In fact, primary insurers may not be aware that they could be paying more for their coverage because of missing information.

Nearly all reinsurers (90 percent) said they apply surcharges to compensate for data quality deficiencies. Among these, 70 percent said they would include a 20-25 percent premium penalty.

What's more, 92 percent of reinsurers said confidence that the cedant did use strong controls when collecting data could mean premium credits between 5 percent and 15 percent. More than one-third said they would be willing to offer a minimum 10 percent premium credit for cedants with high quality data.

This could mean big savings for insurance buyers. For example, a large insurer purchasing $10 million of reinsurance could receive $1 million premium credit on a buy--or more capacity, she said.

One reason reinsurers apply surcharges rather than going back to fill in the data is that "reinsurers renew a lot of contracts at the same time," during Jan. 1 renewals, for example. "They have a lot to do at once and may not go through the smaller accounts as thoroughly," Ms. Conway said.

Ms. Conway added that supplying better data would benefit the primary insurer's underwriting process as well. She said critical information, such as location and number of buildings, could be better gathered with specific data fields.

The study found that more than half of reinsurers currently use sophisticated tools to evaluate the accuracy of exposure data received in broker submissions, and 83 percent have basic checks in place confirming that the most critical data fields are populated. When asked which data items they consider most problematic, reinsurers pointed to insured values, complete inventory of locations and secondary characteristics.

How would reinsurers be convinced the data was, in fact, high quality? With the input of a third party, such as a catastrophe modeling company, or actuary--with the insurance broker being the last choice, she noted.

When analyzing the potential risk of a cedant with poor data quality, a majority (58 percent) of reinsurers said they directly modify their catastrophe model results, while the remaining 42 percent of respondents make upward adjustments to the data before running their models, the study found.

To help boost their confidence level in data quality, the vast majority of reinsurers (92 percent) agreed that if the cedant used strong collection, enhancement and data maintenance controls, the risk would be more attractive to them.

While it's an important consideration, catastrophe modeling is somewhat limited in that much recorded data for extreme events, such as hurricanes, only goes back about 100 years.