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Big Data in Action: Opportunities for Carriers (Part I)

Chander Ramamurthy
Insurance Experts' Forum, November 25, 2013

“Big data” has two meanings. The first is straightforward: vast amounts of data — think petabytes and exabytes. The second refers to the new data tools and technologies used to efficiently process huge amounts of data.

Insurers are collecting more and more data from application logs, server logs, usage logs, social feeds corporate websites and social media. It’s a huge amount of information — thus the need for big data. These technologies now make it possible to mine data across three dimensions:

  • Large size/long duration: Traditional data mining usually was limited to three to five years of data. Now you can mine data accumulated over decades rather than years.
  • Real-time: With the advent of social media and the different sources, data pours in at ever-increasing speeds.
  • Variety of types: There's more variety of data — both structured and unstructured — that are drastically different from each other.

The ability to master the complexities of capturing, processing and organizing big data has led to several data-centric opportunities for carriers that have done so.

Personalized marketing

Big data is playing an increasing role in sales and marketing. Personalization is the hot industry trend. Gathering more information about customers helps insurance companies provide more-personalized products and services. Innovative companies are coming up with new ways to gather more information about customers to personalize their insurance-buying experience.

One example is Progressive's Snapshot device, which is among the latest breed of products and devices that let insurers provide personalized products based on customers’ driving habits.  A device like Snapshot captures information from the car every second, collecting data like how often the driver brakes, how quickly they accelerate, driving time, average speed, etc.

According to facethefactsusa.org, drivers log an average of 13,476 miles per year, or 37 miles a day. Big data systems have to process this constant stream of data coming in every second for however long the user takes to travel 37 miles. Even if 10 to 15 percent of customers use the device, it is still a huge amount of data to process. The systems have to process all this information and use predictive models to analyze risks and offer a personalized rate to the user.

People increasingly are using social media to voice their interests, opinions and frustrations. Analyzing social feeds can help insurance companies better target new customers and respond to existing customers. Using big data, they can pinpoint trends, especially of complaints or dissatisfaction with current products and services. Getting ahead of the curve is crucial because bad reviews can spread like wildfire on the Web.  

Risk management and process improvement

The wealth of data now available to insurance companies — from both old and new data sources – offers ways to better predict risks and trends. Big data can be used to analyze decades of information and identify trends and newer dimensions such as demographic change and behavioral evolution. These trends and dimensions can help insurers calculate risks and anticipate trends with better precision than previously possible.

Another popular use for big data is constant improvement of organizational productivity by recording usage patterns of an organization's internal tools and software. Better understanding of usage trends leads to the:

  • Creation of more useful software that better fits the organization's needs
  • Avoidance of tools that do not have a good return on investment
  • Identification of manual tasks that can be automated. For example, logs and usage patterns from tools at the agent’s office are important sources of information for understanding customer preferences and agency efficiency

It’s time for insurers of all types to hop on the big data bandwagon or risk getting left behind.

In part II of this blog, we’ll explore how big data can be used in predictive analytics, fraud detection and organizational efficiency.

Chander Ramamurthy is an architect with technology company X by 2.

Readers are encouraged to respond to Chander by using the “Add Your Comments” box below. He can also be reached at cramamurthy@xby2.com.

This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.

The opinions of bloggers on www.insurancenetworking.com do not necessarily reflect those of Insurance Networking News.

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