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Insurers work hard to make sure they don’t write policies for individuals with fake identities  – but bad actors work just as hard to make sure they can’t be detected. What are the latest strategies that fraudsters use to create fake identities, and how can insurers detect and mitigate these techniques?

Constructing a fake identity for insurance fraud
In general, there are two types of fake identity that insurers need to watch out for:

  • Purely fake identities
    This is an identity that’s made up from whole cloth – fake ID, fake photo, fake address, and so on. Alternatively, these identities might be based on ID information stolen or purchased from someone else.
  • Synthetic identities
    This is an identity that contains some elements of truth and some elements of falsehood or fictitious information. An ID that combines a person’s real name and a fake social security number is an example of a synthetic identity.


The common factor between fake and synthetic identities is that some of the information involved is real. A fake identity could still use information stolen from a real person, and synthetic identities use a mix of real and synthetic information. This can make these identities hard to detect.

Why is it hard for insurers to catch fake identities?
First, insurers must look across their business.

A fake or synthetic identity can be difficult to recognize during the application process because some of the information submitted is valid. If a fraudster submits a valid name and a valid social security number, for example, the insurer might not notice (or even check) that their address is wrong.

In addition, a fake or synthetic identity can surface during  any aspect of the claims process: insured, claimant, witness, etc. These may go unnoticed unless the bad actor submits a suspicious claim. Even then, some identities will resist discovery by anything except a detailed and thorough investigative process.  

Several factors make it extremely difficult to identify stolen or synthetic identities:

  • Insurers may only perform high-level validation in order to speed the onboarding process. If enough of the false information is valid on its face, the fraudster will pass these routine checks.
  • Once onboarded, the bad actor appears to be a model customer, making payments on time, renewing on schedule, and submitting only a few claims for small amounts.
  • Meanwhile, if the fake ID consists of stolen information, the victim might not be aware. This means that the fraudster’s ID will not be flagged.
  • The insurer may only use a single validation tool to make brief checks during the onboarding process. A fraudulent policy has a higher chance of passing muster if no other tool is used to provide a second opinion during pre-bind.
  • Short-staffed underwriting teams may not have enough time to fully investigate new policyholders. In companies with high high staff turnover, new employees may not know what to look for.


Lastly, insurers may not be incorporating external data. For example, there may be an external database of valid government IDs – but the insurer may not have the permission or technical resources to access it in a timely manner. By relying only on first-party information to validate identities, insurers are exposing a critical blind spot.

Building a better mousetrap to catch fake insurance identities
Many bad actors have figured out that they can get around identity checks by applying for policies at different branches of the same insurer, or by using different channels – in person, online, through the call center, and so on. If these channels don’t communicate, then the insurer may not know that the applicant was previously suspected of fraud.

Bad actors look for ways to avoid detection. They also know that  underwriters and claim handlers have competing priorities. For example, an employee that’s pressed to complete onboarding faster will have less time to investigate fake identities. 

Insurers have adopted better controls for place and  behavior analytics by way of response, utilizing third party data sources and network analytics to help identify what may not be obvious.

Shift Technology delivers a sophisticated AI solution that’s been specifically designed for the insurance industry. Its team of data scientists help insurers to address the problem of fake or synthetic identities in several ways:

  • Finding better ways deliver information from external sources up front in the analysis
  • Bridging data silos within the insurance organization
  • Making better decisions segmenting policies and claims and identifying suspicions


Shift can evaluate policies and claims to better identify patterns of fraud with a flexibility and power that goes beyond business rules, and it can use both internal and external data and social connections to validate its alerts.

Lastly, Shift provides solutions that can detect fake identities throughout the policy lifecycle – at point of quote, point of sale, FNOL, and more. These protections let insurers remove unwanted risks and premium leakage from their portfolios, which provides cost savings that they can pass on to customers. In short, no matter how hard fraudsters work, Shift Technology works harder, providing a solution that unmasks bad actors no matter what they try next.

For more information about Shift Technology and how we use artificial intelligence to mitigate insurance fraud, request a demo today.