Identity fraud detection: Untangling first-party fraud and synthetics
Brandon opens a new credit card account. He begins making purchases, racking up significant debt, never pays his bill and can’t be located by the credit issuer when they attempt to collect on the account. Is this first-party fraud—a standard first-pay default driven by intentional misuse of credit, or is Brandon a synthetic identity concocted by a fraudster? Identity fraud detection is challenging.
If Brandon used his true identity and personal information to apply for the credit account but didn’t intend to repay the debt, most risk managers would probably categorize this as first-party fraud. If a fraudster created a fake identity using manufactured credentials not associated with a real person to open the account, this could be synthetic identity fraud. These scenarios can appear identical to credit issuers, and often confound businesses trying to shore up their defenses against a growing contingent of identity fraudsters.
Unlike third-party fraud (where an entire identity is stolen and used to defraud enterprises and victims), synthetic fraud frequently has no specific consumer victim, meaning there is no one to report the crime or alert the credit issuer to the fraudulent activity. Likewise, with first-party fraud – when consumers set out to steal from a business using their real identities – the consumer will not report fraudulent activity. The result is that both synthetic and first-party fraud become intermingled in credit losses, disguising their methodology and impact to the enterprise.
If financial institutions were better able to (a) differentiate between these two types of identity fraud, (b) understand how each form of fraud was defeating defenses, and (c) determine how often it was happening, could they potentially mitigate the resulting losses? Our research group embarked on a study to better understand the financial impact of synthetic identity and first-party fraud and sought to determine if there were any distinguishing characteristics associated with these types of fraud that could help businesses categorize applications.
In addition to identifying significant differences in account utilization between synthetic identity and first-party fraud, our study also determined that one type of fraud can result in significantly higher financial losses on a per-incident basis. Download our research brief Defending the gray areas: Untangling first-party fraud and synthetics to learn more about our findings.
This is the second in our series of research posts that will examine identity fraud detection in the U.S. in the coming year. This month, I am speaking with Julie Conroy, research director for Aite Group, on the volatility of identity fraud heading into 2020 and how enterprises can protect against this ever-shifting threat. Join us for a live webcast on January 21, Keeping Pace: How Identity Fraud will Change in 2020.
Kevin King is Head of Marketing at ID Analytics