Jun 08, 2022
< 1 min read

Game: Spot 6 Fraudsters Among Honest Users

Fraudsters easily blend in among honest users. Here’s a game to see if you can detect them.

With fraudsters using the identities of real people to register with platforms, it’s becoming harder for businesses to protect themselves. Put yourself in the shoes of a compliance officer defending businesses from financial crimes. See if you can spot the fraudsters in the crowd:

  • A bank card fraudster—one who performs transactions using stolen card information.
  • A money mule—one with a clean banking history recruited by criminals to launder illicit funds.
  • A romance scammer—one who uses dating sites to lure their victims through the false promise of romance.
  • A phishing scammer—one attempting to steal sensitive information or to install malicious software on the victim’s device via email.
  • An identity thief—one who uses stolen identities to register with services and commit fraud.
  • A face spoofer—one who uses masks, deepfakes and other means to disguise themselves in order to pass facial biometrics authentication.

See if you can catch all of them!

spot 6 fraudsters
Did you find everyone? If not, here’s who you were looking for.

Now you’ve found all the fraudsters. But did you notice someone else hiding among the users?
spot 6 fraudsters

The work of an AML compliance officer or verification solution is a bit like this game. In other words, it isn’t enough to detect only those fraudulent patterns that you expect to see. You also have to spot new patterns that you didn’t even know existed.

Find out more on fraud prevention

  • Guide: Masks, Animated Pictures, Deepfakes…—Learn How Fraudsters Can Bypass Your Facial Biometrics.
  • Case study: How global crypto company Bybit fights fraud and quickly onboards users with Sumsub.
  • Know Your Match Game: Play it to see if you can filter out unwanted customers.

Preventing fraud is hard, but it’s easier with Sumsub. Request a demo today.

AMLAutomationBiometricsCybersecurityFacial RecognitionFraud PreventionGameIdentity TheftMachine LearningRisk Management