When businesses in the United States adopted EMV chips in credit and debit cards, more criminals were forced to channel their fraud efforts online. That migration, coupled with large scale data breaches, loosening credit standards, and the exploitation of legacy credit creation practices and systems, laid the groundwork for certain forms of fraud to flourish. Thus, the exponential rise of synthetic identity fraud (SIF) – the combination of real and fake data to create a brand-new identity that belongs to no one, apart from the criminal that created it. At first glance, a synthetic identity appears unremarkable, and that’s the point. With SIF, criminals are in it for the long haul (hence why it’s sometimes called “sleeper fraud”).
The longer an artificial identity remains “in play,” the larger the credit facilities secured by the identity become, and the greater the potential for criminals to profit and companies to foot the bill, as well as consumers’ (especially children’s) credit to be harmed. While no one knows exactly how much synthetic identity fraud schemes cost, because it is difficult to identify after it occurs, the Federal Trade Commission estimates the losses to American businesses at $50 billion per year.
Detecting and preventing synthetic identity fraud schemes and threats requires companies to employ a comprehensive multi-layered approach to identity verification. An effective contemporary approach is required, one that incorporates next-generation tools with synthetic fraud intelligence, utilizes big data relational analysis, and has access to data-rich consortium networks of companies united in their fight against identity theft. To win, organizations must also be able to locate and vet new applications and existing accounts without adding excessive friction to customer interactions or necessitating extra IT work to make system changes.
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