High-Quality Exposure Data Is the Key to Effective Catastrophe Risk Management

by George Davis, Vice President, AIR Worldwide and Bill Raichle, Vice President, ISO Risk Decision Services

As Hurricane Ike approached the Texas coast in September, catastrophe modeling experts were concerned that underinsured properties might undermine insurers’ abilities to assess losses in real time. Now, months later, underinsurance does seem to have been an issue, as some individual insurance companies are expressing concern that their own modeled loss estimates may come in below actual claims.

Companies rely on catastrophe models to provide reliable estimates of loss, whether for purposes of managing risk over the long term or for understanding their loss potential in real time as an actual event unfolds. However, the reliability of model output is only as good as the quality of the exposure data used as input. Of critical importance is building replacement value, a major driver in catastrophe loss estimation and the likely culprit of possible underestimated Ike losses for individual companies. However, replacement value is just one of many property-specific data elements that need to be accurately gathered.

High-quality exposure data is essential for effective catastrophe risk management, improved underwriting, and reinsurance decisions. Raising the visibility of the issue still further, rating agencies are now requesting companies report on the status of their property-specific exposure data. The latest A.M. Best Supplemental Ratings Questionnaire asks companies if catastrophe exposure data, such as geocoded location, construction, occupancy, year built, and other data, are specified for individual risks.

The Collection of Quality Exposure Data Should Start at the Point of Underwriting

A recent AIR Worldwide survey indicated that exposure data quality is a significant issue for property insurers concerned with catastrophe risk. A majority of respondents attributed poor data quality to inadequate practices at the point of underwriting. Those inadequate practices, in turn, were blamed on pressures to conclude a sale quickly and a desire to be seen as “easy to do business with.” While such practices may help companies win more business initially, basing underwriting and portfolio management decisions on poor-quality exposure data may place them at a competitive disadvantage down the road.

Prefilled Property Information Enhances Data Collection

Although insurers have new and strong incentives for enhancing exposure data, the question as to how to do so cost-effectively and efficiently remains. One option quickly gaining popularity is the use of prefilled property information at the point of sale. Widely used in auto insurance, prefilled data lets brokers, agents, and customer service representatives confirm data rather than collect it from scratch.

For residential properties, much of the data necessary for catastrophic risk assessment is available in public records — and is therefore easily available as prefill. However, for commercial lines, public-records data alone is not sufficient for several reasons, including the difficulty in linking public-records data to individual buildings.

A more reliable approach to commercial property-specific information is data based on actual on-site building inspections. Basic building data available today includes construction type, building size, occupancy, fire protection, property replacement value, geocoded location, and other data.

Portfolio-Level Exposure Data Has Opportunities for Improvement

Yet most of the premium dollars collected come from properties already on the books. Because of data improvement challenges for existing risks on the book or ineffective methods to transfer data within a company, portfolio-level exposure data can be of poor quality even if the individual risk underwriting at the point of decision used good data. As such, portfolio-level catastrophe analyses are often run with outdated or limited data, impacting the reliability of results.

Options for companies to validate and improve portfolio-level exposure data quality have historically been limited. Many companies set up manual processes to validate exposure data, but they are typically labor-intensive and limited in scope. It’s easy to identify missing data but virtually impossible to identify incorrect or unreasonable data. Furthermore, to correct any errors that are found, options are typically limited to replacing data with industry averages, if available, or going back to the property owner — a time-consuming and expensive effort.

A better option is automated portfolio-level data validation that depends on an extensive rules hierarchy to identify unrealistic data (for example, a wood-frame building with seven stories). Once identified, the additional challenge is to determine which piece of data is wrong (in this example, is it the building height or construction type?).

Once exposure data fields have been identified as unknown or unreasonable, companies know they need to address the exposure data quality issue. Solutions will differ from company to company based on the severity of the problem and internal business practices.

Now that catastrophe models have become the standard solution for catastrophe risk management, companies need to adopt strategies that will enhance their model output. Improving the quality of exposure data input into catastrophe models should be at the top of the list.

Regardless of a company’s particular circumstances, AIR and ISO offer many solutions insurers can employ to collect better exposure data at the point of underwriting. Data about buildings has been available through the ISO SPI Plus® database for more than 30 years.

»Return to home page

Comments

The comments posted by our readers do not represent the opinions of ISO or the author.

From: Patterson
Comment: Its a known fact that the more facts one obtains on the insured the better the quality of service. The quicker the implamentation of the repairs etc... Personal and property information facts are the start of the solution.

Add Comment Add a Comment

On-demand webinar on this topic