Valuable Information That Aids Claims, Underwriting Gleaned From Big Data

June 18, 2015

The story of big data — the oceans of data that modern technology generates — in some ways presents a modern cliffhanger. Now that we have access to all of that data, what will we do with it?

Actuaries heard a variety of answers at the Spring Meeting of the Casualty Actuarial Society (CAS) in Colorado Springs, where a trio of experts shared how to glean useful information from a world awash in selfies, tweets and status updates. Casualty actuaries now have the ability to tap into data and stretch beyond their traditional roles of pricing and reserving to help claims adjusters and marketers do their jobs more effectively.

Philip Borba, a senior consultant at the consulting firm Milliman, showed how to find gems of insight in the standard claims report. Adjusters’ notes, he said, contain useful information that actuaries can use in predictive models to pick out which claims are most likely to turn contentious.

Borba, like most researchers in this area, divides a standard narrative report into “n-grams.” N-grams are single words or short series of words. For example, the phrase “tested positive for amphetamines and marijuana” would yield several n-grams, including “tested,” “positive,” “tested positive,” and so on.

Borba looked at 6,949 accident reports from the National Motor Vehicle Crash Causation Survey, a National Highway Traffic Safety Administration project that analyzes crashes. In the study, researchers wrote reports after visiting accident sites just after the crash occurred.

The 6,949 reports generated 13.3 million n-grams. Harnessing computer power to build a model, Borba looked for information on the use of cell phones and driving under the influence of medications. He found that narratives held important information on how often the two were linked to traffic accidents.

A second researcher, Douglas Wing, an assistant vice president at ISO, showed how insurers use computers to study visual information.

Computers see photos differently than we do. For people, a picture is a set of signals that helps them remember what an object is, Wing said. To a computer, it’s a large number of pixels or a unique jigsaw of colored polygons.

In a process called image segmentation, the computer turns a picture into a series of polygons. A photo of a tree is changed into thousands of polygons, one for each leaf, one for each branch. Another process, feature extraction, lets the computer find common shapes — eyes or ears, for example.

The process in essence turns the computer into a super-sophisticated set of eyes, which insurers are beginning to take advantage of, according to Wing.

In homeowners’ insurance, for example, photographs can show the area of a home’s roof, along with its pitch and type of roof — all helpful in underwriting a policy or settling a claim. It’s expensive and dangerous to measure a roof by hand, Wing said, particularly in winter.

But a computer can read a flyover photograph, identifying roof lines, chimneys, and vents, all of which interest underwriters. After a disaster, Wing explained, a computer can compare before-and-after photos to see which homes may be damaged and what an insurer’s overall exposure is likely to be. Auto insurers can use the technology as well, Wing said. Claims on many damaged cars can be adjusted with photos alone.

A computer analyzing a damaged vehicle could settle about 40 percent of claims within a day, Wing said. Often the insured could take the picture, reducing the time and cost of settling a claim while also involving the claimant in the settlement process.

A vast database has already been developed, cataloguing the location and time for millions of vehicles. Millions of new data points are collected each month. The practice began as a way for repossession dealers to find cars. Insurers could use the same information, Wing said, to potentially recover stolen autos or validate that a car is garaged where the insurance policy says it should be.

“This is already beginning to happen,” Wing remarked. “We need to start leveraging it.”

Roosevelt Mosley, a Fellow of the CAS and a principal at Pinnacle Actuarial Resources, described how Twitter yields valuable information on insurance marketing. Social media outlets such as Twitter, Facebook, and LinkedIn provide a candid window into the conversations and opinions of millions of people, Mosley said. Insurers have the opportunity to observe and react.

They can respond to online cris de coeur or passionate public protests as part of their customer service practice. They can listen, tapping into customer sentiments. They can monitor and pick up on broad market trends.

Data mining, Mosley said, is a “virtual focus group.” A company can put an ad online, for example, then see how consumers like it.

Mosley has used social media research to understand usage-based auto insurance, in which companies use a telematics device to monitor driving patterns. The question is, how much of a discount do customers want before they think installing a device is worthwhile?

It was no surprise, Mosley said, to learn that people with higher discounts were more satisfied with the program. Surprisingly, however, the size of the discount was not as important as the size of the actual discount compared with the discount customers thought they should get.

Monitoring social media has the advantage of being unfiltered, Mosley added. “People are sharing raw emotional response, whether positive or negative.”

On the other hand, that means insurers have to take care to understand what is driving the strong feelings. This is a process that “can get really tricky,” he said, but one that’s worthwhile.

“Instead of having to guess what your policyholders want or what your customers are thinking,” Mosley said, “sometimes you just have to do a little digging to find out.”

Source: Casualty Actuarial Society

Was this article valuable?

Here are more articles you may enjoy.