitive, and elicit incredulity from business users long accustomed to doing things a different way. Accordingly, proponents of analytics need to constantly judge the effectiveness of existing models, adjust where necessary and be wary of statistical anomalies. "We always have an independent test area to see if one model is truly working or if it's just picking up on an abnormality," Arora says.
THE HUMAN FACTOR
Not discounting the many technical issues facing widespread analytics implementations, the winning over of skeptical business users may be one of the thorniest ones. Arora uses a "show, don't tell" philosophy, where analytics gains a small foothold in a department, and can show business users, based on samples, the value of the models. "We have to continuously deliver results and prove that things are working," he says. "Our grand vision is to have not one department untouched by analytics, and to get business users to see value maximization potential both for the organization and the customer."
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