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It’s no secret that financial institutions are battling against a rising tide of credit write offs. Investigating the root cause can leave us without satisfying answers however, since current economic trends and user behavior patterns don’t line up to account for the steep rise. New research from Gartner may have found a cause. Their latest research suggests that “by 2021, first-party fraud and synthetic identity fraud will account for 40% of credit write-offs, up from an estimated 25% today.”

If we look at the evolving fraud landscape for answers, the picture starts to become clearer. Synthetic identity fraud and first party fraud are evolving at a rate that most current identity proofing tools and some of the older bust-out models are unable to detect. In fact, most of these models were never designed to detect fraud, they were designed to establish creditworthiness, approve new lines of credit, and verify identity. So when fraudsters get smarter with their synthetic identity practices, they are able to bypass these systems. The result of this is an increase in credit write offs, and a pollution of miscategorized fraud, that is never appropriately solved at the source. Once this “hidden” fraud is removed from the chargeback and credit write off categories, we see a true picture of the scope of the fraudscape, thus allowing for its appropriate solutions. Financial institutions can only begin to recover these losses by combatting the correct source of fraud—by first identifying it.

In fact, iovation’s own data amongst its customers shows that synthetic identity is an ever growing problem. 2018 customer polling shows that it is the third most common type of fraud they face.



To see exactly how much synthetic identity fraud might be contributing to your inflated chargeback losses, we first must define the terms we are dealing with that contribute to the problem. Synthetic identity at its core means either an entirely fabricated identity, or an altered version of a real identity, by combining otherwise genuine identity elements from multiple separate identities. This is different than stolen identity fraud, since either some or all of the elements of the identity are in fact not real, or synthetic. These synthetic identities are evolved enough that they pass most identity proofing models, and count as “real” accounts.

Most institutions only measure and provide checks against direct first party or third party fraud losses. These losses vary by institution, but encompass anything that results in a loss where the fraudster is directly using a real identity information, whether their own or stolen, with malicious intent. First or third party fraud losses can include collusion, bad debt, policy abuse, stolen identity to open new lines of credit, bust-out schemes, and frivolous chargebacks to try and gain over the system. Too often, all these types of fraud are lumped together into one category, and treated as the entirety of “first party” or “third party” fraud.

When all these types of fraud are tracked together in legacy systems, it becomes easy for synthetic identity to mask as either first or third party fraud. In fact, it’s possible for synthetic identity fraud to be masked entirely, as legitimate chargebacks or credit losses. When there is no categorization of the different types of fraud within current models, you can’t quantify which types are increasing, or where the source originates. Most systems identify chargebacks as a typical credit failure. And if an institution isn’t measuring chargebacks as its own category of fraud, it’s difficult to see the dramatic rise and originating causes of the chargebacks. As Gartner’s latest report states, “If you can't accurately name it, you can't measure it, determine what an acceptable level is, and watch how these types of losses grow and change, and you can't justify investments in technology solutions and internal resources to fight it.” The key is to appropriately differentiate the types of first and third party losses, their originating causes, and methods of fraud prevention. To prepare and get a clear scope of your own synthetic identity fraud, view our on demand presentation which gives you strategies for identifying synthetic identity and battling credit write-offs.

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