Defining 'Normal': A More Modern Approach to Preventing ID Fraud

By Jost, Allen | American Banker, September 10, 2004 | Go to article overview

Defining 'Normal': A More Modern Approach to Preventing ID Fraud


Jost, Allen, American Banker


Identity fraud prevention efforts have become more sophisticated in recent years, and so have the fraudsters.

Much of the fraud doesn't even have a consumer victim.

In what is known as "synthetic" or "fabricated" fraud, for example, perpetrators create an identity using an amalgam of real and false elements.

Fraudsters mask their behavior to appear authentic and blend in with legitimate consumers. They may even make minimum payments and stay on the books for months or even more than a year to firmly establish an identity, increase a credit limit, and gain good standing. They then establish other accounts to further hide their tracks when they actually steal money.

Creditors can no longer rely solely on traditional methods to detect fraud. They must add a layer of technology and process to predict its likelihood. Instead of looking for a needle in a haystack they need to look for something that doesn't resemble a piece of hay.

The most effective way to detect fraud is to recognize normal behavior patterns. Not all anomalies are frauds, however, so sophisticated analytics must be used. By narrowing the list of possible offenders, creditors can do more to verify identities that are truly suspicious. That means more time and resources available to serve legitimate customers.

Why do current methods fall short?

Credit scoring took risk management to a new level in the 1960s. In addition to making the process more objective, scoring automated account initiation and monitoring. In the 1990s scoring transformed credit card fraud investigation from an inefficient process to an effective prevention and detection technique. But fraudsters have gotten wise.

Most detection methods are based on techniques that model account behavior patterns and use personal data, known frauds, and multiple data sources. …

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