Identity theft and identity fraud are well-known forms of cybercrime in which criminals use consumers’ stolen identities to make fraudulent transactions.
Corporations fall victim to this type of fraud as well, with bad actors leveraging stolen identities to open new accounts, but there is an even more challenging variety: synthetic identity fraud, according to “Monetizing Digital Intent,” a PYMNTS and Neuro-ID collaboration.
Get the report: Monetizing Digital Intent
Synthetic identity fraud involves hackers creating new identities rather than impersonating others, although they often cobble together pieces of information from real people to look more realistic.
The types of synthetic identity fraud that bad actors create can vary greatly. The most common types involve fraudsters leveraging artificial intelligence (AI) programs en masse to impersonate business owners, open credit cards and make fictitious invoices.
Who’s Who?
Synthetic identity fraud can be particularly hard to catch, as it leaves no victim to report a stolen identity. Worse still, traditional authentication methods, such as passwords and multifactor authentication (MFA), are all but useless in preventing it. This is because the bad actor is not using stolen credentials to usurp a known identity but instead is making a new account with its own authentication.
A better choice for fraud detection of this type is behavioral analytics, which scrutinizes the ways in which applicants enter personal details during the onboarding process.
These systems utilize a variety of metrics, such as interaction time, hesitation and automated entry, to determine if a given applicant is a legitimate user or a fraudster disguised with a synthetic identity. The bad actor likely will be creating multiple fake accounts at once, for example, and will be auto-filling entries that a legitimate user typically would type by hand.
A fraudster attempting to circumvent these analytics by entering this data manually still runs the risk of making typos or misspellings that an actual user would avoid, providing the analytics system with another metric to evaluate the risk of fraud.
Lowering False Positives
Behavioral analytics also can reduce the rate of false positives, or legitimate customers mistakenly being identified as fraudsters — a mistake that can deprive organizations of revenue not only from the customers themselves but also from any potential referrals they might make to other prospects.
Both synthetic identity fraud prevention and false positive avoidance are crucial when conducting business online, and any business will need a means to provide both. Behavioral analytics could be a key resource in meeting this objective.
Behavioral analytics tools help companies’ finance and risk management teams get a crowd-level view of bot attacks and behaviors that look risky, allowing them to alert the crowds of legitimate users to be vigilant, while letting more “good” commerce in through the front door, Neuro-ID CEO Jack Alton told PYMNTS in a February interview.
Read more: Preventing Online Fraud Should Start by Examining Customer Intent
“When you unlock that pre-submit data — how that person actually interacted with you — it can point you toward 10%, 20%, 30%, 40% of your customers that are genuine, that you don’t have to subject to friction,” Alton said.
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