So far this year there have been six climate and weather-related disasters with losses over $1 billion in the United States and this was before the totals for Carr Fire and Mendocino Complex Fire have been tallied. While these disasters were all different they all caused extensive damage to homes and infrastructure. These events not only have a significant economic effect on the areas impacted but on the insurance companies who cover those affected. A recent study by Perr & Knight, explored how insurers can be negatively impacted by insurance data discrepancies and how different data sets can impact premiums.
The study researched how insurance premiums compare using data sets such as zip codes and street segments, versus parcel-level data which is more precise. Accurate data ensures that premiums are calculated correctly, which is imperative for insurers navigating an increasingly complex risk landscape and competitive marketplace. When pricing was inaccurate, in five to ten percent of cases, premiums were significantly under or overpriced. The study found that some homeowner policies were underpriced by 86.7 percent or $2,800 annually, causing losses for insurers. While this may seem like a small sum, this study analyzed only 100 cases – imagine the financial damage that could fall upon a large company with thousands of accounts that are mischarged.
While inaccurate data results in routine losses for insurers, the impact of inaccurate data for these large scale disasters is magnified on the bottom line. For instance, one of the top ten U.S. insurers found that only three percent of properties that made claims following the 2017 California wildfires, had been identified as high risk for fires based on zip code data. This large number of unanticipated claims cost the insurance company over $100 million dollars. However, by investing in hyper-accurate intelligence location solutions insurers can gain a better understanding of their customers’ risk exposure and write policies accordingly.
Dan Adams, vice president of data product management at Pitney Bowes, recommends insurers use more precise data sources to be confident in the data quality and decisions based on data sets. When choosing data sets, it’s important to “make sure everything—when you bring the two data sets together—behaves the way it should, and that they’re both accurate and precise in relation to each other,” says Adams.
Insurers need to regularly monitor and maintain their data streams to ensure accuracy. Using pinpointed data to determine premiums can help insurers save money and provide the best coverage in a competitive industry. With hundreds of options for coverage, insurers must use the data they have to correctly price policies, benefiting both the customer and the company itself.
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