Predictive analysis is a leap forward in antifraud technology. It injects artificial intelligence into fraud detection, allowing insurers to uncover suspicious claim patterns earlier in the claim cycle. Basically, it compacts the time-space continuum for investigations.
One target involves large and rapidly spreading medical rings that cost insurers tens of billions of dollars annually in bogus auto, health and workers' compensation claims. Many criminal rings understand software tripwires. Thus they continually evolve in a constant cat-and-mouse chase. This adaptability can render static rules-based red flag approaches less effective.
But predictive analysis adjusts to moving, shape-shifting fraud rings. It can identify complex ring activity from huge volumes of data. When used with data mining tools and other antifraud programs, plus street investigations, predictive analytics can be among the biggest electronic enemies of fraud rings.
Predictive technology is equally effective against single cases across all lines, such as suspicious auto and homeowner claims. Thus it has all-purpose potential. However, many insurers have been slow to adopt predictive analytics. Lack of full understanding of its capabilities, lower internal priority for antifraud IT and the effort of preparing an orderly data infrastructure all contribute to this slow adoption.
Previous antifraud technology has overpromised and under-delivered. Today, falling costs now place predictive analysis within reach of most insurers. Credit card companies and banks have stemmed losses with predictive analytics for years. As pressure mounts to shave costs, the ROI in potentially lowering insurance fraud losses can validate the investment. Properly managed, predictive analytics isn't a cost center but a profit contributor.