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Feb 25, 2010, Warranty Week. SAS says send us your claims data and we'll show you the fraud. If upwards of 10% to 15% of claims are in some way suspect, such a system could pay for itself in just a few months. Does it sound too good to be true? GE Appliances was the first customer.
Warranty fraud reduction has always been a hot topic at the Warranty Chain Management Conference. At the very first WCM gathering five years ago in San Francisco, they ran out of chairs and then they ran out of standing room in the subdivision of the ballroom given over to the topic.
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As was the case last year, the warranty fraud panel in Los Angeles features a software vendor (SAS Institute, a sponsor of this newsletter) and an end user (General Electric Co.) talking about how they reduced the number of suspicious claims. But while last year the system was part of a pilot program whose success had pleasantly surprised the participants, this year it forms the core of a commercial product launch.
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Second Line of Defense
Most of the warranty claims processing systems already have a rules-based engine built into them. And that's just fine with SAS, Froning said. For instance, General Electric is first running its claims through another company's warranty claims processing system, which applies a set of rules against the claims, looking for anomalies. And then SAS runs those claims through its Suspect Claims Detection module, which looks for less obvious patterns in the data (as well as in the text).
Froning said that asking service providers to return replaced parts, or to send inspectors out into the field to look at repairs while they're in process are additional fraud prevention tools. The problem is those methods are very expensive and time consuming. The same goes for field audits.
Analytics, he suggests, can help a manufacturer to create a priority list of which repair facilities deserve the most immediate attention. Where is there a high probability of fraud? Or, if one prefers to give repair shops the benefit of a doubt, where are there "training issues" that are resulting in inadvertent mistakes and unnecessary charges? In other words, if an auditor can perform only a hundred field audits per year, the analytics can help them pick the hundred best prospects.
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