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It wasn’t too long ago that we were told by our elders to never get inside a car with a stranger or stay in an unknown person’s house. We were taught the importance of having a steady job and the term gigs had a sense of uncertainty associated with it. However, today these notions are considered archaic, and this change in perspective largely comes from technological advancements. From shared rides and accommodations to the growth in freelance and part-time jobs, there has been a gradual change in how we choose to live and do business. Insurers are left answering several unique questions as the foothold of this access economy fortifies. Many are asking what are the proper types of coverage for those opting to share, rather than own and how will work be defined at a time when employment status is being reimagined?
Insurers are making data and analytics an integral part of their digital strategies as they seek to answer these questions and to astutely tackle the imminent change in how property, businesses and employees will be insured. What’s noteworthy, however, is that insurers are increasingly leveraging the same sharing paradigm of the access economy to gather the most actionable insights for risk calculations.
Insurers beginning to find answers to their sharing economy dilemma by pooling data in consortiums. A number of factors are responsible. These include:
Growing Need for Transactional and Behavioral Data
Today, insurers rely on more than just information gathered in-house and underwriting rules to ascertain risks. They look to unlock optimal insights through the right combination of data sets. As a result, savvy insurers are seeing more value in sharing their data in exchange for access to information from a variety of sources.
Two of the most sought-after resources employed by successful insurers looking to efficiently assess risk, are transactional and behavioral data. While the former captures a historical record of a customer’s transactions, like insurance claims and payments, the latter provides insights into customers’ habits, for example, how they tend to drive. Insurers can use these data points to gain granular insights into how a customer’s behavior has evolved over time.
Access to Large Pools of Data
Another reason insurers are increasingly seeing value in partnering around data is that sharing data offers access to extremely large reserves of facts, figures and valuable information.
While large pools of data are more complex to manage and maintain, they can be used to create highly predictive, synthetic variables through repeated testing against a more diverse set of data. Synthetic variables are constructed from computations of more than one variable.
This means that by supplementing in-house resources with large pools of transactional third-party information, insurers can gain access to better predictors and, by extension, more robust predictive models.
Race for the Most Predictive Horsepower
In unlocking access to a large reserve of information, data sharing also delivers high predictive horsepower. This is because access to larger amounts of data can generate better predictors and actionable insights that result in better risk assessment.
Today, insurers are navigating their way through a variety of evolving situations. To do so successfully, it is imperative that they be prudent in assessing the data sources they rely on and identify the right tools to enhance their analytics capabilities.