Car keys on insurance form ©Exactostock, SuperStock

If you wait until the last minute to buy car insurance, are you a bigger risk than a customer who purchases it 10 or more days in advance? Are 16-year-old drivers in Miami more or less likely to crash their cars than 16-year-old drivers in Minot, N.D.? Does a history of filing a lot of auto insurance claims mean you're also likely to file homeowners insurance claims?

These are among countless questions insurers are considering as they search for new clues to predict risk. The process is part of an evolving science called predictive modeling, which has revolutionized how the industry prices auto and homeowners insurance policies.

"Its intent is to predict the future by making mathematical sense of the past," says Steven Armstrong, a fellow of the Casualty Actuarial Society and the global chief actuarial officer for consumer insurance at Chartis Insurance.

Predictive modeling uses statistical and analytical techniques powered by technology to sift through millions of pieces of data. It looks for patterns and clues that indicate how likely customers are to file claims.

The mathematical theories that make predictive modeling possible have been around for decades, says Eric Huls, a vice president of quantitative research and analytics at Allstate. "But there wasn't a machine powerful enough to apply them to large data sets. What's really advanced is computer power."

Those technological advancements, combined with stiff competition among insurers and the growing amount of detailed data available, create the perfect conditions for predictive modeling to take off in the last decade, says Claudine Modlin, a senior consultant at global research and consulting firm Towers Watson.

"Today, data is literally everywhere," says Chartis' Armstrong.

If insurers can't collect certain information directly from you, they can buy it from vendors. Armstrong says he's heard of predictive modelers scouring data about what people buy at retail stores to see if there are any connections between shopping habits and risk. He's unaware of any insurance companies actually using that data to set rates.

Details, details

Predictive modeling gives insurance companies the ability to consider a massive number of variables in more combinations than they could a generation ago. Back then, an insurer would probably base an auto insurance premium on 15 different characteristics, Armstrong says. "Today 40 or 50 variables might be considered."

Insurers are also using data with greater detail, including the specific birth dates of customers versus broad age ranges, such as over 55, Towers Watson's Modlin says. In home insurance, some companies are looking at how characteristics of the neighborhoods and houses -- square footage, number of bathrooms, whether the plumbing has been updated -- can predict losses.

Traditionally, insurers have used that type of information to determine how much homeowners insurance coverage a customer needs. Now they're looking at how such data might correlate with risk. Does the median age of residents in the neighborhood predict theft risk, for instance? What about the neighborhood's unemployment rate?

The science also allows insurance companies to isolate the correlation of each variable with risk and then consider how that correlation might be different in certain circumstances, Allstate's Huls says.

For example, an insurer can look at how the combination of your age and where you live correlates with risk. With the ability to isolate variables, as well as examine many combinations of them, insurance companies can set prices that more closely reflect the likelihood that you'll make an insurance claim.

All of this attention to detail may seem intrusive, but improving pricing accuracy is good for you, as well as the industry, Modlin says.

"Nobody wants to pay more than they have to, and at the very least they want it to be commensurate with their risk," she says. "You don't want to be a good driver who's paying more to subsidize a bad driver."

Before predictive modeling, rating structures were similar among insurers, Modlin says. Now, the structures are much more complex and sophisticated, and it's not as easy for one company to understand another insurer's rating methods.

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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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