Time and money – these are the reasons many CEOs in the insurance industry aren’t too keen on embracing predictive analytics, according to a panel of high-ranking actuaries and executives at the recent annual meeting of the Casualty Actuarial Society.
In the presentation, “What Executives Need to Know about Predictive Modeling,” panel members sound off on ideas to help educate and encourage insurance CEOs to move forward with predictive analytics insurance models to improve operations.
Because CEOs are concerned about the time it will take to implement predictive analytics tools, not to mention the cost, the panel acknowledges that it’s up to actuaries to advocate for these tools by clueing the execs in on their potential return on investment and other benefits, according to the Casualty Actuarial Society.
“Executives are becoming infoholics,” notes Martin Ellingsworth, president of the ISO Innovative Analytics (IIA) unit at ISO, a resource for information about property/casualty insurance risk. “If you can show them something they didn’t know and show how the company benefits from knowing it, ‘be ready to be challenged [with] how fast you can do that.’”
And who better to convince CEOs of the importance of predictive analytics insurances models than then the actuarial professionals and predictive modelers who are working closely with business leaders to improve insurance operations and reduce the uncertainty around the carriers’ financial performances, he says.
The way to do it? “Give examples of how the model will help the company do things faster, better and cheaper,” says lan Bauer, former president of Progressive Direct.
In the past 15 years or so, predictive analytics has changed the property/casualty insurance industry, albeit it a bit slower than it could have been, according to Ellingsworth.
Stephen Mildenhall, CEO of Aon Benfield Analytics, points to August 24, 1992 – the day Hurricane Andrew rips through South Florida. Insurance losses top $15 billion, far more than anyone thinks possible. Far more than the old analysis methods indicate.
So, insurance executives begin using complex computer models to more accurately determine their exposures. The models, Mildenhall says, “gave us a ruler to measure risk in a consistent way.”
Alice Gannon, chief actuary of USAA P&C Insurance Group, says executives at her company have always been used to heavy quantitative analysis, since they’ve come from the military, where mathematical precision is paramount and mistakes cost lives. “They said, ‘Why aren’t you doing more?’” she notes in the Casualty Actuarial Society statement.
“The case was made for us by [Hurricanes] Hugo and Andrew after our existing models, like those at many other companies, did not perform adequately,” she says.
Gannon offers suggestions for advocating for predictive models:
- Pitch the model at a high-level. “Start at the 50,000-foot level and don’t plunge into the nitty-gritty too fast,” she says.
- Estimate the return on investment. Show how your project aligns with the executives’ other objectives.
- Emphasize data quality. “You’ve got to make sure the data’s good,” Gannon notes.
- Be clear about the goal of the model. “Make sure this thing is going to move the needle you specify,” she says.
- Build on prior successes. “The executive will listen to the previously successful modeler more than the rookie,” Gannon notes. “If you’re the rookie, work with someone else to get your shot.”
- Put together a strong modeling team, she says, adding that actuaries have a strong skill set for a modeling team. And be sure to invest to sustain your intellectual capital.
- But also be clear about the risks that exist in building the model.
