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$4.5 billion in commercial property premium leakage due to misclassification

By Rick Stoll June 1, 2017
Verisk Commercial Premium Leakage Analysis

$4.5 billion. According to a Verisk analysis of commercial portfolios, that’s the estimated amount of commercial property premium leakage over the course of four years due to misclassification of construction class and fire protection. (Fire protection refers to Public Protection Classification [PPC™] and sprinkler assessment for the purposes of this article.) The $4.5 billion represents a $1.3 billion hit the first year and an additional $3.2 billion over the next 3 years, assuming a 10 percent natural customer attrition rate.

Challenges in the small commercial market

Of course, premium leakage is never a good thing, but for insurers in the BOP and small commercial markets, there’s no room for that kind of error. High volume, scale, and speed are essential in the market—and that makes accuracy vitally important.

Misclassification is rampant in the industry because there are many areas where it can occur. Reliance on unreliable data sources, simple human error in data entry, and receiving faulty information from agents, brokers, or property owners can all cause misclassification.

An additional challenge is data currency. While construction class doesn’t typically change, a community’s PPC class, which is evaluated every four to five years on average, can change about one third of the time—and many insurers don’t have a process to accommodate such changes to their risk profile.

Misclassification can lead directly to poor underwriting decisions and/or failure to adhere to underwriting guidelines, and that can veer an insurer’s book off course in short order. The following statistics, compiled from Verisk’s analyses of commercial portfolios and industry records, shed more light on the scope of the problem and potential impact on small commercial underwriting:

Misclassified commercial properties

In the small commercial market, insurers must achieve both speed to quote and accuracy in the information they use to produce the volume of business needed for profitability. With modern straight-through processing, information can flow through a system across all commercial lines, from property to workers’ compensation, general liability, or even commercial auto with little or no human interaction. Data must be of the highest quality to accurately quote a risk at a profitable price point.

Almost 50 percent of properties are misclassified for construction class and/or fire protection, which are essential measures of risk, but only two of the many factors that can lead to misclassified risks. The table below (Insurer Listed Construction Class) represents the results of a recent Verisk analysis of several insurers’ books of business. The green line illustrates when the insurers’ construction class records matched our site-verified data.

As you can see by adding up the percentages, the insurers’ records were only on-target about 67.5 percent of the time. The red section shows underclassification, where the exposure is underestimated and results in premium leakage, and the purple section refers to overclassification. The table shows that overall about 32.5 percent of the insurers’ risks had incorrect construction class.

Insurer Listed Construction Class Verisk Study

Nearly one out of three Class 1 frame buildings were listed by the insurer as Class 2 through Class 6, which are lower risks. One wonders how a frame structure can be misclassified as masonry or fire resistant, but it does happen. Due to the complexity and nuances of construction, many agents and other insurance professionals might not have the experience or knowledge to make the correct call. Once an error has been made, it’s on the insurer’s book at an incorrect rate, leaving it vulnerable to premium leakage and unforeseen risk.

In addition to construction class and fire protection, misclassification can occur in other areas. One prime example not included in this study is measuring square footage, which Verisk found was incorrect in 80 percent of the insurers’ risks evaluated. The percentage decreased as allowance for error increased, but there were gross mismatches. Although not as injurious as the other two misclassification errors, each type of misclassification adds to the problem and increases the risk for premium leakage, further illustrating how important it is to get correct information to underwrite a property.

Comm PL Chart Web 2 v3

Misclassifying fire protection

Another serious risk of damage to premium is in misclassifying fire protection. For example, an analysis of one insurer’s book featured a case where there was an incorrect PPC score listed for a community. The PPC cited was a four when it should have been an eight, a much riskier rating.

A community rated a four has a significantly better capability to protect properties from fire losses than a community rated as an eight. Premium calculated on such an inaccurate PPC won’t reflect the true effect on exposure and is inadequate for the risk assumed, leaving an insurer open to greater than expected losses at point of claim.

Almost 27 percent of the insurers’ risks listed an incorrect PPC, and many risks received unwarranted sprinkler credits. PPC scores are determined at the community level, but properties are covered individually. Insurers need to know details on individual buildings, such as fire flow availability, hydrant spacing, responding fire department, and more to determine if the community-based score should be reduced for a specific building, making coverage more specific to the actual risk of that property.

The need for reliable data

The most important factor in avoiding misclassifications is obtaining and using quality data. That’s the best way for underwriters to ensure adherence to guidelines and assign the correct premium to the coverage. Straight-through processing supported by reliable data provides the speed and accuracy necessary to compete profitably in the BOP and small commercial markets.

This article focused on statistics and measures from just a few classifications, such as construction class and fire protection. Other analyses pointed out significant discrepancies in Class Code and building age, further exacerbating the extent of premium leakage.

Data reliability depends upon the quality of the information. When performing this analysis, Verisk tapped into its ProMetrix® commercial underwriting database of 3.7 million site verified commercial properties, records on 26 million businesses, and 200 million vehicles.

Underwriters need the most accurate data and a prefill solution that provides reliable information on dozens of property characteristics. A straight-through-processing solution allows the reliable data to flow swiftly and directly into an insurer’s system, providing the property characteristics essential for risk decision making.

Property coverage is just the start

The premium leakage challenge for small commercial and BOP underwriting is not limited to property coverage. The issue cuts across all lines of business, including workers’ compensation, general liability, and commercial auto. Every line of business has its specific problems, and we’ll explore those problems in subsequent articles.

Verisk Insurance Solutions can perform a complete portfolio assessment of your book of business to determine any issues with premium leakage or other challenges. You can request an assessment here.


Rick Stoll is vice president for Commercial Underwriting Products, Verisk Insurance Solutions. You can contact Rick at rick.stoll@verisk.com