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Five Steps to a Better Fraud Detection Program

Though predictive modeling has long been considered a natural fit for claims fraud detection, results have been mixed. To better utilize this powerful tool, insurers must examine past flops and build a stronger defense.

By James Ruotolo | Updated on October 01, 2013
For years, the insurance industry was a leader in the field of business analytics. In the early days, this referred primarily to descriptive analytics, like reports, dashboards and scorecards.

Regardless of the complexity or level of functionality introduced by new business intelligence tools, these analytical solutions had one major flaw: They were always backward-looking. They could only tell a story about what had happened in the past.

Over the last few years, interest has grown in predictive analytics. The reason is obvious: If insurers can predict what will happen in the future for particular policies and claims, then they can better position themselves to maximize profits, reduce costs, and improve customer satisfaction. In fact, the whole basis for the insurance system relies on insurers’ capability to predict with a certain level of accuracy what is likely to happen in the future.

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