Emerging Trends in Data and Insurance Underwriting

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Since the advent of the modern industrial era, the most successful enterprises have leveraged spatial analytics, or data on the space and workstreams of our physical world, to upend industries. In the early 20th century it was Ford using data points to find a better way to manufacture high quality and low-cost cars — this was only refined by Toyota’s TPS system later in the 20th century. Now well into the 21st century, just what vertical applications are best suited to take up the baton and execute on the opportunity?

Immediately FinTech comes to mind based on the number of platforms utilizing data sets of all types to underwrite risk. Whether it be commercial or consumer loans or insurance policies, the more data a business has to assess risk, the more efficiently the platform can price its products, attract customers, and reduce write-offs. Within FinTech, insurance as a $5T global category has already seen traction in applying spatial data to practice. While data geeks have dominated the insurance industry for decades, the diversity of data sets used in incumbent models is surprisingly Neolithic. For instance, traditional underwriting algorithms only take into account a few dozen data points relative to the treasure trove of real time sets of spatial data around us. That said, new models are emerging to augment underwriting with data from the physical world, a notable example of which is Root Insurance, a full stack carrier employing dynamic driving behaviors to underwrite policies. Though founded just five years ago, the company recently IPO’d at a multi-billion-dollar valuation, largely attributable to growth associated with its novel underwriting capabilities. Root is not alone in this practice either as incumbents and emerging InsurTech platforms across lines are now unlocking billions in value via enhanced underwriting tools augmented with data sets from the physical world. The value of this type of information is even more topical when considering the substantive blind spots of legacy insurer underwriting models. Take that Consumer Reports findings suggest certain insurers charge minorities premiums as much as 30% higher than their white counterparts in areas with similar accident costs. Despite the historical correlation of zip codes to loss ratios, this underscores the importance of leveraging additional data points for a more holistic understanding of an individual’s and/or property’s risk profile. …

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While media attention around PropTech has historically focused on high growth, platform economic plays such as WeWork, a colossal opportunity lies within the walls, by the water cooler, and everywhere in between the residential, commercial, industrial spaces around us (and beyond). Specifically, real estate sits on a unique and largely untapped set of data that has historically remained underutilized, to the detriment of the industry. This was fully realized as the sector was both the hardest hit and slowest to react at the onset of the COVID-19 pandemic, in part because of the lack of information available across residential, commercial, and industrial spaces, and also in part because the category lacks software to extract this data in the first place. …


Dave Mullen

Venture Investor @ SVB Capital, Data Enthusiast, Emerging Venture Capital Association

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