Synthetic identity fraud is on the rise in 2018. Sophisticated crime rings have access to never-before-seen numbers of compromised records, and they know that most fraud prevention software is fooled by this type of fraud.
Synthetic identity fraud differs from tradition identity theft in that the perpetrator creates a new synthetic identity rather than stealing an existing one. The process starts with someone stealing real social security numbers that aren’t actively being used — think children and elderly people who use little, if any, credit — and then creating identities by adding fake addresses.
Playing a long con that can take years to pay off, these thieves slowly build a credit rating for these new identities, interacting with banks using burner phones. They eventually rack up debts of $20,000 or more on countless accounts only to disappear without a trace.
Synthetic identity fraud is costing banks billions of dollars and countless hours as they chase down people who don’t even exist. That is part of the reason why global card losses have been rising at an average annual rate of 18% in recent years, according to Accenture estimates.
Synthetic identity theft alone may account for 5% of uncollected debt and up to 20% of credit losses, or $6 billion in 2016, according to some industry analysts. The problem is even more acute with store credit cards and auto loans.
Central to solving the issue will be banks getting to know their customers better. Some banks are already demanding that customers show up at a physical branch to open a bank account or to apply for credit, trading high losses from synthetic fraud for a poorer customer experience. However, while it would be nice if we could return to the days when everyone had a relationship with their bank managers, that may be impractical in these digital times, especially for the largest banks.
A key part of the solution will be using artificial intelligence engines and machine learning methods to comb through the growing repository of digital data about each of us to better verify identity. For example, if a customer purporting to be from New York, is applying for credit, can the bank ascertain, using social media and community data, that there is an actual person of that name in that town? Have they been posting from that location on Facebook? Did they appear in the local school yearbook in the correct year? AI is perhaps the technology best suited for this challenge because the amount of data that banks will have to search is an enormous pool that is constantly growing.
Another part of the solution will be a central method of verifying identity that works as seamlessly as the major credit bureaus do today. This might be easier said than done though. Regulators will rightly have concerns about the prospect of a bank turning down a credit application because someone doesn’t post on Facebook. A central repository also could raise privacy concerns.