Global Insurance Services Executive Brief Spring 2013
Predictive Analytics and Reengineering Combine to Improve Catastrophe Claims Results
Super Storm Sandy is the latest reminder to the industry about the need to rapidly and appropriately manage claims in a CAT loss environment. According to the January 2013 report by the Property Casualty Insurers Association of America, Sandy involved nearly 5,000 adjusters, produced an estimated 385,300 claims and $20-25 billion insured loss for the entire storm.
With tens of thousands of claims being reported daily–ranging from auto and very small homeowner matters to multi-million commercial claims–insurance companies were faced with the overwhelming task of not only satisfying their customers, but also addressing myriad requests from investors, regulators and the media.
While traditional CAT models are highly valuable tools that continue to improve, they paint with a broad brush and do not capture some elements of behavior in specific events that can affect losses from a particular event.
Fortunately, by using predictive analytics, even with limited claims information, it is possible to quickly synthesize and analyze loss data at any time during the claims process, greatly improve claim outcomes, and more accurately predict the ultimate total claim payment. This valuable information helps prioritize claims, guide the claims analyst when negotiating settlements, set appropriate case reserves, and provide the claims department with insights into process efficiencies, among other applications. The aggregate results of this data can also be used to help the insurers quickly determine estimates of total aggregate losses attributable to the catastrophic event, thus providing enhanced information to senior management.
Also, efficiencies gained in the claims process can lead to improved customer satisfaction and retention, and help insurers outperform expectations in difficult catastrophe circumstances such as Sandy.