American International Group Inc. uses one of the industry’s leading algorithmic models to determine how much companies should pay for insurance. It just doesn’t trust what the model computes on its own.

As part of an approach it started rolling out last year, the global insurance conglomerate pairs its models with human underwriters. The approach reflects the company’s belief that human judgment is still needed in sizing up most of the midsize to large businesses that it insures. AIG even has a nickname for underwriters who keep the same price as the model every time: “flat liners.”

Insurance companies such as AIG are among the original financial quants, from tracking ships to derive marine-insurance prices to using quantitative analysis to determine the likelihood of automobiles crashing, hurricanes wiping out beach towns and juries finding doctors liable for malpractice.

Now, promising new techniques including algorithms ?
Set of rules that can parse data and automatically decide what to buy and sell. can help carriers tap into a growing array of detail to price risk: sensors in factories, devices worn by construction workers, employee-sentiment data and satellite imagery, to name a few.

AIG’s commercial-insurance unit alone pays $75 million each business day in claims. The insurer is a major seller of policies that cover such diverse things as the cost of injured workers; wrecks of corporate-owned vehicles; fire and other damage to premises; cyber hacking, contaminated products and offshore drilling rigs.

When an underwriter ?
Insurance professionals who determine how much an applicant will pay for insurance, or if the applicant will be sold insurance at all. “turns off his or her brain, we’re done,” said Madhu Tadikonda, chief underwriter for AIG’s commercial unit. “The models by themselves are not perfect” for many of the risks posed by policyholders.

Still, in an example of how data analysis can make a difference, AIG recently informed a hotel client that it should mop its floors at 3 a.m. to reduce slips and falls. It also has advised clients that employees with carpel tunnel syndrome are at high risk of repeat injuries, so the customary push to quickly get a person back on the job can backfire.

Underwriters such as Matthew Lebron call on business clients keeping an eye out for characteristics that distinguish them, “so I can make the judgment call to go upward or downward” from the computer-generated average price. Among questions he asks: Is a business up to snuff with industry safety standards? Is it working with AIG’s loss-control program to try to minimize the risk of claims?

“There’s definitely an element of human touch,” said Brett Herrman, who works as a “technical underwriter” at AIG, running the model when colleagues go out in the field. “We’re diving in on detail and collaborating as a team.”

The need to pair with humans hasn’t stopped the arms race for data and innovative technology in the industry.

In a recent survey by consulting firm Accenture, 37% of about 550 insurance executives said they plan to invest “extensively” in machine learning over the next three years, and another 44% anticipate “moderate” investment.

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