The insurance industry is in dire need of greater investment in claims technology and Big Data, according to a recent insurance company survey conducted by business process and technology services provider Xchanging.

When asked which claims technologies were most valuable, nearly half (42%) of respondents chose predictive modeling/analytics and a quarter chose Big Data. But only 16% stated claims was their top investment priority.

“The insurance industry as a whole generates more than 2.4 billion gigabytes of data per year,” says Jenna Richardson, director of North American insurance services at Xchanging. “For everyone who’s on the front lines of an insurance-based relationship, a lot of that information is just kind of floating around out there, uncaptured and unanalyzed. It’s a missed opportunity—inherent in that data is some type of information that will help make a better business decision.”

While most insurance professionals are familiar with Big Data, they may be less familiar with analytics and predictive modeling, which “work together,” Richardson explains. “The only difference is predictive modeling takes things a slight step further, to say that based upon the analytics we’ve derived from the data, we think X, Y and Z is going to happen.”

How do these buzz words help the insurance industry? “So much information comes into organizations by way of claims,” Richardson says. Since claims data drives the underwriting process, Big Data and analytics can reveal “historic claims patterns” that can help companies create ratings and pricing based upon previous trends, Richardson says. “It’s a cyclic approach—we need that claims data to be able to improve our underwriting practices.”

One example of how companies can leverage Big Data and predictive analytics for business success is improved customer segmentation: “understanding what the customers are doing from a behavior standpoint and what they want,” Richardson says. That allows companies to “better tailor their customer service and even their products and programs to a specific demographic or client base.”

And the benefits of predictive modeling extend beyond improving processes and service. “Based upon your prior knowledge and models, maybe you’re able predict a claim might be fraudulent, so a red flag automatically goes up,” Richardson says. “Rather than that progressing through the system and paying that out, you can model specific exceptions to just passing through a claim.”

Although some older primary carrier organizations that still use legacy systems or work off paper-based submission have been “slow on the uptake,” Richardson says, the insurance industry is certainly open to adopting these processes. In fact, the Xchanging survey found that 36% of respondents said Big Data and analytics have the highest likelihood of increased investment this year compared to technologies such as mobile applications, electronic placing platforms, expert management processing solutions and end-to-end insurance lifecycle solutions.

“Big Data is only getting bigger,” Richardson says. “People are talking about usage-based auto insurance, cars that drive themselves, houses that are significantly connected and collecting data on everything from how often I turn the light on to when my garage door opens—as society becomes more technologically enabled, we’re going to have so many incremental touch points for collecting data. It’s a huge opportunity to derive value in the future.”

But where do independent agents come in? Aren’t their hands tied, waiting on their insurance company partners to invest in Big Data, analytics and predictive modeling? Not necessarily. In fact, “carriers who work through agents exclusively are at the mercy of their agents to feed them back clean data,” Richardson says. “It’s up to the agents on the front lines of communication with their customers to really help derive that value.”

That means you can do your part to keep the momentum going strong. “Don’t underestimate the value of quality data,” Richardson says. “Make sure you’re asking the right questions. Make sure you’re collecting the information as accurately as possible, and that you’re actually physically collecting it—it’s not just something you take in and once the policy is bound, you get rid of it. That data can be valuable to you in many other ways.”

Jacquelyn Connelly is IA senior editor.