When investigating financial or insurance fraud, sometimes there are things about a person’s identity record that don’t quite add up. In these cases, you might be facing a Synthetic Identity created with a mixture of stolen and fake PII. As this problem grows, organizations are looking for ways to save their organizations the time and thousands of dollars spent (per instance) reconciling the issues caused by it.
As a first step, investigators need to verify if all the PII being used belongs to the same person – and if so, whether or not it’s an actual person. Most fraud filters can determine if data attributes such as DOB, SSN, email, physical locations, phone, etc. are real. With synthetic identities the bigger questions center on: 1) Does the data belong together 2) Did the identity attributes surface publicly in a normal asymmetric pattern and 3) Is there enough data from key sources to use for corroboration.
In reality, synthetic identities are a ‘fraud product.’ This means they’ve literally been ‘assembled’ in tightly defined periods of time and propagated across less digital space than a real identity record would be. They lack many of the patterns observed in a real person’s evolving life journey. When scrutinized against a deep-rooted identity index, properly timestamped linkage between online and public records cannot be faked. For example, fraudsters would be hard pressed to build an identity record which confirms an email address is linked to a 10-year old VIN number, or to a deep, asymmetric history of Street Addresses.
For more detail, we’ve developed a list of best practices which focuses on how to effectively investigate and segment real and fake identities.
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