Filed Under:Claims, Investigative & Forensics

Technology, Claims Segmentation Enhance Fraud Detection

(Editor's Note: This article has been contributed by Janine Johnson, an analytics manager at the ISO Innovative Analytics (IIA) unit of Verisk Analytics (www.verisk.com).

It's no secret: Fraudulent claims continue to be an insidious problem for the industry, costing P&C insurers and consumers an estimated $40 billion a year (according to the FBI). Of course, that figure stands to skyrocket as the National Insurance Crime Bureau (NICB) reports that questionable claims (QCs) increased an unprecedented 20 percent during the first half of 2012 when compared to the same period in 2011.

No single indicator suffices to deny coverage, but applications that raise suspicion should be taken off of the direct-channel conveyor belt and placed in front of an underwriter, who will ask additional questions to verify information prior to granting coverage. In many instances, a dishonest applicant will not continue with the submission process when required to be in direct contact with an underwriter.

Claims Segmentation

Text mining has become more accessible because of increased computing. Among the most common applications of this technology is the mining of claims adjusters’ diaries. Ideally, an adjuster should recognize and escalate the same suspicious indicators that text mining uncovers. Unfortunately, lack of training or time constraints can prevent an adjuster from picking up on those red flags. Text mining applies a consistent level of scrutiny to every diary entry, ensuring that even subtle indicators will not go undetected.

A second powerful technique that is rarely used is anomaly, or outlier, detection. The algorithms involved are computationally intensive yet extremely powerful for identifying behavior that does not fit the norm. Adopted from artificial intelligence, the algorithms learn new patterns to detect unusual characteristics even if the anomalous behavior does not include hallmark SIU red flags. This allows potentially problematic patterns to be identified earlier and put into the SIU pipeline for investigation.

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