Property/casualty insurance actuaries should not be afraid to try alternative approaches to data mining as part of the predictive modeling process, lawyer and economist told the Casualty Actuarial Society (CAS) Ratemaking Seminar in Las Vegas.

Ayres, author of the book “Super Crunchers,” noted in a keynote address that non-traditional approaches to predictive analysis, such as neural networks, might have a role for actuaries, subject to regulatory constraints.

“You should be thinking about trying alternative approaches, even if your central approach is general linear regression,” he said at the seminar. “Every once in a while I would try a neural network and see if your traditional approach is robust to alternative specifications.”

He went on to explain that as the size of datasets has increased, neural networks may be able to estimate many more parameters than traditionally accommodated by linear regression.

He also cited the example of Epagogix Ltd, a UK-based company specializing in artificial intelligence, and its ability to forecast the box office success of movies by using a neural network model.

According to Ayers, a studio gave Epagogix the scripts for nine movies and asked the company to make their predictions on the box office revenues before a single frame was shot. Independently the studio also made their predictions.

While Epagogix wasn’t perfect, it made accurate predictions on about six of the nine movie scripts—twice the accuracy of the studio.

“If Epagogix can be successful at number-crunching on a very high degree of difficulty question with relatively little data, it shouldn’t be surprising that people in this room can do a much better job on trying to score out some insurance risk when we have much larger data sets,” Ayers said.

Ayres observed that a chic approach in certain number-crunching areas is to draw on the power of the collective wisdom of crowds to make a prediction. However, he suggested that true wisdom lies in mining a company’s historical data.

Ayres went on to discuss a number crunching research project he co-authored on Lojack, a hidden radio transmitter device used for retrieving stolen vehicles.

“This is central to insurance,” he said. “The theory we wanted to test is about hidden precaution. The big difference between Lojack and many traditional car alarms is that Lojack is hidden to a potential thief.”

The idea behind the research, according to Ayers, was that hidden precautions could have a positive influence in reducing theft in a city because the thieves would become scared—they wouldn’t know which vehicles were Lojack-equipped.

“We looked at data from 1981 to 1994 in 60 large cities,” Ayres said. “After Lojack comes in there is a substantial downturn in crime. The bottom line is that we found the social benefit of Lojack was 15 times greater than the costs of putting the device in the vehicle.”

Most of that benefit was external to the owner of the Lojack, however. “Most of the benefit isn’t that it reduces your chance of getting a theft loss but that it reduces the loss on auto theft for other people in that city who don’t have Lojack,” he said.

According to Ayres, the findings suggest that insurer premium discounts to car owners who install Lojack are far less generous than the apparent social benefit. Yet Lojack appears to be one of the most cost-effective crime reduction approaches.

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