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Strategies to Tackle First-Party Fraud and Mule Activity

Rob Rendell, Global Head of Fraud Market Strategy & Fraud Prevention - Subject Matter Expert at Nice Actimize, offers a detailed overview of First-Party Fraud and Mule Activity, two unnervingly popular scam methodologies.

First-party fraud (FPF) is when the fraudster doubles as a customer. Fraudsters will open accounts for the specific purpose of executing their fraud schemes. One common example these days are money mules who open accounts for fraudsters, enabling them to transfer money through multiple banks and countries to avoid scrutiny.


Money mules, who open accounts for criminals to transfer funds, are also considered a type of FPF.

Multi-Faceted Fraud

Common FPF forms mentioned by Mr. Rendall include:

  • Checking account abuse on a current or deposit account
  • "Bust out," where a fraudster obtains facilities, gets a credit card or completes a loan, then disappears without paying
  • Check kiting, a familiar scheme where fraudsters cross-deposit checks of increasing values at different financial institutions
  • Making false claims of fraud
  • Engaging Money Mules to move funds

Fraudulent misrepresentation is when a genuine person is applying for an account, but...

... they lie about personal details, such as address history, or income and expenditure, to hide bad debts or get accounts or facilities they would not otherwise be granted. It’s possible that they intend to use the account or facility genuinely, but the bad rate on these transactions will be higher. In any of these categories, the fraudster might be planning to undertake, bust out or commit mule activity or abuse.

Fighting FPF and Mule Activity

The article recommends the following examples of data to include in a model:

  • Early defaults, such as three payments down in the first six months
  • High excesses, such as 50% over limit
  • Loan churn prior to default
  • Bust out indicators
  • Internal and external data matches that show a high risk of fraud

And, furthermore, while reviewing cases to confirm fraud, it's important to look for:

  • Fake documents
  • Clear misrepresentation, including hidden address with adverse credit data
  • Inability to contact the account holder
  • Claim of ID theft by a genuine party

Much of what Mr. Rendall discusses are part of predictive analytics/behavioral analytics systems that we covered earlier this week. Monitoring accounts is crucial in the fight against not only check fraud, but bank fraud in general. However, checks are the vehicle that is most exposed to vulnerabilities.

As we know, there is no silver bullet when it comes to fraud, whether it's checks specifically or bank fraud in general. It takes multiple solutions to create a strong defense against fraud -- in the case of checks, combining predictive intelligence/behavioral analysts, image forensic AI, dark web monitoring, positive pay, and consortium data. Financial institutions need to continue to invest in all of these systems to ensure that their funds and customers are protected.

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