Text mining and real-time applications have improved the accuracy and timeliness of predictive analytics, making it a better bet for businesses.
Computerworld - In the movie Minority Report, Tom Cruise's character relies on visions from "precogs," people who can predict crimes, to catch criminals before they can act. While the film takes place in the future, the predictive analytics tool sets available to businesses today are bringing similar scenarios to life.
For example, LoanPerformance uses such tools to help its clients predict which of their customers will be late with payments, which will be lying when they say the check is in the mail and which will be likely to default altogether. The San Francisco-based firm operates a cooperative database of loan payment information for financial institutions. Richard Harmon, senior vice president of scoring and analytic services at LoanPerformance, says its customers, which include mortgage servicers, use the data to encourage on-time payments or to put delinquent accounts on the fast track to foreclosure.
Predictive analytic tools are also used to predict outright fraud. For example, at health insurer Highmark Inc. in Pittsburgh, such systems are set to anticipate and block fraudulent claims.
The adoption of predictive analytics systems is on an upswing, driven by technology advances and the potential for large bottom-line benefits. The number of preconfigured and proven models available for specific industries and applications is increasing, while the model-creation process is more automated than it once was. That means analysts can build models fasterand refresh them more frequently in response to changing business needs.
Successful models can pay off big. At LoanPerformance, a model that predicts which accounts that are 90 days in arrears will default saved one client $2 million in six months. The total cost of deployment was $400,000. Those types of returns are one reason why IDC research shows the sale of predictive analytics tools growing to $3 billion by 2008, which would be a nearly 40% increase from 2004. Such tools make up 25% of the business intelligence market.
As the volumes of business data have increased, the desire to extract value from that information has intensified. Fortunately, predictive analytics tools have become easier to use, says Harmon, allowing more streamlined model-building workflows and enabling analysts steeped in business issues to do more without the involvement of statisticians. "This is where the future lies," he says. "The tools are being automated."
The biggest benefits, however, are coming on two fronts: the inclusion of unstructured data into the predictive modeling process to improve accuracy and a push to execute predictive analytics and present results in real time.
Predictive analytics involve several steps, ranging from identifying and preparing target data to developing a statistical model, testing it on a sample for accuracy and then running it against the full data set. Results are sent to front-office systems, where business logic is used to, for example, cross-sell a customer a different product or flag an insurance claim as potentially fraudulent. While most organizations customize predictive models to their customer bases and business challenges, many processes for finding models have been automated.
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