This is the second blog in a series on Predictive Modeling in Life Insurance. Read the first blog here.

Over the years, life insurers have adopted—and adhered to—certain practices because “they have weathered the test of time,” or because “we have always done it this way.” But an approach that may have been a best practice 10, 20 or more years ago is not necessarily one today. Nowhere is this truer than in the realm of life underwriting.

Consumers Today Are Different

Consumers today are no longer willing to wait weeks or even months for their life insurance policy to be issued. Technology has raised their expectations regarding the speed at which they expect their purchases to be delivered. For them, life insurance is no different.

But despite the changing expectations of today’s consumers, the challenge for insurers remains the same: how to meet those demands, while still appropriately assessing risk.

A Different Approach

Some life carriers that wish to remain competitive have started revising their underwriting best practices to adapt to the demands of today’s fast-moving marketplace. One way they have accomplished this is by leveraging technology and valuable tools—such as real-time data and advanced analytics—to help assess the risk quickly and effectively. To do this, they have adopted a change in approach: from causation-based underwriting to correlation-based underwriting.

Causation- vs. Correlation-Based Underwriting

Life insurers have traditionally assessed risk by evaluating a proposed insured’s medical history to help the insurer “predict” whether that individual poses the risk of an early death—since various medical conditions are viewed as the “cause” of premature death. This is the causation-based underwriting approach that insurers have used for decades. The problem with this approach? It’s time consuming and doesn’t paint the full picture of the proposed insured, since many components of an individual’s lifestyle may not be taken into consideration.

There’s another approach that some insurers have started to adopt in life underwriting: correlation-based underwriting. This approach involves the use of FCRA-compliant data—including public records, motor vehicle records and credit records—which together can be predictive of relative mortality risk. And such data can become available to the underwriter in seconds or minutes, rather than weeks.

The type of data used in correlation-based underwriting is available through products such as LexisNexis® Risk Classifier, which provides life insurers with relevant data and an instantaneous view of the proposed insured.

So, why are so many insurers turning to correlation-based underwriting?

  • Traditional health-based underwriting elements (such as labs, paramedical exams, Rx scores) are useful in assessing risk, but only tell part of the applicant’s story.
  • Different elements of LexisNexis Risk Classifier can be predictive of relative mortality risk—and can effectively supplement (and in some instances, even replace) traditional health-based life underwriting.
  • This shift in underwriting approach (from causation to correlation) has been widely practiced within other insurance industries (P&C, Commercial, etc.) for many years. It’s only relatively new to the life
  • Correlation-based underwriting is quickly being adopted by today’s most innovative insurers. Adopting a “correlation” perspective in life underwriting can enable carriers to price policies more accurately, improve risk posture and drive efficiency.

The bottom line? Adopting a “correlation” perspective in life underwriting can enable carriers to price policies more accurately, improve risk posture and drive efficiency.

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