Tunde Panaki | November 30, 2017

Learn How Smaller Insurance Carriers Capitalize on Big Data

Recently, I was speaking to a friend at a small insurance carrier who said to me, “This big data thing is not for us. We don’t have the resources or enough data to justify a big data environment—and even if we did, we’re still in the process of modernizing our core systems. We have other priorities right now and big data isn’t one of them. Big data is for the big guys.”

Still Struggling with Core System Modernization

For smaller insurance carriers, the reality is that the costs of standing up a big data environment and the ongoing maintenance could be prohibitive.  Data scientists are hard to find and resources to manage such environments are slim.  It should come as no surprise, then, that Gartner predicts that through 2017, 60 percent of big data projects will fail to go beyond piloting and experimentation, and will be abandoned.

A number of smaller carriers are still recovering from legacy system modernization projects that may have gone over budget, stalled or failed. Such negative experiences tend to sway opinions and make insurers lose confidence in new initiatives. Yet no matter the size of a carrier, every insurer is in the business of weighing risk. When it comes to big data, smaller carriers must think in terms of upfront costs and the requirements and resources needed to get the most out of big data. But they must also balance those costs against the relative benefits to determine if the potential rewards of big data outweigh the costs.

Why Big Data Can Help Modernize the Smaller Insurance Carrier

One of the biggest benefits of big data is its ability to use all data available to provide unparalleled insights that drive business decisions. But what exactly does this mean for an insurance company? All insurance companies, large and small, continue to look for ways to improve their claims process. So let’s examine a benefit that could come from leveraging the power of big data to streamline claims.

Imagine, for example, that I have a crack in my windshield. I call my insurance company to file a claim. I send a picture of the crack. My claims adjuster reviews my policy, coverage and pictures of the damage, and then sends me a list of preferred glass companies.  After scheduling an appointment, the glass company fixes my windshield and sends the invoice to the insurance company. The bill is paid by the insurance company and the claim is closed. The entire process from when first notice of loss (FNOL) is reported to when the claim is closed could take up to four weeks. This is an example of low risk claims that cause unnecessary bottlenecks in a claims adjuster’s case load. However, if the process were streamlined, operational inefficiencies in the claims process could be reduced or eliminated, which by some estimates can cost an insurance company $10MM per year.

In the scenario above, there are five steps that can easily be done by a big data platform to streamline the process and free up the claims adjuster to handle more complex cases. Within minutes of opening a claim, a big data platform that is powered by machine learning can:

  1. Determine if my policy is active and if the claim is covered
  2. Analyze my claim in the context of other claims in my insurer’s database
  3. Assess the picture of the damage to determine severity and make sure the image is not a stock photo from Google
  4. Score the claim as a low risk
  5. Automatically generate a list of glass companies that can fix my windshield

What a Successful Solution Looks Like

Smaller carriers have a low appetite for risk even as technology is upending the insurance industry and challenging all carriers to go digital. However, big data does not have to be a low priority for smaller carriers. The solution lies in embracing a big data platform that offers turnkey solutions without the need for pre-existing data lakes or costly upfront investments.

My friend who said that big data is not for smaller carriers couldn’t be more wrong. Smaller carriers interested in leveraging big data should look for a comprehensive big data platform that offers a full-service data management solution encompassing data governance, data quality and analytics capabilities. An integrated platform can provide a framework of governance that clearly defines data usage, sources, and ownership; automated end-to-end data quality monitoring and checks to build trust and catch errors before they impact business; and machine learning and analytics to help insurers leverage data to extract insights and improve outcomes.  This would allow them to make faster, smarter business decisions and improve the customer experience, which could streamline operations, increase written premiums, and directly impact their bottom line. Big data shouldn’t just be for the big guys. A data management platform like this can democratize big data and put it to work for insurance companies of any size.

To learn more about data unification and an all-inclusive big data platform, download this data sheet.

Download the Data Sheet

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