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March 5, 2019

Understanding New Generations of Data

Summary:

New, more contextual streams of data have become available, allowing for robust analytical insights, with huge implications.

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To effectively acquire customers, offer personalized products and provide seamless service requires careful analysis of data from which insights can be drawn. Yet executives cite data quality (or lack thereof) as the chief challenge to their effective use of analytics. (Insurance Nexus’ Advanced Analytics and AI survey).

This may, in part, be due to the evolving nature of data and our understanding of how its changing qualities affect how we use it — as technology changes and different data sources emerge, the characteristics of data evolve.

More data is all well and good, but more isn’t simply…more. As new and more contextual streams of data have become available to insurance organizations, more robust and potent analytical insights can be drawn, carrying with them huge implications for insurance as a whole.

See also: Data, Analytics and the Next Generation of Underwriting  

Insurance Nexus spoke to three insurance data experts, Aviad Pinkovezky (head of product, Hippo Insurance), Jerry Gupta (director of group strategy, Swiss Re Management (US)) and Eugene Wen (vice president, group advanced analytics, Manulife), for their perspectives on what each generation of data means for the insurance organization of today, and how subsequent generations will affect the industry tomorrow.

See full whitepaper here.

While there is disagreement regarding which generational bucket data should fall into, current categorizations appear to be largely aligned. Internal, proprietary data is generally agreed to form first-generation data, with the second-generation comprising telematics and tracking device data. There is some contention over the categorization of third-party data, but these are largely academic distinctions.

Experts agree that we are witnessing the arrival of a new classification of data: third-generation. As Internet of Things (IoT) data becomes more commonplace, its incorporation with structured and unstructured data from social media, connected devices, web and mobile will constitute a potentially far more insightful kind of data.

While this is certainly on the horizon, and has been successfully deployed with vehicular telematics, using “IoT, including wearables, in the personal lines space [and elsewhere], is still not widely adopted,” says Jerry Gupta, senior vice president, digital catalyst, Swiss Re. Yet, he is confident that third-generation data will “be the next wave of really big data that we will see. Wearables will have a particular relevance to life and health products as one could collect lot of health-related data.”

Download the full whitepaper to get more insights.

Despite this promise, there are significant roadblocks to effectively leverage third-generation data. According to Aviad Pinkovezky, head of product at Hippo Insurance, the chief problem is one of vastly increased complexity: “This sort of data is created on demand and is based on the analysis of millions of different data points…algorithms aren’t just generating more data streams, they are taking new data, making decisions and applying them.” Clearly, this requires a change in how data is handled, stored and analyzed. Most significantly, third-generation data has the potential to change the nature of insurance.

See also: 10 Trends on Big Data, Advanced Analytics  

Given that data is no longer the limiting factor for insurance organizations, our research suggested five areas on which insurance carriers should focus to turn data into real-time, data-driven segmentation and personalization: cost, technical ability, compliance, legacy systems and strategic vision.

A challenge, certainly, but the potential rewards to both insurance carrier and insureds are hugely promising, especially the change in relationship between carrier and insured. The potential to not only predict, but mitigate, risk has huge implications for insurance.

Efficient, accurate and automated data gathering is a clear benefit for insurance carriers, and the potential to provide value-added services (by mitigating risk altogether) greatly enhances their role in the eyes of the customer. Measures that reduce risk to the insured increase trust and strengthen the bond between the carrier and the insured. Customers are less likely to view insurance as a service they hope to never use but, rather, a valuable partner in keeping themselves secure, both materially and financially.

The whitepaper, “Building the Customer-Focused Carrier of the Future with Next-Generation Data,” was created in association with Insurance Nexus’ sixth annual Insurance AI and Analytics USA Summit, taking place May 2-3, 2019, at the Renaissance Downtown Hotel in Chicago. Expecting more than 450 senior attendees from across analytics and business leadership teams, the event will explore how insurance carriers can harness AI and advanced analytics to meet increasing customer demands, optimize operations and improve profitability. For more information, please visit the website.

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About the Author

Ira Sopic is currently focused on how insurance carriers are integrating AI and advanced analytics into their existing processes to increase efficiency and revolutionize the way they work. This includes the key partnerships that the industry is creating and a clear picture of how the future will be shaped.

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