Briefs

November 1, 2008

Of all the processes inherent to insurance, none produces such a voluminous amount of disparate data as claims. Insurance Networking News asked Stuart Rose, global insurance marketing manager at Cary, N.C.-based SAS, how claims analytics can be used to unlock efficiencies in the process.

INN: Why apply analytics to claims?

SR: Claims analytics is the ability to analyze claims data at each stage in the claims cycle, from entry of first notice of loss through to payout, to make the right decision at the right time to the right party. Rather than analyzing one case at a time-based only on the current information at hand-analytics gives insurers added perspective by allowing them to view claims "in context" by comparing them with previous claims settlements in their database.

INN: How can analytics improve the claims process?

SR: Predictive analytics can enhance the claims process in multiple areas such as optimizing claims settlements, improving loss reserving, preventing fraud, discovering unforeseen subrogation opportunities and better resource allocation that ultimately leads to increased customer satisfaction.

One of the challenges insurers face today is the inability to accurately forecast the loss reserve and, ultimately, predict the outcome once the claim has been submitted. By applying time series and econometric analysis, it is possible to calculate an accurate loss reserve amount and benchmark each claim based on similar characteristics and, hence, reduce the propensity for claims padding.

By implanting data mining techniques to cluster and group loss characteristics, claims can be scored, prioritized and assigned to the most appropriate adjuster.

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