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The Big Data Dive for Life Insurance

The Big Data Dive for Life Insurance

If your clients need life insurance, they don’t always have to wait for the results of invasive medical tests. New policies can be turned around within days, regardless of complexity, according to an analysis by Independent Life Brokerage.

For that, thank “predictive modeling,” or using algorithms to crunch hundreds of variables, including a person’s medical, social, geographic, personal and financial history. Using massive data sets unavailable a short time ago, companies can better calculate the odds of future health issues. Insurance companies began doing this in earnest within the past few years, and some back studies suggest it has a 95 percent accuracy record.

Dan Pearson, chief executive of Independent Life Brokerage, says that a predictive modeling pilot program showed non-smokers between the ages 18 and 60, and seeking coverage to $1 million, could get a policy in five to six days from submission to issue. “Although approval rates are still below 50 percent, it is expected that the algorithms will improve and approval rates will increase,” he says.

Because predictive modeling is less invasive than traditional medical underwriting, requiring no blood and urine tests, an advisor can better help clients solve an immediate need.

“It is going to help advisors get into the middle market,” Pearson says. In the next few years, he predicts, 48-hour approval for life insurance will be available.

Part of this is due to better databases and medical procedures. For instance, underwriters have less reason to obtain information from an applicant’s doctors when they can tap an information database on the client’s prescriptions. Underwriters also can conduct online investigations pertaining to criminal activity and bankruptcy and motor vehicle violations, any of which may influence how much a policy costs, or whether it’s approved at all.

What’s perhaps counterintuitive is that more detailed scrutiny of potential clients’ personal records has resulted in more clients being offered coverage, not less, says Neal Halder, chief underwriter for the Principal Financial Group. Of course, better medical treatment for things like cancer, heart disease and diabetes are also taken into consideration.

The new technology of underwriting even helps clients who have recovered from illnesses that once would have precluded them from getting policies at all. Halder says that in the past, most cancer patients were rated as a substandard risk. Today, some of those cancer patients would be able to obtain a standard rating sooner and at a lower cost.

There are no official statistics, but Daniel Cotter, director of risk management for Rehmann, a Cleveland-based financial planning firm, says he is writing policies for clients with heart problems, cancer and diabetes that he could not write 10 years ago.

“A decade ago, someone who had a heart disorder with three stents and an angioplasty had to wait six years before they could be considered for life insurance!” Cotter says.

Thanks to big data and predictive modeling, those individuals may get a chance to buy protection earlier, quicker and at a better price.