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Data key to improving insurance for SMEs


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Data key to improving insurance for SMEs

Collecting more data from small and medium-sized enterprises should improve the prices and terms their insurers offer them, according to panelists on an S&P Global Market Intelligence webinar.

Gathering that data does present challenges, however, and smaller businesses are keen to avoid greater commoditization of their cover.

Flemming Bengtsen, founder and CEO of technology-enabled trade credit insurance underwriting agency Nimbla, said government stimulus across the globe to alleviate the impacts of the coronavirus pandemic kept some companies afloat that may otherwise have failed, creating an "enormous credit risk challenge." Nimbla needs the right data to price risk appropriately, but the fact that SMEs do not produce the same amount of data as larger corporations, such as quarterly earnings, makes it much more difficult to price those risks.

"Obviously that is also going to drive costs up," he said.

Bengtsen added that it is important for insurers to gain the trust of smaller companies to make them more comfortable with passing on their data.

Natalie Botha, head of systems and data analytics, credit specialties at Marsh & McLennan Cos. Inc. subsidiary Marsh, said that the more data that can be gathered from a company, the better the presentation risk brokers can make to underwriters and the better the terms for clients.

But David Perry, managing director of FSB Insurance Service, an insurance broker serving members of the U.K.'s Federation of Small Businesses, said "more data means less advice" many times for smaller businesses. He said the association is concerned about the presumption that smaller companies have less complex insurance needs than larger ones.

Perry acknowledged that data has helped insurers to react more quickly and that as a trend, standardization of data is "really good." However, he sees "intermediation and advice" as critical to understanding everything that goes into the businesses of SMEs.

Marsh's Botha also warned against sacrificing nuance in data standardization.

"You are not always going to have a one-size-fits-all solution," she said.