New Insights Article: Big Data Analytics: Changing the Calculus of Insurance
Across all industries, reports Accenture, 89 percent of large companies say big data is going to revolutionize business operations, with changes predicted to be on a scale comparable with how the internet changed the way people work in the 1990s.1 Insurance companies are part of this revolution, which is upending the way they underwrite, handle claims, control losses, develop new products, and service customers.
Big data, as its name implies, involves large sets of data—too large, in fact, to be gathered and analyzed by traditional methods. Although insurers have always gathered and analyzed large amounts of data to make business decisions, the quantity of data available in recent years has increased exponentially because of increased sharing of information and virtually connected objects, through what is called the Internet of Things (IoT).
This increased volume of data challenges insurers to develop new ways to store, access, process, and analyze data. By mining big data for patterns and trends, insurers are able to gain a competitive edge through reduced expenses and improved processes relating to claims, underwriting, and operations.
At the same time, vehicles, buildings, and machines are becoming smarter, through innovative technologies such as wireless sensor networks and computer vision. As a result, many insured objects and workers are safer, causing industry experts to predict that insurance companies will experience significantly lower claims amounts.
Furthermore, technology will foster continuous engagement between insurers and their policyholders, resulting in increased adoption of loss-prevention measures and thereby adding to the downward trend in claims. Given today’s technology, it is not hard to imagine your insurance company sending a text to alert you to replace your washing machine’s water supply hose or to move your car into the garage because of an imminent hail storm.
Big Data Analytics
An insurer’s big data arises from both internal and external sources and can be structured or unstructured. Insurers’ internal underwriting data on losses and exposures is structured because it is organized into databases with rows and columns. An insurer’s external data, such as that provided through vehicle telematics, is usually structured in a similar way. By contrast, unstructured data is not organized, with a prime example being text data from claims adjusters’ notes.
Analytics is a process that enables insurers to gain deep insight from big data to make effective decisions. Many of the big data analytical techniques employed by insurers are not new, such as exploratory data analysis, which is used to develop a basic understanding of data, and data segmentation, which is used to classify data based on its characteristics. However, automation using these techniques allows insurers to analyze data much more quickly and at more granular levels.
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