Risk Insider: Barry Vogt

Making Predictive Analytics Actionable

By: | December 6, 2016 • 2 min read
Barry J. Vogt, senior vice president and chief claims officer of EMPLOYERS, is responsible for claims strategy, medical management/managed care strategy, catastrophic claim management, fraud investigations, claim controls, regulatory compliance and subrogation.

Imagine how powerful a tool would be that could see into the future. While such a tool may not yet exist, insurance companies have been using their unprecedented access to data to build predictive models for a number of years now. The use of big data can help identify claims that are likely to develop in an adverse way. This is quickly becoming a best practice within our industry.

With access to such wonderful and valuable information, there are still potential issues. The issues lie in how organizations operationalize – or more specifically, fail to operationalize – predictive analytics.

A lot has been written about the importance of data integrity, data mining and the identification of the most predictive attributes to factor into these algorithms. However, what tends to get overlooked is how organizations will use the output of these models within the claims organization to make better and faster decisions. The most common reason these kinds of initiatives fail is not necessarily bad data, but bad implementation.


If a claims department doesn’t use the output from their predictive modeling to change how they are managing their claims, it won’t drive a significant improvement in their outcomes and is likely to fail. It’s not enough to have a successful model that will provide for the early identification of claims likely to develop into large losses. Claims leaders need to think through how they are going to use that information to change the trajectory of potentially severe claims. That is the key to success.

When implementing a claims predictive model focused on early identification of large losses, a well thought out plan as to how this will be operationalized should include at least the following elements:

  • New claim intake
  • Triage
  • Staffing model
  • Claim assignment
  • Return to work
  • Reserving
  • Auto adjudication
  • Settlement strategy
  • Litigation management
  • Key metrics/outcomes reporting
  • Fraud identification
  • Training

A good approach is to look at each of these key areas and ask the question: “How would we design this if we were starting from scratch with the advantage of predictive modeling, versus trying to simply tweak what is already in place?”

If a claims department doesn’t use the output from their predictive modeling to change how they are managing their claims, it won’t drive a significant improvement in their outcomes and is likely to fail.

Let’s take new claim intake for example. This is an area where insurers can use their analytics efforts to identify certain new data elements that could be collected early in the claim process, yet are shown to be highly predictive of claim development.  By collecting the right data at intake, potential large claims can be identified sooner which will in turn enable more impactful triage and facilitate claim assignment to the right resources earlier in the claim life cycle. By getting the right claim and medical resources involved sooner, we can see improved recovery and return-to-work times along with reduced average severity.

Investing the time and resources necessary to operationalize the model up front will result in a much more successful implementation that will drive improved outcomes for your company.

More from Risk & Insurance

More from Risk & Insurance

Robotics Risk

Rise of the Cobots

Collaborative robots, known as cobots, are rapidly expanding in the workforce due to their versatility. But they bring with them liability concerns.
By: | May 2, 2017 • 5 min read

When the Stanford Shopping Center in Palo Alto hired mobile collaborative robots to bolster security patrols, the goal was to improve costs and safety.

Once the autonomous robotic guards took up their beats — bedecked with alarms, motion sensors, live video streaming and forensics capabilities — no one imagined what would happen next.


For some reason,  a cobots’ sensors didn’t pick up the movement of a toddler on the sidewalk who was trying to play with the 5-foot-tall, egg-shaped figure.

The 300-pound robot was programmed to stop for shoppers, but it knocked down the child and then ran over his feet while his parents helplessly watched.

Engaged to help, this cobot instead did harm, yet the use of cobots is growing rapidly.

Cobots are the fastest growing segment of the robotics industry, which is projected to hit $135.4 billion in 2019, according to tech research firm IDC.

“Robots are embedding themselves more and more into our lives every day,” said Morgan Kyte, a senior vice president at Marsh.

“Collaborative robots have taken the robotics industry by storm over the past several years,” said Bob Doyle, director of communications at the Robotic Industries Association (RIA).

When traditional robots joined the U.S. workforce in the 1960s, they were often assigned one specific task and put to work safely away from humans in a fenced area.

Today, they are rapidly being deployed in the automotive, plastics, electronics assembly, machine tooling and health care industries due to their ability to function in tandem with human co-workers.

More than 24,000 robots valued at $1.3 billion were ordered from North American companies last year, according to the RIA.

Cobots Rapidly Gain Popularity

Cobots are cheaper, more versatile and lighter, and often have a faster return on investment compared to traditional robots. Some cobots even employ artificial intelligence (AI) so they can adapt to their environment, learn new tasks and improve on their skills.

Bob Doyle, director of communications, Robotic Industry Association

Their software is simple to program, so companies don’t need a computer programmer, called a robotic integrator, to come on site to tweak duties. Most employees can learn how to program them.

While the introduction of cobots into the workplace can bring great productivity gains, it also introduces risk mitigation challenges.

“Where does the problem lie when accidents happen and which insurance covers it?” asked attorney Garry Mathiason, co-chair of the robotics, AI and automation industry group at the law firm Littler Mendelson PC in San Francisco.

“Cobots are still machines and things can go awry in many ways,” Marsh’s Kyte said.

“The robot can fail. A subcomponent can fail. It can draw the wrong conclusions.”

If something goes amiss, exposure may fall to many different parties:  the manufacturer of the cobot, the software developer and/or the purchaser of the cobot, to name a few.

Is it a product defect? Was it an issue in the base code or in the design? Was something done in the cobot’s training? Was it user error?

“Cobots are still machines and things can go awry in many ways.” — Morgan Kyte, senior vice president, Marsh

Is it a workers’ compensation case or a liability issue?

“If you get injured in the workplace, there’s no debate as to liability,” Mathiason said.

But if the employee attributes the injury to a poorly designed or programmed machine and sues the manufacturer of the equipment, that’s not limited by workers’ comp, he added.

Garry Mathiason, co-chair, robotics, AI and automation industry group, Littler Mendelson PC

In the case of a worker killed by a cobot in Grand Rapids, Mich., in 2015, the worker’s spouse filed suit against five of the companies responsible for manufacturing the machine.

“It’s going to be unique each time,” Kyte said.

“The issue that keeps me awake at night is that people are so impressed with what a cobot can do, and so they ask it to do a task that it wasn’t meant to perform,” Mathiason said.

Privacy is another consideration.

If the cobot records what is happening around it, takes pictures of its environment and the people in it, an employee or customer might claim a privacy violation.

A public sign disclosing the cobot’s ability to record video or take pictures may be a simple solution. And yet, it is often overlooked, Mathiason said.

Growing Pains in the Industry

There are going to be growing pains as the industry blossoms in advance of any legal and regulatory systems, Mathiason said.

He suggests companies take several mitigation steps before introducing cobots to the workplace.

First, conduct a safety audit that specifically covers robotics. Make sure to properly investigate the use of the technology and consider all options. Run a pilot program to test it out.

Most importantly, he said, assign someone in the organization to get up to speed on the technology and then continuously follow it for updates and new uses.

The Robotics Industry Association has been working with the government to set up safety standards. One employee can join a cobot member association to receive the latest information on regulations.

“I think there’s a lot of confusion about this technology and people see so many things that could go wrong,” Mathiason said.


“But if you handle it properly with the safety audit, the robotics audit, and pay attention to what the standards are, it’s going to be the opposite; there will be fewer problems.

“And you might even see in your experience rating that you are going to [get] a better price to the policy,” he added.

Without forethought, coverage may slip through the cracks. General liability, E&O, business interruption, personal injury, cyber and privacy claims can all be involved.

AIG’s Lexington Insurance introduced an insurance product in 2015 to address the gray areas cobots and robots create. The coverage brings together general and products liability, robotics errors and omissions, and risk management services, all three of which are tailored for the robotics industry. Minimum premium is $25,000.

Insurers are using lessons learned from the creation of cyber liability policies and are applying it to robotics coverage, Kyte said.

“The robotics industry has been very safe for the last 30 years,” RIA’s Doyle said. “It really does have a good track record and we want that to continue.” &

Juliann Walsh is a staff writer at Risk & Insurance. She can be reached at [email protected]