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Using data analytics to find fraud under those shells



Fraudsters increasingly are using shell companies to commit everything from asset misappropriation and money laundering to bribery and corruption schemes. Audit committees want fraud examiners to make sure their organizations aren’t victims. Here’s how to use fraud data analytics to sniff out illegal shells.

Google the phrase “shell company fraud scheme” and you’ll discover more news stories than you have time to read. In Southeast Asia, an employee stole $11 million using a false-billing shell-company scheme. In the U.S., a company lost $65 million through a similar scheme. Fraudsters are using shell companies to steal millions of dollars from organizations every year.

Shell companies are business entities that typically have no physical presence (other than a mailing address), no employees and generate little, if any, independent value. They’re not necessarily illegal, but employees can use them to commit fraud.

They might be nothing more than a fabricated name and address that an employee uses to collect disbursements from false billings. However, because the perpetrator receives payments made out in the shell-company name, the perp normally will also set up a bank account in their new company’s name so they can deposit and cash the fraudulent checks. (See the online ACFE Fraud Examiners Manual, Section 1: Financial Transactions and Fraud Schemes/Asset Misappropriation: Fraudulent Disbursements/Billing Schemes.)

In the last few years, organizations have become increasingly concerned about this growing threat. Perpetrators have used shell companies to commit a variety of fraud schemes — everything from asset misappropriation and money laundering to bribery and corruption schemes. Now audit committees are asking the tough question: Could this happen in our company?

In this article, I’ll walk you through the process of using fraud data analytics to discover shell companies. The process begins with: 1) writing a fraud-risk statement, 2) determining the fraud data analytics methodology and 3) searching master file and transactional data. I’ll also share with you some real-life stories of how we’ve detected these schemes.



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