Thanks to a compelling business case and a proven track record in banking and other industries, a growing number of forward-looking life insurance companies have embraced predictive modeling and are seeing tangible benefits from initial investments in new tools and processes.
To date, predictive modeling in the life insurance industry has primarily been considered for underwriting because of the cost savings and increased speed-to-issue. However, it’s now gaining traction in other areas and functions, such as sales and marketing programs, where the value proposition is similarly clear. If the experience of the property and casualty (P&C) insurance sector is any indication, the growing adoption by life insurers should come as no surprise. Indeed, there is a pervasive sense among life insurers that predictive modeling’s time has come.
To differentiate themselves from their competitors, some insurers turned to automated underwriting as a means to reduce costs and streamline these processes. Straight-through processing and electronic interfaces for policy applications eliminate the need for costly and error-prone manual data entry. Furthermore, automated underwriting can utilize electronic applications with “drill-down" capabilities. For many applicants, this means fewer questions and a faster application process. Workflow tools and business rules further streamline the process by automating the ordering of medical tests and by recommending approval of (and in certain instances actually approving) applications without underwriter involvement. Such underwriting systems have the added advantage of being able to automatically assign tasks and cases to the right underwriter at the right time, based on current workloads and resource availability. These capabilities allow underwriters to focus on the risk assessment of borderline cases.
Predictive modeling should be viewed as the next step beyond automated underwriting. Specifically, it streamlines and optimizes underwriting decision-making by applying business rules and enhancing traditional medical underwriting information with external data. This rules-based approach extends the process efficiency gains and produces underwriting decisions in a faster time frame and within a company’s existing underwriting infrastructure.
The value proposition for predictive modeling for life insurers starts with more efficient underwriting.
Life insurance companies ready to explore predictive modeling should consider the necessary steps, which have been proven enablers for smooth, efficient deployments at P&C carriers, as well as the leading-edge adopters in the life insurance sector.