This chapter will explain a methodology for using fraud data analytics in the search for fraud scenarios in financial statement accounts. Chapters 14 and 15 will explain how to use the methodology for revenue and journal entry testing.
This chapter will focus on uncovering fraud scenarios that are recorded in the general ledger or omitted from the general ledger either in a source journal or through a manual journal entry. This chapter will not focus on standards of care required by the professional auditing standards to uncover material misstatement through an intentional error. It is assumed the reader has knowledge of GAAS and will incorporate the standards into the data analytics plan.
I would encourage the reader to study the known financial statement frauds that occurred to improve their personal knowledge of how management has misstated financial statements. The knowledge will assist the auditor in the data interpretation aspect of the methodology. It is always easier to see a fraud scenario the second time versus the first time. Through the study of previous financial statement fraud cases, the auditor will learn the concept of fraud predictability or the logic of building the fraud analytics plan.
In one publicly traded company, revenue was materially overstated through a series of small‐dollar general journal entries that in the aggregate caused a material misstatement. Therefore, financial statement history has ...