The commercial-insurance industry may soon find itself in two camps: companies that adopt and utilize analytics and gain profitable market share by creating positive risk selection, and those who do not use analytics and suffer from adverse selection, a recent report says.

In its “2014 Outlook: Commercial Lines” report, Valen Analytics says commercial insurers are in a similar situation that the credit-card industry was in during the late 1980s and 1990s. In that time, Valen says, “new marketing and risk-assessment strategies fundamentally changed the credit-card industry. Technology and information-based companies like Capital One flourished and garnered significant market share while those that clung to traditional methods floundered.”

Valen sums up its point by asking, “The agent of change [for the credit-card industry]? Analytics.”

Valen says insurers that eschew analytics risk being saddled with poorer-performing risks “because they are working with outdated pricing and risk-assessment strategies.”

For insurers that commit to analytics, Valen says it is more than just adding tools, but also a change in organizational philosophy. “Implementing sophisticated analytical tools within underwriting requires a commitment to becoming a more analytically driven organization,” explains the report.

C-level commitment represents “the most important first step,” Valen says. CEOs, the report adds, typically want to know:

That the predictive model works.
That predictive analytics will drive meaningful and demonstrable benefits—for example profits, loss-ratio improvement and pricing accuracy.
The implications to the organization—IT impact, changes to the underwriter workflow, how underwriters will be trained and implications to key stakeholders such as agency relationships.

Once the organization buys in, Valen says subsequent considerations are what data assets to use to build a robust predictive model, how information will be consumed within the organization and incorporated into the underwriting workflow, and how predictive scores will work “in synergy with the expertise your underwriters bring to the table.”

Regarding the data to be used, Valen notes that selection bias can occur when predictive models are built solely on a carrier's own data. “Your company has a risk appetite along with risk selection and underwriting guidelines that help define your profile of business,” says Valen. “Therefore, if you build a model on your data alone, the dataset will only include those policies that your underwriting practices selected and, by definition, does not represent the entire market.”

Valen notes that carriers may use third-party data or leverage data consortiums to supplement their own policy data.

Special Reports /

Valen: Commercial Insurers at Analytics Crossroads

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