Identity Resolution Daily

[Post from Infoglide] Attacking Subscription Fraud with Identity Resolution

“In March 2006, the Communications Fraud Control Association (CFCA) estimated that annual global fraud losses in the telecom sector were between $54 billion and $60 billion, and the losses continue to be substantial. Many types of fraud have been identified, but by far the most prevalent is subscription fraud.”

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By Mike Shultz, Infoglide Software CEO

In March 2006, the Communications Fraud Control Association (CFCA) estimated that annual global fraud losses in the telecom sector were between $54 billion and $60 billion, and the losses continue to be substantial. Many types of fraud have been identified, but by far the most prevalent is subscription fraud.

A new subscriber signs up for mobile service using false or stolen identification, with no intention of paying the bill. Since new subscribers are given a grace period of one to three months before the account is shut off, the criminal can make thousands of dollars worth of calls before being detected.

Subscription fraud can be difficult to differentiate from simple bad debt when genuine customers are unable to pay. It’s been estimated that 30% or more of all bad debt is actually subscription fraud.

Different solutions have been tried yet fraud continues to be a problem. One common method is to look for patterns of use that suggest potential fraud, but criminals adapt and learn to probe the limits of these fraud detection systems fairly quickly.

Given the industry’s long history with fraudsters, it seems probable that enough is known about them that they could be spotted at the time they subscribe.  Using similarity searching technology, would-be fraudsters can be vetted against lists of known bad actors. Using multiple public and private data sources, non-obvious relationships can highlight risky individuals, and they can then be asked to submit to a more thorough qualification process.

Identity resolution is already used across multiple industries to solve similar problems. By matching an individual’s attributes with common attributes associated with those committing fraud, the “bad guys” are being detected in areas like lottery fraud, fusion centers, insider trading, and workers’ compensation employer fraud. Part of finding the bad guys is finding hidden relationships, connections that often uncover rings of criminals.

The “birds of a feather” axiom predicts that subscription fraud criminals often share the same types of social networks. Applying identity resolution to subscription fraud problem may be the way to finally solve it.

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