Assessing and Minimizing Risk Profiles
Big data and analytics is a business imperative for insurance companies. Without the ability to analyze large, unstructured data sets about customers, companies like Auto-Owners Insurance are unable to calculate the risk profiles on which their business depends on.
Eric Truax, and the Auto-Owners analytics teams, were looking for a way to analyze larger, more complicated data sets than their current systems could handle. The data sets that they wanted to analyze often involved unstructured data, like photos, videos, audio ﬁles, and more.
The problem is, though, that the potential in terms of risk assessment of data collection and analysis is an enormous one. Many insurance companies don't have a scalable way to incorporate many types of data into their prediction models. This can impact the ability to properly assess and minimize their risks, but also tailor more insurance products that suit each customer's needs and expectations.
"The business of insurance is about keeping a promise to our customers to be there when they need us," said Eric Truax, Analytics Manager at Auto-Owners Insurance. "We're always looking to find new ways to analyze data and use predictive modeling to improve our customer service, underwrite policies more effectively, settle claims faster, and better match premium to risk."
“We keep throwing more and more problems at D2iQ and getting more value out of it. It's a great ﬁt for us.”
- Eric Truax, Analytics, Auto-Owners Insurance
Becoming More Data-Driven Than Ever
Truax and team turned to D2iQ, which offered the kind of support that a large enterprise, like Auto-Owners, required. As the team kept expanding its cluster and discovering new uses for their work around the company, the agility of D2iQ in terms of operations, development, and data began to shine through.
"Insurance has always been data-driven, especially for pricing," said Truax. "We’re using D2iQ to make many of our core business processes like underwriting and claims handling more effective by leveraging the data we've captured and becoming more data-driven than ever.
For Truax, a big advantage to using D2iQ for predictive modeling is that the development group can make and deploy models to production and do some of the IT work themselves, cutting down on time to market and decreasing their reliance on other teams. The team now considers itself a self-sufficient DevOps team, rather than just a development team, which has turned out to be a big value proposition.
"After an initial learning curve, everyone came to realize we knew what we're doing, and we didn't break anything important," says Truax. "Over the past year, we've onboarded my entire team to our cluster which keeps the system pegged at about 80 percent capacity.”
With new Insurtech disruptors popping up to improve various aspects of the insurance process, established insurers, such as Auto-Owners, must respond to this new competition as soon as it appears. Thanks to D2iQ, Auto-Owners is able to use the insights gathered from analyzing unstructured data to improve its underwriting efficiency, reduce costs, and accelerate its data science program.