By Kristine Masopust, published December 18, 2018

Slip and fall fraud costs U.S. businesses approximately $2 billion annually. Further, according to the Office of the Insurance Fraud Prosecutor, insurance fraud as a whole, costs U.S. taxpayers $80 billion annually. While some fraudsters get caught, the clever ones go undetected because they have real skills.

Let’s paint a picture of a seemingly typical slip and fall event. Freddy (the fraudster), a supermarket customer, approaches the beverage aisle. He slips and falls on some juice leaking out of a broken bottle. Ouch – poor Freddy.

The store manager, Mark, rushes to his aid. No ambulance needed – a slip and fall report is filled out. Mark the manager has Freddy sign a liability release form, and Mark then submits a request to the controller for payment.

However, unbeknownst to supermarket executives, Mark knows Freddy personally. Freddy is his brother in law – he’s married to Mark’s sister, Sally. And there you have it – clever fraud committed.


But not so fast – imagine being able to automatically connect the dots between people and organizations to detect such collusion while the slip and fall report is being filled out – and before a settlement check is issued.

Enter Senzing – the first real-time AI for Entity Resolution that helps organizations uncover critical information to detect and preempt such clever fraudsters in their tracks.

Let’s dive deeper…

How would the supermarket be able to connect the dots between Freddy, Sally and Mark – and ultimately wave the red flag alerting the supermarket security to the fact that something suspicious could be in the works and thus warrant a closer look?

Here’s how…

Upon accepting the store manager position, Mark provided human resources with his emergency contact’s name, address, home and cell phone numbers – in this case his sister Sally.

When Mark the store manager is collecting Freddy’s information, Freddy provides the same home phone number as Sally’s home phone (which exists in Mark’s very own emergency contact info).

Voila – dots connected!


Mark is only two degrees of separation from the slip and fall victim – via his sister. Definitely a situation worth a quick review by the supermarket’s internal fraud team.

Finding collusion between victims and those authorizing settlement is one way to use Entity Resolution. Here are some other ways:

  • Discover multiple slip and fall victims emanating from the same physical address (a household filled with like-minded fraudsters)
  • Discover a single victim making repeated slip and fall claims across store locations, altering their identity a bit each time
  • Discover common witnesses between seemingly unrelated slip and fall instances within and across store locations
  • Discover common lawyers between claimants bringing slip and fall claims cases – potentially revealing organized activity

To test this scenario out for yourself, download the Senzing software for free here then pull this sample data set here.