Data Mining Improves Fraud Mitigation Efforts
Sometimes, fraud is obvious.
Dennis Parker, insurance industry marketing manager at SPSS, a Chicago-based predictive analytics provider, remembers back to 1992 when he first started in the insurance business in the special investigations unit (SIU) of a large, nonstandard carrier. He describes seeing instances of what he calls "no duh" fraud, in which the indicators were so obvious that even the least-experienced claims professional or SIU team member should have been able to identify the claim as suspect -- such as when two obviously related individuals were involved in an auto accident with one another.
And while Parker says his team would identify such claims as potentially fraudulent most of the time, he still points out the cold, hard truth of the matter: "There's only one reason those claims were being perpetrated," Parker explains. "Insurance companies were paying them."
The industry has come a long way since then. Just about every carrier has a special investigations unit dedicated to fraud detection, and many have basic claims-scoring algorithms in place that automatically flag suspect claims. Some are able to access real-time databases to examine multiple claims filed by an individual. And although these capabilities sometimes are limited to specific lines of business, perpetrators of fraud no longer should expect insurance companies to pay out claims with no-duh indicators of wrongdoing. >>
But as insurers have improved their methods of mitigating fraud, perpetrators have improved their methods for committing it. "What we're starting to see is that those who are perpetrating fraud are becoming much more intelligent in the way they do so," says Michael Costonis, the Philadelphia-based executive director of Accenture's global claims practice. "They understand the scoring algorithms. They know what information they need to present at first notification of loss and then what to present at the next discussion to continually fly under the radar."
Fraudsters have indeed become adept at learning from experience and have leveraged that knowledge to increase their chances for future paydays. The insurance industry, however, has been slower to leverage its own institutional knowledge to keep pace with those who are walking away with claims dollars to which they are not entitled. According to Accenture's 2007 global claims study, only 17 percent of insurers currently utilize advanced IT tools to detect fraudulent claims.
Just as perpetrators of fraud today have learned from insurers, advanced fraud mitigation IT tools can help insurers learn from the perps. Some new solutions -- those with predictive modeling and text analytics functionalities, in particular -- allow insurers to mine data contained within their existing and incoming claims files to proactively, rather than reactively, establish patterns and associations, allowing carriers to develop better and quicker ways to accurately detect potentially fraudulent claims.
A New World of Fraud
Traditionally, according to Costonis, insurers have used an organizational approach to detect fraud. Potentially fraudulent claims are identified by scoring systems and red flags, which sometimes rely on manual processes and the expertise of experienced claims adjusters. Flagged files or those that tallied a certain fraud score are then sent to the SIU for further investigation.
But, "The new world of fraud is quite different," Costonis relates. "It's requiring much more advanced analytical and data-driven techniques to be able to get after these patterns, which aren't quite as obvious as they used to be."
Costonis estimates that less than 5 percent of insurers have data-patterning capabilities or the ability to find relationships between claims via data and text analytics. But that's not because insurers wouldn't welcome those capabilities. Unfortunately, many carriers would need to improve their paper-based claims situations in order to unlock the data found within their static, paper documents, Costonis explains, though advanced optical character recognition (OCR) technologies could help remedy the situation.
"The biggest challenge is just that [the necessary data] is not accessible to them in their systems," Costonis asserts. "As much as they'd like to have it, they can't get it because it's all in paper files."
 |  |
Thirty-seven percent of insurers still leave fraud detection and prevention efforts to their claims handlers, according to an Accenture study that surveyed 49 P&C carriers in North America. | |
|
Many companies that have already reduced their paper-based claims processes, however, have become de facto early adopters of data-mining technologies, Costonis notes. "Companies that are adopting the new core claims technologies -- the new types of platforms to remit their process -- are starting to look at [data mining functionality] as an add-on capability to help exploit the value in that data," he says. "It's the capability of the chosen few as opposed to the many."
Tweet This