Closing the information gap

Closing the information gap | Insurance Business

Closing the information gap

When insurers begin pricing a policy for a new small business customer, they often rely solely on a single financial data source. Did you know that using only one commercial credit source could give you only 50% coverage of your small businesses book? That could leave the other half of your book of business completely unknown when underwriting or rating, leaving you exposed to any competition that wants to move in on that business. Pricing in this way often results in small business owners paying for other business owners’ risks simply due to a lack of sufficient information.

There’s so much data and analysis available to help overcome this problem. To properly protect a small business customer, insurance carriers need to make sure they’re collecting and analyzing all available data in order to accurately assess the risk, design and price the policy.

One of the challenges facing insurers when underwriting a small business is finding sufficient financial performance information, leaving them to rely only on financial data about the business itself for underwriting, creating a gap in the full risk picture. While consumers start accumulating credit history any time they apply for credit, business credit is vastly different with – according to an internal study conducted by LexisNexis Risk Solutions in 2019, only around half of small businesses have a credit profile in a single commercial credit bureau. When insurers use commercial credit for commercial rating, that could leave half of the insurer’s book of business completely unknown when underwriting or rating a small business.

How can commercial insurers close this gap? The key is to determine a methodology that enables the insurer to accurately and confidently assess, predict and price the risks associated with each business. A multiple-source approach can help address the gap, but identifying and evaluating the right sources is critical. If risks are assessed and insurance policies priced based on only one view of financial data, the insurer is potentially missing other views and critical information. Our internal analysis shows that when you add three or more financial data sets to your underwriting, it results in an average rate of 74% compared to just 52% with only one source.

When insurers layer in the business owner’s personal information, particularly for non-employer or sole proprietor firms, they increase not only their coverage, but also segmentation. Since both types of data effectively identify higher loss propensities when combined, there is additional segmentation available for the insurer to leverage. This overlaying segmentation can enable insurers to better identify areas of the population where they might otherwise over- or under-predict using only a business entity model.

So, what are the steps to take? First, understand your current and future target market. How do these types of businesses compare to similar entities in your book of business and what financial products do they use?

Next, select the right sources of data for a particular business. Credit bureaus, non-traditional financial sources and personal financial data can all be used to better align to your book.

Finally, design an underwriting program that leverages these data sources to better segment small businesses based on a more accurate view of the business’s financial performance. Taking advantage of this segmentation can increase the effectiveness of your program and positively impact your loss ratio relativities.

To remain competitive within the growing small business insurance market, carriers need to evaluate the right mix of information on both the business and its owner to accurately price the risks of each small business they insure. Embracing change and following the right models can help insurers provide a faster and more seamless underwriting experience.

Working with this multi-source process can be a daunting task. Partnering with a trusted solutions provider can help insurers manage this effort by helping move from one data set to another, providing better predictive modeling, and targeting and capturing new profit pools.

 

Sharon Maloney is a director of commercial insurance for LexisNexis Risk Solutions, where she’s responsible for assessments, requirements, and the design of data solutions and services focused on commercial insurance.