Synthetic Identity Fraud

Fraudsters can use any personal information they can find online or buy on the dark web, as well as steal through breaches, phishing, and other illicit methods. For example, tax records, medical records, SSNs, children’s data, or specifically personal information stolen from seniors who might not recognize the red flags of having their data stolen are valuable for identity thieves. 

The biggest difference is that synthetic identity fraud is harder to detect because the fraudster creates a fake identity using various pieces of information that aren’t linked to a single person. In traditional identity theft, a real person’s identity is stolen, and their credit is often maxed out. The victim usually notices the fraud quickly and reports it to the authorities.

Criminals create synthetic identities to make large purchases or take out bigger loans without the intention of paying them back. In other words, they commit fraud for their financial benefit. This happens because they build up credit and make their identity look legitimate, which makes them appear as a low-risk borrower. Since this synthetic credit file looks like those of many real people with limited or no credit history, it’s very difficult to detect the fraudster.

However, some types of synthetic identity fraud aren’t based entirely on the desire to steal money. For example, there are cases in the US when immigrants use stolen or fake SSNs to access financial services, and not steal money, just access cards to receive funds and handle financial transactions. 

Bust-out fraud is a fraudulent credit card scheme that occurs when a criminal applies for a credit card, builds a good history, and then maxes out the card without ever repaying the charges. This type of fraud is linked to synthetic identities because they are used as a tool here by fraudsters who spend months or even years building up a strong credit record using synthetic identities. Fraud rings typically create thousands of synthetic identities, all set up to eventually “bust out.”

Users should be aware of some warning signs that can be indicators of synthetic identity fraud and that their information has been stolen. Common signals include unfamiliar charges on your account, especially small amounts you don’t recognize, and emails from unusual sources that you didn’t request or subscribe to.

Businesses use AI and ML to analyze such anomalies automatically and detect inconsistencies in their customers’ behavior. Other standard methods include identity verification during the user onboarding process, as well as screening and ongoing monitoring later on since risk profiles change frequently. For users, preventing synthetic identity theft means monitoring their data, such as checking credit card reports, IRS documents, and general personal information, to spot any unusual activity.