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Fraud Data Analytics Methodology by Leonard W. Vona

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Chapter 14Fraud Data Analytics for Revenue and Accounts Receivable Misstatement

SAS no. 99 states that you “should ordinarily” presume there is risk of material misstatement due to fraud relating to revenue recognition. If you do not identify improper revenue recognition as a risk of material misstatement due to fraud, you should document the reasons supporting this conclusion. The assumption in this chapter is that you are searching for material misstatement of revenue in the source journal. I should clarify that the phrase sale of goods will be used throughout the chapter to indicate revenue earned from the sale of tangible goods, services, or rental income.

Chapter 13 described the methodology for building a fraud data analytics plan for financial accounts. Using the plan, this chapter will focus on the fraud data analytics search routines for fraud scenarios in the source journal.

Revenue is the candy jar of choice used by management to misstate the financial statements. The candy jar has many different types of candy to cause revenue to be over‐ or understated. Fraudulent revenue may be recorded through a source journal or through a journal entry. Management may decide to misapply GAAP, thereby causing the recorded transaction to misstate the financial statements.

For the auditor, revenue recognition requires an understanding of the industry and the organization. The auditor needs to have the ability to apply and interpret the principles of revenue recognition to the audit ...

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