North American companies have developed sophisticated processesand technology to detect claim fraud. There are a variety ofanalytical techniques to use with claim data, such as automated redflags, predictive modeling, rules-based analysis, data mining, andothers. At the most basic level, companies use decision rules toidentify fraud at the claim level. Some companies process data atthe provider level. They focus on the intermediaries who sitbetween the company and the customer. This is useful in cuttingdown fraud; however the weakness is that it assesses serviceprovider behavior in isolation. A key gap in the arsenal is thecapability to monitor the service provider network as a compositeand assess pair-wise or group-wise culpability in fraud. This isthe type of fraud we term "collusion." Let's discuss tactics tobeat it.

Collusion is a tacit agreement amongst two or more entities inthe value chain between the insurer and the customer. The purposeof the agreement is to misrepresent or to inflate loss events andthus to defraud the insurer. A typical example of collusion fraudwould be a third party adjuster approving fraudulent claims on softtissue injuries as submitted by a complicit clinic. As with othertype of fraud, collusion hurts the industry in two ways: First,there is the direct charge to the insurer for claims that are notlegitimate. Second, the inaccurate or non-existent claims corruptthe data used by underwriters.

How Rampant Is Collusion Fraud?