Looking for the next big predictor

Predictive modelers can approach their work in a couple of ways. They can create hypotheses for risk predictors and test them, or they can feed data into computers and let the machines identify patterns.

Insurance companies are on the hunt for anything new that can help them price insurance more accurately. With that information, Modlin says, insurers can attract low-risk customers from the competition, which boosts their profits.

"It's like trying to find the needle in the haystack," Armstrong says. "The fun of predictive modeling is finding that needle."

Among predictive modelers' biggest finds was the connection between credit history and the risk for filing auto and homeowners insurance claims. Actuaries don't have to show how one factor causes another, only that they correlate.

The use of credit history for pricing insurance is controversial. Insurers say customers with poor credit will file more claims, but some consumer advocates say the practice is unfair to people who have suffered financial setbacks and that it disproportionately affects low-income people and minorities. Some states have put limits on using credit history to price insurance.

The industry learned an important lesson from the backlash, Modlin says. Even though the connection between bad credit and risk is clear, insurers must do better communicating internally and with the public about how they use that type of information.

A new source of data for predictive modeling is real-time driving behavior collected through usage-based insurance programs. Customers who enroll in usage-based programs agree to having a telematics device (which records and reports key driving habits, such as mileage, frequency of hard braking and time of day when driving) on board. Customers with less-risky habits earn discounts on their car insurance rates.

"Predictive modelers are going gaga over telematics because it provides so much data, and the data is so rich," Armstrong says.

What's next?

Insurance companies are expanding the role of predictive modeling beyond pricing into other areas, such as marketing, claims handling and fraud prevention. Which claims should be investigated for fraud? How will getting a damaged car to a body shop one day sooner affect the size of the claim? Which customers are most likely to shop for a new insurer when their premiums goes up?

It's all in the models.

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