What to look for when underwriting a general liability policy

By Rick Stoll June 22, 2016

Underwriters today have a thirst for reliable data but often face daunting research challenges to find it. They also face disjointed data sources that provide inaccurate and incomplete information. For general liability underwriting, you need reliable data that falls into three categories: the business itself, the actual premises, and the surrounding neighborhood. All three categories are essential to a complete risk picture—and getting data on all three presents its own unique underwriting challenges.

The key criteria for evaluating a business begin with the nature of the business operations. That may seem rudimentary, but it’s not so obvious when actually seeking the information. The initial source is the insurance application itself, but that’s self-reported information. It may not reflect the true nature of a business. Is your distributor or applicant 100 percent forthcoming about previous violations, business hours, size of business, outside catering, attractive nuisances (such as a swimming pool or play area), previous bankruptcies, or entertainment? Or are they crafting a conservative picture based on information readily available?

Each data point can have a large effect on the risk, and confirmation is often time-consuming. Google can reveal hundreds of bits of data, but much of it may not be relevant, accurate, or current. That makes it hard to sort through it all and reach a conclusion with some level of confidence.

Another necessary area of business data is management competency. This is just as important—and just as difficult to gather. No one would call him- or herself a bad manager or incompetent business owner and would be even less likely to share a negative opinion with an insurer or agent. However, management efficiency and experience of the business are important. A low or dropping credit score, health and other violations, slow bill payment, and liens are all red flags to an underwriter and need careful examination.

Next, the premises themselves deserve attention. They may contain specific risk factors related to the business or property, such as construction class, observed hazards, building occupant information, and hazards that can affect the likelihood and severity of premises-related liability claims.

Beyond the doors of the business, the surrounding neighborhood may present additional risk factors. What’s the crime score for the area? Is the score for crimes against persons or property trending up or down? Is there heavy foot traffic from nearby theaters, popular restaurants and bars, or other attractions? Many businesses intentionally locate near train stations and bus stops. That’s good for business, but locations with such large numbers of people present additional risks.

Successful insurers use and follow smart risk selection criteria and evaluation processes. To optimize effectiveness, underwriters need to consider the positive effect that high-quality data and analytics can have on underwriter efficiency, revenue potential, and loss ratios.

For more information about point-of-sale data for general liability underwriters, view our video or visit our website. Feel free to contact me directly at Rick.Stoll@Verisk.com.


Rick Stoll

Rick Stoll is the assistant vice president of product management at Verisk Insurance Solutions. He is responsible for the development and management of data and analytic products. Rick has over 15 years’ experience building, managing, and selling complex digital services – primarily ones grounded in big data analytics. Prior to ISO, he lead innovative product initiatives at Dun & Bradstreet and American Express. He holds a bachelor of science degree in Industrial & Systems Engineering from Virginia Tech and a master of business administration from New York University - Leonard N. Stern School of Business.