Case study: Intelligent automation at work for insurance companies
More intelligent, more efficient and better for the customer — sounds great, right?
These claims aren’t too good to be true. Here’s a case study that leverages automated intelligence.
RPA eliminates lag time and engineers major savings for FDLIC
Funeral Directors Life Insurance Company had eight staffers who were responsible for settling more than 2,000 contracts each week. As claims picked up and new business accelerated, employees were staying past close to process contracts and reduce lag time.
Already users of OnBase, a Hyland content services platform, all it took the FDLIC team was a “Hack Week” and Hyland’s robotic process automation (RPA) solution to automate new business processing. During its five days of innovation and fun, FDLIC figured out how the Hyland RPA and OnBase integration could improve its processes.
Within a few weeks, FDLIC added five bots that were integrated with Hyland’s platform to automatically surface information and settle new business contracts.
A month later, the company had an 88% return on its bot investment and a total elimination of lag time in line-of-business processing.
The bots allow the company’s workers to focus on building relationships with customers and producing quicker, more accurate outcomes.
Implementing RPA meant FDLIC’s claims department began to save about seven minutes and $4.36 per claim. Also significant: The insurer saved 20,000 hours in manual processes and its claims volume increased by more than $15 million over a two-year period.
Why top performers are turning to AI and machine learning
Interest in AI and machine learning is as prevalent as an insurance spot during a football game. But effectively and intelligently capturing critical business information is as important to an insurer as a star quarterback is to an NFL team.
Organizations, AIIM says, can no longer “afford the luxury of manually identifying and categorizing incoming information.” AIIM’s report on intelligent capture, which includes a significant number of responses from insurers, recommends that organizations view AI and machine learning in two contexts.
The first is traditional: how the tools are being used and could be used to improve efficiency and gain insight.
The second is more complex: how the tools can be used to make information more understandable by the machines.
The latter, AIIM says, is “done by adding context to unstructured information — i.e., content.”
Top-performing companies, the association found, are pushing the envelope on AI and machine learning much more aggressively than average organizations. AIIM says the difference in the percentage of large-scale implementations of intelligent capture technologies between top performers and average organizations is “at least” 20 percentage points.