RMS

RMS To Release Data Quality Toolkit, Enabling Companies To Target Data Improvements

Calif – May 19, 2009 – Risk Management Solutions (RMS) today unveiled its new Data Quality Toolkit, which will empower insurers and reinsurers to assess, manage, and improve the quality of their exposure data. The Toolkit integrates advanced RMS analytics, data from a unique exposure database, and result reports, allowing companies to comprehensively assess both the completeness and accuracy of their exposure data from a model-informed perspective.

As the issue comes under growing scrutiny by third parties, companies are increasingly required to demonstrate robust data quality practices. The RMS® Data Quality Toolkit enables companies to target data improvements in the areas that will have the greatest impact on catastrophe model loss results. It also enhances their ability to select between previously undifferentiated risk and to offer competitive pricing structures on the risks selected.

The Data Quality Toolkit – a Microsoft Windows® application - draws on the proprietary RMS ExposureSource™ Database, which contains primary modeling attribute information for more than 9 million commercial and 65 million residential locations throughout the U.S. This allows companies to compare their existing information with the RMS database, and enhance missing or incorrect attributes including occupancy, construction class, year built, number of stories, and floor area.

“The release of a tool that can objectively assess data quality and help to fix the highest priority locations is a significant advancement,” commented John DeMartini, executive vice president of Towers Perrin. “We envision using this tool to accelerate our ability to help clients improve the quality of their data in time to impact 1/1 renewals.”

As well as being able to compare their data against the ExposureSourceTM Database, companies can use the Toolkit to assess the accuracy of their data by:

		Using validation heuristics, which highlight suspect geocoding, unusual combinations of primary modeling attributes, and questionable coding of financial structures, and
		Comparing it against an industry benchmark portfolio to suggest aggressive or conservative data coding of primary attributes and secondary modifiers.

Reports produced from the Data Quality Toolkit allow users to examine the drivers of data quality at an aggregate level - such as portfolio, account, state, or county – or down to location level, enabling them to implement specific strategies to address data issues.

Roger Arnemann, vice president of Data Solutions at RMS, explained: “The Data Quality Toolkit offers an objective perspective on data quality, giving companies unique insight into factors such as exposed limit, underlying hazard, vulnerability for different perils, and sensitivity effects on modeled losses.” He added: “New metrics will help insurers and reinsurers quantify the potential range of modeled loss changes due to incomplete or insufficiently detailed data.”

Rating agency requirements

Rating agencies examine data quality when assessing the reliability of a company’s probable maximum loss (PML) and its balance sheet strength, and A.M Best has recently introduced additional questions about data quality in their Supplemental Rating Questionnaire (SRQ). Thomas Mount, assistant vice president of the Property & Casualty Ratings Division at A.M. Best, explained: “If our analysts have concerns about a company’s data quality and these are not explained or addressed properly, we could potentially add up to 30 percent onto the probable maximum loss that is reported.” The Data Quality Toolkit includes a report that directly addresses these questions, potentially saving companies days in compiling the information.

Mr. Arnemann concluded: “Systematic use of data quality analytics informs the insurance lifecycle from primary underwriting and pricing through to reinsurance, retrocession, and capital allocation. Well-informed risk selection, whether for policies or treaties, leads to better pricing and fewer surprises in the event a catastrophe strikes.”
The Data Quality Toolkit will be available from June 30, 2009. More information is available at: www.rms.com/Publications/Data_Quality_Toolkit_brochure.pdf