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The race to zero on a road paved with quality data

The road to a profitable small commercial portfolio begins with effective underwriting guidelines, and it’s paved with integrated, high-quality data.

With simple entries, such as a business name and address, insurers can prefill and fast-track applications leveraging robust business and property data to accurately classify a risk, uncover exposures, and evaluate management competency. And more reliable and accurate data can accelerate effective underwriting decisions and meet rising customer expectations.

Automated Underwriting: Winning In The Race To Zero

Competing in the “race to zero”—that is, zero questions for applicants to answer—has never been more important for insurers and managing general agents. An ISO survey of agents in the small commercial market found accuracy, speed of quote, and ease of applying were the key elements in dealing with an insurer. Commission rates were a distant fourth.

Having access  to automated underwriting capabilities is vital for many small commercial insurers that have faced problems, including:

  • high-touch quote-to-bind processes on low-complexity policies
  • frustrating, cumbersome application processes for business owners
  • underwriting data that’s often inaccurate or incomplete

Without a data solution that is built on industry intelligence and underwriting expertise, insurance providers (carriers and managing general agents, or MGAs) are jeopardizing their ability to grow profitably and sustainably. This would ultimately offset any gains from automation and efficiency initiatives. Additionally, self-reported information or reliance on traditional data sources will continue to burden underwriters with labor-intensive research and validation on low complexity risks. The integration of data can help, but only if it’s backed by strong quality.  

One vexing issue is risk misclassification. For example, a retail-only wood products store might be labeled as a sawmill. Fast-food restaurants might be confused with full-service eateries. Or a machine shop could be labeled as an online parts warehouse.

Multiple industry classifications must be accounted for in most cases. Examples could include a construction contractor that operates in both residential and commercial properties with a focus on carpentry, electricity, and roofing—or a landscaper that’s involved in excavation, snowplowing, and roof cleaning. These types of misclassifications are frequently the result of data that’s not granular enough to uncover insights on a business’s operations and risk characteristics.

Plugging the leaks

Bad data can lead to incorrect pricing, which can affect profitability and potentially lead insurers to underwrite policies that may otherwise exceed their risk appetite. An even thornier problem is premium leakage.

A Verisk analysis, encompassing small commercial policy classifications and millions of records of commercial premium and loss experience over a five-year period found industry misclassification by SIC/NAICS code was 52 percent. This translated to an estimated $6.5 billion in premium leakage in the first year. On a four-year basis, assuming a 10 percent churn rate, total leakage exceeds $22.3 billion in business owners policy (BOP) premium alone.

Automated Underwriting: Winning In The Race To Zero

Just a few errors can lead to huge losses for an insurer and jeopardize the profitability of a book of business. Those concerns can be averted by automated underwriting deployed in conjunction with high-quality data.

To win the race to zero and help avoid costly misclassifications:

  1. Automate underwriting with rigorously audited data and proven analytics built on industry intelligence and expertise
  2. Establish a return-on-investment model that takes in consideration several factors beyond cost saving. These factors include premium leakage, retention risk, loss ratio improvements, and customer experience.
  3. Choose a holistic solution that is continuously and quickly evolving to stay ahead of the market and emerging risks
  4. Integrate the solution using industry best practices with a customer-centric focus and a view across the end to end policy lifecycle

This approach will not only fuel your profitable growth in the small commercial space and delight your customers (brokers, agents, and/or insureds). It will also empower your underwriting talent to focus on higher value accounts and product strategy.

The Need for Speed

A streamlined process is not only ideal; it’s what customers want. They expect a quick answer to how much a policy will cost, without having to complete a sprawling form with more than 90 questions.

On the broker level, avoiding the paper trap means greater operational efficiency, an important consideration when the average premium is relatively low. It also allows more time to pursue new business. While direct written premium for small commercial insurance exceeds $100 billion, 40 percent of small businesses lack any insurance.1

The full potential of automated underwriting depends on a deliberate strategy, one that counts return on investment by more than just shortsighted efficiency metrics. Strong quality controls can ensure the information is verified. Analytics built off insurance data help to turn prefill into insight. And working with an organization that has the right focus and expertise can help steer best practices.

For more information on how you can leverage data and analytics to automate your small commercial underwriting visit You can also download our white paper, Automated Underwriting: Winning the Race to Zero.

  1. Insurance Information Institute, I.I.I.: Small Businesses Have Big Insurance Needs, May 6, 2019, < >, accessed on October 8, 2020.

Othman Loudghiri

Othman Loudghiri is senior product manager for small commercial underwriting at Verisk. He can be reached at

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