While there has been a lot of focus on new data elements that carriers can apply to advance underwriting, a number of carriers are also finding the opportunity to achieve significant improvements in reducing pricing leakage from inaccurate information by enhancing core data elements.

Pitney Bowes, for example, has a new geospatial solution that provides more accurate location information for personal lines and the ability to get individual latitude/longitude information for individual buildings for commercial. When compared against traditional point solutions, carrier tests have shown that it can be 10 to 20 percent more accurate than existing solutions and improve underpricing of location-based risks by five percent or more depending on the carrier’s existing GIS and pricing solutions. Select carriers are using this to create a pricing advantage over their peers.

Other carriers are starting to look at the accuracy of other data elements and are seeking options to boost these data elements by either using new third party data sets, using advanced analytics to layer data sets, or even exploring the use of Artificial Intelligence/Machine Learning to increase the accuracy of these core sets. This can include efforts to augment the precision, accuracy, and completeness of fire protection scores, building types, or other key classifications. In short, as important as new data elements are to consider, it is equally important to improve the accuracy of your existing data.

Improvement in any of the key factors that drive pricing and automation algorithms and rules will generate direct improvement to losses, expenses, and pricing accuracy. As more and more insurance is processed automatically, the criticality of accurate information only increases. This means decision-making on what data a carrier should purchase, how accurate and complete that data is, and what it costs, becomes a critical business element. Carriers need to transition this governance from being just a procurement or IT decision back to core underwriting management supported by data expertise.

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