Risk selection for general liability underwriters

By Rick Stoll  |  February 1, 2016

Typically, when a general liability underwriter receives an application for insurance, the first task is to quickly determine if it’s a risk that can be written based on their underwriting guidelines. To maintain efficiency, underwriters swiftly review certain attributes to immediately reject an application if necessary. For example, if guidelines restrict writing general liability risks for car dealers and the application has a business SIC of “5511, Motor Vehicle Dealers – New & Used,” it quickly gets turned down with minimal research.

However, many risk selection criteria aren’t accurately answered on an application, or the application may require research. This leaves the underwriter with a few suboptimal choices:

  • Spend time researching an application, thereby reducing efficiency.
  • Reject applications that look suspicious or require a lot of research, cutting into potential revenue.
  • Take every application at face value or make inferences that could negatively affect loss ratio.

Cautionv1To minimize the negative effect of the above scenarios, corporate home office underwriters often create simple risk selection criteria to act as proxies for underlying concerns. One example I frequently hear is using the number of years in business as a proxy for a company’s management competency. The assumption is that a business that’s been functioning for a long time is more likely to be competent than a new one.

While this may hold true as a generalization, it’s certainly not the best indicator of a well-run business. I’m sure we all encounter long-running businesses that make us wonder how they stay open! Underwriters need access to reliable data and analytics to guarantee they have accurate and readily available risk selection criteria. For example, information on violations and business credit scores can serve as much better proxies for management competency than years in business.

Even simple risk selection guidelines can be difficult to verify. Many underwriters can’t write businesses that are open late at night. This basic business information may be omitted from an application or other documentation. Should underwriters reject such applications? Do they need to spend time surfing the web for information? Should they contact the agent? Having that reliable data and analytics available in just seconds is invaluable to make quick, smart decisions.

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

For more information about point-of-sale data for general liability underwriters, visit our website and read our brochure.

Feel free to contact me 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.