Actuaries Should Explore Alternative Approaches To Data Mining As Part Of Predictive Modeling Process

LAS VEGAS – 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 Ian Ayres told the Casualty Actuarial Society (CAS) Ratemaking Seminar.

In a keynote address, Ayres who is author of the book “Super Crunchers” noted that non-traditional approaches to predictive analysis, such as neural networks, may 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. Every once in a while I would try a neural network and see if your traditional approach is robust to alternative specifications,” he said.

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

Ayres cited the example of a company called Epagogix and its ability to forecast the box office success of movies by using a neural network model.

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 6 of the 9 movie scripts -- twice the accuracy of the studio.

“If Epagogix can be successful 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,” he 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.

“For many things the wisdom of the crowd and asking people what they think doesn’t work. For example, it wouldn’t do a good job if I asked a lot of individuals to predict what the chance of fire loss was for a particular commercial establishment.”
“Instead the new wisdom is actually incredibly old for actuaries -- going back and finding the wisdom that is locked in historical data,” he said.

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. 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,” he said.

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

“We looked at data from 1981 to 1994 in 60 large cities. 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,” Ayres noted.

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.

<p align="justify">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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