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Finding the balance: Personalization of risk and risk pooling

By Blake Hill, FCIA, member of the CIA Single Topic Task Force on Risk Classification

The latest buzz phrase in the insurance industry is “embedded insurance,” which presents an opportunity to seamlessly offer protection in real time given a proactive approach to risk management. Underpinning embedded insurance is the ability to personalize insurance. This requires that customers understand the necessity of sharing more data with their insurance providers.  

Over the past seven years, I have been involved in customer engagement and bringing health and wellness programs to insurance customers, both during my time with an insurance provider and now as I work for Dacadoo, an insurtech company and a leading provider in this space. Through this experience, I have seen an array of attitudes from actuaries, underwriters, marketers, and insurance regulators to programs that gather real-time data (on lifestyle, blood pressure, and the like).

These programs are meant to improve the lives of customers by offering ever-relevant services and support. The ability to offer relevant feedback to a customer relies on gathering data on the customer and personalizing the customer experience, and with this type of ongoing data insurance, services can indeed be personalized. This also creates new opportunities for insurance companies.

This is the point, however, at which differing perspectives on how the data can be used enter the conversation; some perspectives are based on the fear of “what if it is used irresponsibly,” while others are optimistic and focus on the numerous benefits. Regardless of your point of view, both perspectives are crucial and will aid in the development of a solution that will benefit all parties. In fact, the opportunity to encourage a robust and open dialogue on this topic was key in my decision to volunteer with the CIA task force on risk classification, which recently released its Big data and risk classification: Understanding the actuarial and social issues statement.

There are practical examples to readily consider, such as in auto insurance, where routes for delivery drivers are updated in real time to avoid areas of traffic congestion and higher accident risks (e.g., left-hand turns in major intersections). Likewise, in home insurance, the ability to collect water-usage data can aid flood risk management. Despite the best of intentions, consumers of these types of insurance may have differing viewpoints, from gratitude for the support to fear, and even distaste, that they are being tracked.

The competing forces of risk sharing and moral hazard have always been present in insurance, and increasing the number of risk classes has been a result. But with recent advancements in technology, the improved ability to gather data increases competition between risk pooling and creating new risk classes (or personalization of risks).

It should also be noted that personalization of risks may drive risk pools so shallow that they contain only a few individuals, thereby negating the ability to share risk in an insurance pool.

Finding the balance will be an ongoing act, considering the views of different consumers and insurers, as well as competition in the marketplace and oversight by governing bodies, aiding in this evolution. For now, I highly recommend the CIA’s statement on big data and risk classification as a starting point in this discussion.

Fundamental to our evolution is the ability to discover and learn, powered by data.

This article reflects the opinion of the author and does not represent an official statement of the CIA.

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