A major threat of lowered financial ratings and soaringreinsurance rates is lurking for workers' compensation insurers asa result of flawed catastrophe modeling data and processdifficulties. Above all, there is the possibility of ticking timebombs within otherwise profitable portfolios, which might producesurprising loss amounts that could threaten the financial stabilityof insurance companies.

The 9/11 terrorist attacks concentrated the minds of insurers onthe truly gigantic losses that are possible in workers' comp andunderscored the need for accurate catastrophe modeling.

However, while much work has been done over the past six yearsby commercial modeling firms and some insurers to develop themodeling discipline, the accuracy of these models is being hamperedby poor data quality, which is a growing concern within theindustry.

As models increasingly are used by reinsurers and required byrating companies, ceding companies will be at a competitivedisadvantage if they do not gather accurate, consistent data abouttheir workers' comp exposures.

Poor data and imprecise modeling eventually could lead toadverse reinsurance pricing for ceding companies, which also couldfind their financial ratings under threat.

The current lack of accurate workers' comp modeling could oneday pose a problem for underwriters, unless they get a betterhandle on their loss exposures.

In their development, workers' comp models currently are at astage similar to that of property models 15 years ago. There areseveral–and, certainly, very understandable–reasons that workers'comp modeling efforts lag behind property models:

o Modeling is a complicated discipline and takes time to fullydevelop.

It took the catastrophic loss of Hurricane Andrew in 1992 toprovide the impetus for the major developments in propertymodeling. If we view 9/11 as providing a similar impetus forworkers' comp models, real strides have been made in the yearssince.

o Few people could have imagined that two buildings wouldcollapse in a terrorist attack with such an enormous loss of lifeand physical injuries.

The 9/11 attacks shattered assumptions about the potential forlosses when a catastrophe hits an office building during workhours.

The terrorist attacks on the World Trade Center and Pentagonrepresented the largest ever workers' comp loss. Indeed, inSeptember 2006, the Insurance Information Institute estimated thatworkers' comp losses from the event totaled nearly $2 billion.

o There have not been as many resources allocated to modelingworkers' comp as there have to modeling property.

One reason for this is that reinsurers have driven much of theeffort to develop property models, primarily because of their hugefinancial stake in the property market.

Nevertheless, reinsurers increasingly are getting behind theeffort to create accurate workers' comp modeling techniques inorder to protect their own balance sheets.

o Workers' comp is a complex risk and is harder to assess thanproperty risks.

For example, employees move around within locations, makingoccupancy totals hard to determine in workers' comp modeling.

In addition, a disparate range of injuries–from minor wounds toparalysis requiring lifetime care–can result from the sameevent.

o Workers' compensation modeling faces two significant obstacleswhich impede accuracy: poor data quality and high inherentuncertainty.

Alone, either of these two issues can be overcome, but togetherthey function as a major impediment to accurate modeling.

So, what can be done to achieve more effective workers' compmodeling?

The evolution of modeling techniques certainly would beexpedited if everyone spoke the same language–the samevocabulary–in order to standardize the data required for computerentry and analysis.

Standards need to be developed because the expectations of thedata required for modeling can vary dramatically–for example,between reinsurers and rating agencies. These discrepancies canlead to inadequate and inconsistent data quality.

For the models to be effective, all interested parties need toagree on the data to be captured and how the data will be factoredinto underwriting decisions and risk management practices. (For ashort list of the ideal data required, see infographic.)

An insurer's data often does not reach this level of detail, andinstead, for example, is restricted to accumulated employee countsor payroll at a main mailing address. This lack of precise datameans that books of business cannot be adequately modeled, as largeblocks of exposures are unaccounted for in terms of locations,covered employees, or both.

However, even with accurate and specific data, modeling ofworkers' comp risk is still complex due to the nature of the perilsinvolved.

Workers' comp offers the added difficulties of accounting forevent occurrence times, mobile employees and the fact that mostcatastrophe perils impacting workers' comp are low in frequency buthigh in severity.

The timing of an event is a major factor simply becauseemployees do not work all the time. A building is a fixed, staticobject of constant value for 24 hours each day; employees, however,move around.

For example, an earthquake occurring at 2 a.m. would have a verydifferent impact on a book of workers' comp business than oneoccurring at 2 p.m., even though the property loss might be thesame.

Mobile employees are another complicating factor. Contractors,delivery truck drivers and similar workers cannot be located atspecific, fixed locations. Instead, their locations can vary in thecourse of a work shift. Approximations, therefore, must be made fortheir loss potential, adding to the underlying uncertainty of ananalysis.

Sept. 11, 2001 stands as the seminal moment in workers' comploss, but severe earthquakes, industrial accidents or new terroristattacks all threaten to inflict major losses in the future.

This high severity of major events, paired with their lowfrequency, means that while catastrophe losses in any given yearare likely to be zero, the potential for an extreme loss cannot bediscounted.

Accurately quantifying this exposure and loss potential isessential to effective risk management. Failing to do so can leadto adverse pricing from reinsurers unable to adequately measure therisk of a book of business.

Likewise, it can engender skepticism from rating agencies aboutrisk exposure and management practices.