Transforming the insurance claims life cycle using analytics

Let’s be honest, the financial results of most insurers in 2011, especially Property & Casualty,  were….well disastrous. Higher combined ratios were predominately a consequence of increasing loss ratios, hence improving the claims process must be a high priority for insurers. One way to do this is to use predictive claims analytics to optimize loss reserves, increase productivity and assist in preventing fraud.

Claims analytics is the process to analyze the structured and unstructured data at all stages in the claims cycle, from First Notice of Loss, to payout, through to subrogation. Rather than analyzing one case at a time - based on only the current information available - analytics gives you added perspective by allowing you to view this one claim “in context” –by comparing it with previous claims settlements in your database.

Previous Analytical Insurer blogs have covered how analytics can help with fraud detection, as well as claims recovery by improving the salvage and subrogation process. But claims analytics is not limited to these areas. Two other opportunities for claims analytics are in claims benchmarking and litigation management.

A major challenge insurers face today is the inability to accurately forecast the loss reverse and ultimately predict the outcome once the claim has been submitted. Using analytics, it is possible to calculate an accurate loss reserve amount and benchmark each claim based on similar characteristics, hence reducing the propensity for loss padding. For example, data mining techniques have helped insurers identify that the size of a claim payout grows significantly based on the number of days between when the claim occurs and when it's reported. In most instances the size of a claim can increase by approximately 50 percent if the insured does not report the claim within the first four days.

Finally, some insurers are beginning to use analytics to calculate a litigation propensity score. Claims that involve an attorney often double the settlement amount and significantly increase an insurer’s expenses. Analytics can help insurers determine which claims are likely to result in litigation, and mitigate those claims to more senior adjusters who can settle the claims sooner and for lower amounts.

Fortunately I am not a lone voice on this topic. I am looking forward to next week’s ACORD LOMA conference, which has sessions from Tower Group’s Karen Pauli on “Next Generation Claims Analytics: Five Areas Claims Executives Must Invest In” and Jose Trasancos from Utica Insurance talking about “Harnessing analytics for Claims”. While at the IASA conference in early June I will be speaking with FINEOS on “The Future of Claims-Optimizing Outcomes”.

For those of you unable to attend these conferences and would like more information about claims analytics then download this white paper on “Predictive Claims Processing”.

I’m Stuart Rose, Global Insurance Marketing Manager at SAS. For further discussions connect with me on LinkedIn and Twitter.

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