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Transforming Underwriting

MARCH 2004

TRANSFORMATION OF UNDERWRITING FINANCIALS

The same three technologies, business rules engines (BRE), predictive scores and data management, can also transform an insurer’s underwriting profitability.

As each step in the underwriting process becomes automated, the percentage of applications which reach an “accept and rate” or “reject” decision with little or no human involvement can increase, for example from under 50% to 80% or higher. The number of underwriter and support staff needed to process a given number of incoming applications could drop from 20% to 40%. That will reduce the “underwriting expense only” ratio (as the numerator decreases). Alternatively, an insurer could maintain the level of underwriting staff to handle an increased premium volume—which will have the same effect.

Several factors will also drive down the numerator of the loss ratio (losses and loss adjustment expenses). Accurate predictive scores will produce accurate rates—an applicant with an expected loss of US$300 will not be charged a premium as if the expected loss were US$200. Correct rates will drive away previously subsidized poor risks. The consistency of rules-driven scores and pricing decisions has another subtle but important advantage. Any mis-pricing of a given group will appear more clearly, thus enabling earlier corrective action.

Premium (the denominator of the expense and loss ratios) will increase for reasons described above: the insurer’s market reach and appetite has increased, and it is a more attractive business partner for its agents. Retention of improving risks will also increase, as many policyholders become better risks over time. A rule for renewing policies can call for new data which generate a better predictive score, yielding a lower rate and higher retention.

Lastly, more fine-grained rates will allow an insurer to improve its premium/risk profile. For example, an insurer begins selling to a new market segment which it formally would have rated based on an expected loss of US$300. Now with better predictive scores, it knows that group’s true expected loss is US$250. The insurer can begin selling to the group, charging a premium based on an expected loss of US$275 -- lowering premium some, but losses even more.

Celent estimates that, taken together, these changes could reduce the average personal lines insurer’s “underwriting expense only” ratio by about 30% (0.6 combined ratio points) and reduce the loss ratio by 2% to 4% (1.5 to 3.0 combined ratio points).

© 2004, Celent Communications. Authorized reproduction permitted.

www.celent.com

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