Insurers in the small to mid-size business underwriting game know that quality data acts as a risk reducer, proprietary advantage, and catalyst for new segment adoption. Even the best data has a half-life though, and competitive insurance companies must constantly look for new sources of data that add new dimensions of insight to their current datastore– nowhere is this more relevant than the complex world of small business.
Additive data helps solve your existing problems
Misclassification is one of the largest expenses in SMB underwriting, and that’s due in part to a tricky thing called reality. Most businesses don’t fit perfectly into the molds prescribed to them, and classifying a million small businesses as opposed to a hundred enterprise companies creates an even larger margin of error.
By diversifying and updating your current business classification strategies with data from new and emerging sources, you can provide the right coverage at the right time and drastically reduce the risk of losing customers (and reputation points) to a lack of appropriate coverage due to misclassification.
Data that is aggregated and normalized from the vast world of online content can also reveal entirely new business characterizations in real-time, providing a clearer picture before and after that policy was written. For example, say you insure 10 cafes in a coffee-loving part of town: Do all of those cafes manage their businesses in the exact same way? Chances are they’ve had to diversify their offerings to remain competitive (just like you!), which means their classifications should change accordingly. Online sources supply insight much faster than traditional methods like self-reporting, and give you a more dynamic and customer-centric approach to your underwriting.
That means associated risk characteristics, too
Just like additive data supplies new definitions for small business classification, it also helps identify the risks characteristics that accompany them. If one of our cafes adds a row of salon chairs in the back, this new coffee/barber shop has assumed new risks that may not be disclosed right away.
The real benefit to working with online data is the immediate access to new knowledge as it’s happening: a public Instagram account tagged their location at your client’s store, in front of their new salon chairs with the hashtag #GrandReopening #Cafecutz; a Yelp review loves the vibe but found it gross that hair could fly into their coffee from the new salon in the back; bestcoffeeintown.blog says the Cafecutz experience is ‘bizarre but not uncharming.’ Paired with a little machine learning you’ve got yourself a powerful combination of vast information stores and the means to monitor it for relevant activity at scale, in seconds.
Predictive tools help determine your future rating factors
Since we’re on the topic of machine learning and its implications for new types of data, we’d be remiss not to mention the most exciting frontier; the future. While raw sources start as biased, unstructured and unrefined, data scientists use AI tools to clean and normalize it, allowing their predictive values and comparative dimensions to shine through. It is these resulting factors that can be leveraged to forecast which of your business lines will be most successful in that area, indicate where you could enhance your price segmentation strategies, and provide foresight into new markets so you aren’t entering blind.
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While many traditional methods of data gathering remain integral to an overall underwriting strategy, alternative data provides additional layers of insight to your existing stores as well as new dimensions of its own. Carpe Data’s new Commercial Data-as-a-Service Platform supplies each of these advantages with its various additive data offerings. See our new classification system, risk characteristics, and next-gen scores and indexes for yourself.
