Synthetic identity fraud
Also called synthetic ID fraud, frankenstein identity fraud
Synthetic identity fraud is the creation of a fictitious person by combining real data, such as a stolen national insurance or social security number, with invented details like a name and date of birth. Fraudsters use the identity to open accounts, build a credit history and then borrow heavily before disappearing.
How it works
A synthetic identity often passes standard checks because part of it is real and there is no existing victim to report a problem. The fraudster applies for credit, gets declined, and the application itself creates a credit file. Over months the identity gains small credit lines, pays them on time and grows its limits. The final stage is a bust-out, where every line is drawn down at once.
For banks the losses often look like ordinary credit write-offs, so the fraud is under-counted. Detection relies on checking that the data elements belong together. Firms compare names and dates of birth against issuing authority records, look for thin files with fast credit growth and link shared phones, devices or addresses across applications. AI-generated documents and faces make document checks alone less reliable.
The rules
Required the Social Security Administration to let financial institutions check, with consent, whether a name, date of birth and social security number match.
Social Security Administration and permitted financial institutionsRequires banks to collect identifying information and verify customer identity using risk-based documentary or non-documentary methods.
Banks, savings associations and credit unionsCommon questions
How is synthetic identity fraud different from identity theft?
Identity theft uses a real person's full identity, and that person usually notices and complains. A synthetic identity mixes real and fake data into a new person, so there is often no single victim to raise the alarm.
Why is synthetic identity fraud hard to detect?
The identity behaves like a good customer for months. It pays bills on time and builds a credit record before the bust-out. Losses are then often recorded as bad debt rather than fraud.
Who is most targeted?
Section 215 of the 2018 US law names minors and recent immigrants as groups disproportionately affected. Their identity numbers rarely have an active credit history, and the real holder is unlikely to check soon.
Does generative AI make the problem worse?
Yes. AI tools can produce realistic documents, selfies and voice samples in minutes. That lets fraudsters create and operate many synthetic identities at scale and defeat basic document and selfie checks.
Our coverage
News and analysis on KYC and identity from RegTech Post.
Read the coverage →RegTech PostBiometric KYC Comes of Age: The Next Frontier in Digital IdentityRead the article →RegTech PostHow AI and Deepfakes Are Reshaping KYC in Financial ServicesRead the article →
