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North American companies have developed sophisticated processes and technology to detect claim fraud. There are a variety of analytical techniques to use with claim data, such as automated red flags, predictive modeling, rules-based analysis, data mining, and others. At the most basic level, companies use decision rules to identify fraud at the claim level. Some companies process data at the provider level. They focus on the intermediaries who sit between the company and the customer. This is useful in cutting down fraud; however the weakness is that it assesses service provider behavior in isolation. A key gap in the arsenal is the capability to monitor the service provider network as a composite and assess pair-wise or group-wise culpability in fraud. This is the type of fraud we term “collusion.” Let’s discuss tactics to beat it.

Collusion is a tacit agreement amongst two or more entities in the value chain between the insurer and the customer. The purpose of the agreement is to misrepresent or to inflate loss events and thus to defraud the insurer. A typical example of collusion fraud would be a third party adjuster approving fraudulent claims on soft tissue injuries as submitted by a complicit clinic. As with other type of fraud, collusion hurts the industry in two ways: First, there is the direct charge to the insurer for claims that are not legitimate. Second, the inaccurate or non-existent claims corrupt the data used by underwriters.

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