Getting Personal with Big Data in Insurance: Strategies for Mastering Massive Amounts of Data

Telemetry, IoT, wearables, AI, chatbots and drones are tools that help group Insurers better engage with customers and improve business processes. There is one thing that all of these technologies have in common: data.

Personal data to be precise.

Exactly how insurers will mine, manage and utilize the massive amounts of data now available from various internal and external sources may mean the difference between data mastery and data mystery for many carriers. In this blog, I’ll outline a few things carriers can start to think about as they incorporate big data into their corporate strategies.

Start Out Simple and Stay Focused

Data science is comprised of several disciplines and skill sets. AJ Goldstein does an excellent job of deconstructing this complex craft into what he calls, “The Data Science Process,” consisting of six parts:

  1. Frame the problem
  2. Collect raw data
  3. Process the data
  4. Explore the data
  5. Perform in-depth analysis
  6. Communicate the results

Goldstein says that although data science is a large, complex paradigm, only about 20% of the skills needed will contribute to 80% of the outcomes. So focusing on that core 20% necessary to achieve the results you’re looking for will help simplify the process and keep IT departments focused on the goals you originally set out to achieve with the data.

Applications for big data in insurance currently center on providing solutions to tasks like setting premiums, fraud reduction and target marketing. How this looks will differ across projects, but regardless of the application, data experts will collect data from various sources, analyze it, and use it to draw conclusions about how the company can improve the bottom line and provide value to customers.

They don’t call it big data for nothing! The amount is gigantic. New variables and trends that arise can easily lead you astray from the original question you set out to answer.

Stay on track and focus on what you set out to determine. You can always circle around to address new insights later.

Data Exchange Standards and Industry Best Practice

There has been a notable absence of data exchange standards to accompany the onslaught of insurtech technologies and the data that support them. Certain governing bodies have attempted to implement such standards (LIMRA, CLIEDIS, and ACORD). However, none have become pervasive.

Insurance carriers are taking it upon themselves to develop tools that will support data exchange. This can be expensive and cumbersome, especially where older legacy systems are involved. Some vendors provide data exchange protocols along with their proprietary solutions to ensure smooth integration with existing systems.

Data exchange standards ought to encompass data aggregation, format and translation, and frequency of delivery. In one of my recent blogs, I outlined different types of data available for data mining. These can come from a myriad of internal/external channels. For example, on one hand client information can be derived from internal policy administration and claims systems for setting premiums or predicting risk.

On the other hand, the source of marketing data is usually external, often social media that identify consumer preferences. This allows for the creation of specific individual profiles, assisting carriers in suggesting products at key intervals such as enrollment, renewal or major life events. Depending on your goals, marketing messages can be tailored to individual preferences or carriers can be alerted to intervene prior to a customer cancellation – actions all made possible with data trends.

Each of these areas will require different technology solutions to support outcomes and will require integration with core systems across the value chain to be fully optimized. Set your projects up for success by ensuring industry standards are utilized and best practices for data exchange are adhered to.

Mitigating Risks

Consumers know that sharing data comes with risks. Even the most hardened networks can be vulnerable to cyber-attacks and data breaches, leaving consumers understandably wary of how and with whom they share their personal information. Carriers that take the proper cybersecurity measures will be better prepared to ward off or respond to breaches. Obtaining accreditations such as ISO 27001 may help identify any gaps before hackers do.

Privacy is another important factor when obtaining and storing customer data. Consumers want to know what their data is being used for and be assured that it will not be used for anything else. If carriers can guarantee this, studies show that customers are willing to provide personal data in exchange for lower fees and improved services.

When the proper measures to manage big data are in place, an opportunity to form digital trust with customers is possible. If this is established, the possibilities are endless for the kind of engagement and relationships that can be developed and sustained. With information everywhere, people still value relationships they can trust. That’s never going to change.

Data is vital to benefits insurers. Insurers have gone from seeing the value in data, to being able to analyze it, to capitalizing on automation that is now having an immediate impact on operations. The ability to automate business front-ends and back offices has in many cases catapulted insurers into the digital age, and most are landing on their feet.

This is due in no small part to strong leadership from CIOs, a shared understanding of what customers now expect, and a mandate to provide it. Insurers that master big data will likely leap to the front of the pack. Those that see it as a mystery may quickly find themselves out of the race.

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